<reponame>GiuliaLanzillotta/omni-learn<gh_stars>0
import os
import numpy as np
import cv2
from io import BytesIO
from PIL import Image
import torch
from torch.utils.data import DataLoader, Dataset
# from torch.utils.data.dataloader import re, numpy_type_map, _use_shared_memory, string_classes, int_classes#,
import re
try:
	from torch.utils.data.dataloader import container_abcs
except:
	pass

from .misc import TreeSpace
import random
import collections
import itertools
import h5py as hf

import omnifig as fig

import torch
from torch import nn
from torch.nn import functional as F


class make_infinite(DataLoader):
	def __init__(self, loader, extractor=None):
		assert len(loader) >= 1, 'has no len'
		self.loader = loader
		self.itr = None
		self.extractor = extractor
		
		self.cached = None
	
	def __len__(self):
		return len(self.loader)
	
	def end(self):
		self.itr = None
		self.empty()
	
	def empty(self):
		self.cached = None
	
	def demand(self, N, extract=None, merge=None):
		'''
		
		:param N:
		:param extract: callable input: raw batch; output: mergable data with len()
		:param merge: callable input: list of extracted batches; output: whatever is expected
		:return:
		'''
		
		if extract is None:
			extract = self.extractor
		
		missing = N
		batches = []
		
		while missing > 0:
			if self.cached is None:
				x = next(self)
				# by default, assume batch is a tuple and pull first element
				x = x[0] if extract is None else extract(x)
			else:
				x = self.cached
				self.cached = None
			
			if len(x) > missing:
				self.cached = x[missing:]
				x = x[:missing]
			missing -= len(x)
			batches.append(x)
		
		return (torch.cat(batches),) if merge is None else merge(batches)
	
	def __next__(self):
		if self.itr is not None:
			try:
				return next(self.itr)
			except StopIteration:
				pass
		return next(iter(self))
	
	def __iter__(self):
		self.itr = iter(self.loader)
		return self
	


def get_h5_data(path, key, idx=None):
	with hf.File(path, 'r') as f:
		return f[key].value if idx is None else f[key][idx]


def make_np_wrapped_env(env_type):
	def create(*args, **kwargs):
		return Numpy_Env_Wrapper(env_type(*args, **kwargs))
	return create
class Numpy_Env_Wrapper(object):
	def __init__(self, env, device='cpu'):
		self._env = env
		self.device = device
		
	def to(self, d):
		self.device = d
	def cpu(self):
		self.device = 'cpu'
	def cuda(self, i=0):
		self.device = 'cuda:{}'.format(i)

	def __getattr__(self, item):
		if item not in self.__dict__:
			return self._env.__getattribute__(item)
		return super(Numpy_Env_Wrapper, self).__getattribute__(item)

	def reset(self, state=None):
		if state is None:
			return torch.from_numpy(self._env.reset()).float().to(self.device)
		return torch.from_numpy(self._env.reset(state.detach().cpu().numpy())).float().to(self.device)

	def step(self, action):
		ns, r, d, info = self._env.step(action.detach().cpu().numpy())
		ns = torch.from_numpy(ns).float().to(self.device)
		r = torch.tensor(r).float().to(self.device)
		return ns, r, d, info

	def render(self, *args, **kwargs):
		return self._env.render(*args, **kwargs) # no wrapping here



def batches(iterable, n): # batches an iterable (batches are not lazy)
	iterable = iter(iterable)
	while True:
		tup = tuple(itertools.islice(iterable, 0, n))
		if tup:
			yield tup
		else:
			break


def fill_in(src, new, locs, add=True, inplace=False):
	'''fill in `new` in `src` at pixel coords `locs` (B,2) (batched)'''

	squeeze = False
	if len(src.shape) == 3:
		B, H, W = src.shape
		C = 1
		squeeze = True
		src.unsqueeze(1)
		if len(new.shape) == 3:
			new = new.unsqueeze(1)
	else:
		assert len(src.shape) == 4

		B, C, H, W = src.shape

		if C != 1:
			raise NotImplementedError

	U, V = new.shape[-2:]

	u, v = locs.t()

	vals = new.view(-1)
	src = src.view(-1)

	shift = torch.arange(0, U * V).fmod(V).eq(0)
	shift[0] = 0
	shift = shift.int().mul(W - V).cumsum(0)

	pos = torch.arange(U * V * C).unsqueeze(0)

	idx = u * W + v

	inds = (shift + pos + (H * W * C * torch.arange(B) + idx).unsqueeze(1)).view(-1)

	if inplace:

		if add:
			src.index_add_(0, inds, vals)
		else:
			src.index_copy_(0, inds, vals)
		out = src

	else:

		if add:
			out = src.index_add(0, inds, vals)
		else:
			out = src.index_copy(0, inds, vals)

		out = out.view(B, C, H, W)

	if squeeze:
		out.squeeze(1)
	return out


def to_one_hot(idx, max_idx=None):
	if max_idx is None:
		max_idx = idx.max()
	dims = (max_idx,)
	if idx.ndimension() >= 1:
		if idx.size(-1) != 1:
			idx = idx.unsqueeze(-1)
		dims = idx.size()[:-1] + dims
	return torch.zeros(*dims).to(idx.device).scatter_(-1, idx.long(), 1)

# linear - discretize continuous values into N classes
# TODO: implement log space
def discretize(input, N, range=None):

	if range is None:
		range = input.min_val(), input.max()

	input = input.clamp(*range)
	input -= range[0]

	return input.mul((N-1)/(range[1]-range[0])).round().long()

## load image

def load_images(*paths, root=None, channel_first=False, conv_brg=True, **flags):
	assert not len(flags)
	for path in paths:
		if root is not None:
			path = os.path.join(root, path)
		img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
		if conv_brg and len(img.shape) == 3 and img.shape[-1] in {3,4}:
			img = img[..., ::-1].copy()
		if channel_first:
			img = img.transpose(2,0,1)
		yield img

def read_raw_bytes(path, root=None):
	if root is not None:
		path = os.path.join(root, path)
	with open(path, 'rb') as f:
		return f.read()


#########################
# Util to convert images to strings for compressed saving

def rgb_to_str(img, compression=5):
	assert img.shape[2] == 3
	assert img.dtype == np.uint8

	return cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compression])[1].tostring()


def float32_img_to_str(img, compression=5):
	H, W = img.shape
	assert img.dtype == np.float32

	img.dtype = np.uint8
	img.shape = (H, W, 4)

	s = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compression])[1].tostring()

	img.shape = (H, 4 * W)
	img.dtype = np.float32

	return s


def byte_img_to_str(img, compression=5):
	assert img.dtype == np.uint8

	return cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compression])[1].tostring()


def str_to_rgb(s):
	return cv2.imdecode(np.frombuffer(s, np.uint8), -1)


def str_to_float32_img(s):
	img = cv2.imdecode(np.frombuffer(s, np.uint8), -1)

	H, W = img.shape[:2]

	img = img.reshape(H, W * 4)
	img.dtype = np.float32

	return img


def str_to_byte_img(s):
	return cv2.imdecode(np.frombuffer(s, np.uint8), -1)


def jpeg_to_str(path):
	with open(path, 'rb') as f:
		return f.read()

def str_to_jpeg(s, ret_PIL=False):
	img = Image.open(BytesIO(s))
	if ret_PIL:
		return img
	return np.array(img)


# Wrapper to create a DataLoader with NS_collate
# def loader(dataset, def_type=None, multi_agent=False, **kwargs): # switches default_collate to NS_collate
# 	if 'collate_fn' not in kwargs:
# 		kwargs['collate_fn'] = make_collate(def_type, multi_agent)
# 	if 'batch_size' in kwargs and kwargs['batch_size'] is None:
# 		kwargs['batch_size'] = len(dataset)
# 	return DataLoader(dataset, **kwargs)


### old

# multi traj gen: [N] x {NS} x [A]  --> [A] x {NS} x [N]
#
#
# def make_collate(stack=True):
# 	merge_fn = torch.stack if stack else torch.cat
#
# 	def collate(batch):
# 		r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 		error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
# 		elem_type = type(batch[0])
# 		if isinstance(batch[0], torch.Tensor):
# 			out = None
# 			if True:
# 				# If we're in a background process, concatenate directly into a
# 				# shared memory tensor to avoid an extra copy
# 				numel = sum([x.numel() for x in batch])
# 				storage = batch[0].storage()._new_shared(numel)
# 				out = batch[0].new(storage)
# 			try:
# 				return merge_fn(batch, 0, out=out)
# 			except RuntimeError:
# 				return batch
# 		elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
# 				and elem_type.__name__ != 'string_':
# 			elem = batch[0]
# 			if elem_type.__name__ == 'ndarray':
# 				# array of string classes and object
# 				if re.search('[SaUO]', elem.dtype.str) is not None:
# 					raise TypeError(error_msg.format(elem.dtype))
#
# 				return torch.stack([torch.from_numpy(b) for b in batch], 0)
# 			if elem.shape == ():  # scalars
# 				# py_type = float if elem.dtype.name.startswith('float') else int
# 				# return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
# 				return torch.tensor(batch)
# 		elif isinstance(batch[0], int):
# 			return torch.LongTensor(batch)
# 		elif isinstance(batch[0], float):
# 			return torch.DoubleTensor(batch)
# 		elif isinstance(batch[0], str):
# 			return batch
# 		elif isinstance(batch[0], container_abcs.Mapping):
# 			return {key: collate([d[key] for d in batch]) for key in batch[0]}
# 		elif isinstance(batch[0], container_abcs.Sequence):
# 			transposed = zip(*batch)
# 			return [collate(samples) for samples in transposed]
#
# 		raise TypeError((error_msg.format(type(batch[0]))))
#
# 	return collate
#
#
#
# def NS_make_collate(def_type=None, multi_agent=False):
#
# 	collate_fn = None
#
# 	if def_type is None:
# 		collate_fn = NS_list_collate
# 		return collate_fn
#
# 	def NS_list_type_collate(batch):
# 		r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 		full = TreeSpace()
#
# 		for key in batch[0].keys():
# 			full[key] = []
# 			for sample in batch:
# 				data = NS_collate(sample[key]) if key == 'env_infos' else NS_collate([sample[key]])[0] # TODO: clean up
# 				if isinstance(data, torch.Tensor):
# 					data = data.type(def_type)
# 				full[key].append(data)
#
# 		return full  # returns NS containing collated paths
#
# 	collate_fn = NS_list_type_collate
#
# 	if multi_agent:
# 		if def_type is None:
# 			return NS_multi_list_collate
#
# 		def NS_multi_list_type_collate(batch):  # no default tensor type
# 			r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 			num_agents = len(next(iter(batch[0].values())))
#
# 			full = [TreeSpace() for _ in range(num_agents)]  # a separate batch of paths for each agent
#
# 			for i, f in enumerate(full):
# 				for key in batch[0].keys():
# 					for sample in batch:
# 						if isinstance(sample[key], list):
# 							if isinstance(sample[key][0], dict):
# 								data = NS_collate(sample[key])
# 							else:
# 								data = NS_collate([sample[key][i]])[0]
# 								if isinstance(data, torch.Tensor):
# 									data = data.type(def_type)
# 						else:
# 							data = NS_collate([sample[key]])[0]
# 							if isinstance(data, torch.Tensor):
# 								data = data.type(def_type)
# 						if key not in f:
# 							f[key] = []
# 						f[key].append(data)
#
# 			#print(full[0].rewards)
#
# 			return full  # returns NS containing collated paths
#
# 		return NS_multi_list_type_collate
#
# 	return collate_fn
#
#
# def NS_multi_list_collate(batch):  # no default tensor type
# 	r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 	num_agents = len(next(batch[0].values()))
#
# 	full = [TreeSpace() for _ in range(num_agents)] # a separate batch of paths for each agent
#
# 	for i, f in enumerate(full):
# 		for key in batch[0].keys():
# 			f[key] = [NS_collate([sample[key][i]])[0] for sample in batch]
#
# 	return full  # returns NS containing collated paths
#
# def NS_list_collate(batch): # no default tensor type
# 	r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 	full = TreeSpace()
#
# 	if isinstance(batch, dict):
# 		full = TreeSpace(**{k:NS_list_collate(batch[k]) for k in batch.keys()})
# 		return full
#
# 	for key in batch[0].keys():
# 		full[key] = [NS_collate([sample[key]])[0] for sample in batch]
#
# 	return full # returns NS containing collated paths
#
# def NS_collate(batch):
# 	r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 	error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
#
# 	if isinstance(batch, dict):
# 		return {k:NS_collate(batch[k]) for k in batch.keys()}
#
# 	elem_type = type(batch[0])
# 	if isinstance(batch[0], torch.Tensor):
# 		out = None
# 		if _use_shared_memory:
# 			# If we're in a background process, concatenate directly into a
# 			# shared memory tensor to avoid an extra copy
# 			numel = sum([x.numel() for x in batch])
# 			storage = batch[0].storage()._new_shared(numel)
# 			out = batch[0].new(storage)
# 		return torch.stack(batch, 0, out=out)
# 	elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
# 			and elem_type.__name__ != 'string_':
# 		elem = batch[0]
# 		if elem_type.__name__ == 'ndarray':
# 			# array of string classes and object
# 			if re.search('[SaUO]', elem.dtype.str) is not None:
# 				raise TypeError(error_msg.format(elem.dtype))
#
# 			return torch.stack([torch.from_numpy(b) for b in batch], 0)
# 		if elem.shape == ():  # scalars
# 			py_type = float if elem.dtype.name.startswith('float') else int
# 			return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
# 	elif isinstance(batch[0], int_classes):
# 		return torch.LongTensor(batch)
# 	elif isinstance(batch[0], float):
# 		return torch.DoubleTensor(batch)
# 	elif isinstance(batch[0], string_classes):
# 		return batch
# 	elif isinstance(batch[0], TreeSpace):
# 		return TreeSpace(**{key: NS_collate([d[key] for d in batch]) for key in batch[0]})
# 	elif isinstance(batch[0], collections.Mapping):
# 		return {key: NS_collate([d[key] for d in batch]) for key in batch[0]}
# 	elif isinstance(batch[0], torch.distributions.Distribution):
# 		return [distrib for distrib in batch]
# 	elif isinstance(batch[0], collections.Sequence):
# 		transposed = zip(*batch)
# 		return [NS_collate(samples) for samples in transposed]
#
# 	raise TypeError((error_msg.format(type(batch[0]))))
import os
import numpy as np
import cv2
from io import BytesIO
from PIL import Image
import torch
from torch.utils.data import DataLoader, Dataset
# from torch.utils.data.dataloader import re, numpy_type_map, _use_shared_memory, string_classes, int_classes#,
import re
try:
	from torch.utils.data.dataloader import container_abcs
except:
	pass

from .misc import TreeSpace
import random
import collections
import itertools
import h5py as hf

import omnifig as fig

import torch
from torch import nn
from torch.nn import functional as F


class make_infinite(DataLoader):
	def __init__(self, loader, extractor=None):
		assert len(loader) >= 1, 'has no len'
		self.loader = loader
		self.itr = None
		self.extractor = extractor
		
		self.cached = None
	
	def __len__(self):
		return len(self.loader)
	
	def end(self):
		self.itr = None
		self.empty()
	
	def empty(self):
		self.cached = None
	
	def demand(self, N, extract=None, merge=None):
		'''
		
		:param N:
		:param extract: callable input: raw batch; output: mergable data with len()
		:param merge: callable input: list of extracted batches; output: whatever is expected
		:return:
		'''
		
		if extract is None:
			extract = self.extractor
		
		missing = N
		batches = []
		
		while missing > 0:
			if self.cached is None:
				x = next(self)
				# by default, assume batch is a tuple and pull first element
				x = x[0] if extract is None else extract(x)
			else:
				x = self.cached
				self.cached = None
			
			if len(x) > missing:
				self.cached = x[missing:]
				x = x[:missing]
			missing -= len(x)
			batches.append(x)
		
		return (torch.cat(batches),) if merge is None else merge(batches)
	
	def __next__(self):
		if self.itr is not None:
			try:
				return next(self.itr)
			except StopIteration:
				pass
		return next(iter(self))
	
	def __iter__(self):
		self.itr = iter(self.loader)
		return self
	


def get_h5_data(path, key, idx=None):
	with hf.File(path, 'r') as f:
		return f[key].value if idx is None else f[key][idx]


def make_np_wrapped_env(env_type):
	def create(*args, **kwargs):
		return Numpy_Env_Wrapper(env_type(*args, **kwargs))
	return create
class Numpy_Env_Wrapper(object):
	def __init__(self, env, device='cpu'):
		self._env = env
		self.device = device
		
	def to(self, d):
		self.device = d
	def cpu(self):
		self.device = 'cpu'
	def cuda(self, i=0):
		self.device = 'cuda:{}'.format(i)

	def __getattr__(self, item):
		if item not in self.__dict__:
			return self._env.__getattribute__(item)
		return super(Numpy_Env_Wrapper, self).__getattribute__(item)

	def reset(self, state=None):
		if state is None:
			return torch.from_numpy(self._env.reset()).float().to(self.device)
		return torch.from_numpy(self._env.reset(state.detach().cpu().numpy())).float().to(self.device)

	def step(self, action):
		ns, r, d, info = self._env.step(action.detach().cpu().numpy())
		ns = torch.from_numpy(ns).float().to(self.device)
		r = torch.tensor(r).float().to(self.device)
		return ns, r, d, info

	def render(self, *args, **kwargs):
		return self._env.render(*args, **kwargs) # no wrapping here



def batches(iterable, n): # batches an iterable (batches are not lazy)
	iterable = iter(iterable)
	while True:
		tup = tuple(itertools.islice(iterable, 0, n))
		if tup:
			yield tup
		else:
			break


def fill_in(src, new, locs, add=True, inplace=False):
	'''fill in `new` in `src` at pixel coords `locs` (B,2) (batched)'''

	squeeze = False
	if len(src.shape) == 3:
		B, H, W = src.shape
		C = 1
		squeeze = True
		src.unsqueeze(1)
		if len(new.shape) == 3:
			new = new.unsqueeze(1)
	else:
		assert len(src.shape) == 4

		B, C, H, W = src.shape

		if C != 1:
			raise NotImplementedError

	U, V = new.shape[-2:]

	u, v = locs.t()

	vals = new.view(-1)
	src = src.view(-1)

	shift = torch.arange(0, U * V).fmod(V).eq(0)
	shift[0] = 0
	shift = shift.int().mul(W - V).cumsum(0)

	pos = torch.arange(U * V * C).unsqueeze(0)

	idx = u * W + v

	inds = (shift + pos + (H * W * C * torch.arange(B) + idx).unsqueeze(1)).view(-1)

	if inplace:

		if add:
			src.index_add_(0, inds, vals)
		else:
			src.index_copy_(0, inds, vals)
		out = src

	else:

		if add:
			out = src.index_add(0, inds, vals)
		else:
			out = src.index_copy(0, inds, vals)

		out = out.view(B, C, H, W)

	if squeeze:
		out.squeeze(1)
	return out


def to_one_hot(idx, max_idx=None):
	if max_idx is None:
		max_idx = idx.max()
	dims = (max_idx,)
	if idx.ndimension() >= 1:
		if idx.size(-1) != 1:
			idx = idx.unsqueeze(-1)
		dims = idx.size()[:-1] + dims
	return torch.zeros(*dims).to(idx.device).scatter_(-1, idx.long(), 1)

# linear - discretize continuous values into N classes
# TODO: implement log space
def discretize(input, N, range=None):

	if range is None:
		range = input.min_val(), input.max()

	input = input.clamp(*range)
	input -= range[0]

	return input.mul((N-1)/(range[1]-range[0])).round().long()

## load image

def load_images(*paths, root=None, channel_first=False, conv_brg=True, **flags):
	assert not len(flags)
	for path in paths:
		if root is not None:
			path = os.path.join(root, path)
		img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
		if conv_brg and len(img.shape) == 3 and img.shape[-1] in {3,4}:
			img = img[..., ::-1].copy()
		if channel_first:
			img = img.transpose(2,0,1)
		yield img

def read_raw_bytes(path, root=None):
	if root is not None:
		path = os.path.join(root, path)
	with open(path, 'rb') as f:
		return f.read()


#########################
# Util to convert images to strings for compressed saving

def rgb_to_str(img, compression=5):
	assert img.shape[2] == 3
	assert img.dtype == np.uint8

	return cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compression])[1].tostring()


def float32_img_to_str(img, compression=5):
	H, W = img.shape
	assert img.dtype == np.float32

	img.dtype = np.uint8
	img.shape = (H, W, 4)

	s = cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compression])[1].tostring()

	img.shape = (H, 4 * W)
	img.dtype = np.float32

	return s


def byte_img_to_str(img, compression=5):
	assert img.dtype == np.uint8

	return cv2.imencode('.png', img, [cv2.IMWRITE_PNG_COMPRESSION, compression])[1].tostring()


def str_to_rgb(s):
	return cv2.imdecode(np.frombuffer(s, np.uint8), -1)


def str_to_float32_img(s):
	img = cv2.imdecode(np.frombuffer(s, np.uint8), -1)

	H, W = img.shape[:2]

	img = img.reshape(H, W * 4)
	img.dtype = np.float32

	return img


def str_to_byte_img(s):
	return cv2.imdecode(np.frombuffer(s, np.uint8), -1)


def jpeg_to_str(path):
	with open(path, 'rb') as f:
		return f.read()

def str_to_jpeg(s, ret_PIL=False):
	img = Image.open(BytesIO(s))
	if ret_PIL:
		return img
	return np.array(img)


# Wrapper to create a DataLoader with NS_collate
# def loader(dataset, def_type=None, multi_agent=False, **kwargs): # switches default_collate to NS_collate
# 	if 'collate_fn' not in kwargs:
# 		kwargs['collate_fn'] = make_collate(def_type, multi_agent)
# 	if 'batch_size' in kwargs and kwargs['batch_size'] is None:
# 		kwargs['batch_size'] = len(dataset)
# 	return DataLoader(dataset, **kwargs)


### old

# multi traj gen: [N] x {NS} x [A]  --> [A] x {NS} x [N]
#
#
# def make_collate(stack=True):
# 	merge_fn = torch.stack if stack else torch.cat
#
# 	def collate(batch):
# 		r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 		error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
# 		elem_type = type(batch[0])
# 		if isinstance(batch[0], torch.Tensor):
# 			out = None
# 			if True:
# 				# If we're in a background process, concatenate directly into a
# 				# shared memory tensor to avoid an extra copy
# 				numel = sum([x.numel() for x in batch])
# 				storage = batch[0].storage()._new_shared(numel)
# 				out = batch[0].new(storage)
# 			try:
# 				return merge_fn(batch, 0, out=out)
# 			except RuntimeError:
# 				return batch
# 		elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
# 				and elem_type.__name__ != 'string_':
# 			elem = batch[0]
# 			if elem_type.__name__ == 'ndarray':
# 				# array of string classes and object
# 				if re.search('[SaUO]', elem.dtype.str) is not None:
# 					raise TypeError(error_msg.format(elem.dtype))
#
# 				return torch.stack([torch.from_numpy(b) for b in batch], 0)
# 			if elem.shape == ():  # scalars
# 				# py_type = float if elem.dtype.name.startswith('float') else int
# 				# return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
# 				return torch.tensor(batch)
# 		elif isinstance(batch[0], int):
# 			return torch.LongTensor(batch)
# 		elif isinstance(batch[0], float):
# 			return torch.DoubleTensor(batch)
# 		elif isinstance(batch[0], str):
# 			return batch
# 		elif isinstance(batch[0], container_abcs.Mapping):
# 			return {key: collate([d[key] for d in batch]) for key in batch[0]}
# 		elif isinstance(batch[0], container_abcs.Sequence):
# 			transposed = zip(*batch)
# 			return [collate(samples) for samples in transposed]
#
# 		raise TypeError((error_msg.format(type(batch[0]))))
#
# 	return collate
#
#
#
# def NS_make_collate(def_type=None, multi_agent=False):
#
# 	collate_fn = None
#
# 	if def_type is None:
# 		collate_fn = NS_list_collate
# 		return collate_fn
#
# 	def NS_list_type_collate(batch):
# 		r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 		full = TreeSpace()
#
# 		for key in batch[0].keys():
# 			full[key] = []
# 			for sample in batch:
# 				data = NS_collate(sample[key]) if key == 'env_infos' else NS_collate([sample[key]])[0] # TODO: clean up
# 				if isinstance(data, torch.Tensor):
# 					data = data.type(def_type)
# 				full[key].append(data)
#
# 		return full  # returns NS containing collated paths
#
# 	collate_fn = NS_list_type_collate
#
# 	if multi_agent:
# 		if def_type is None:
# 			return NS_multi_list_collate
#
# 		def NS_multi_list_type_collate(batch):  # no default tensor type
# 			r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 			num_agents = len(next(iter(batch[0].values())))
#
# 			full = [TreeSpace() for _ in range(num_agents)]  # a separate batch of paths for each agent
#
# 			for i, f in enumerate(full):
# 				for key in batch[0].keys():
# 					for sample in batch:
# 						if isinstance(sample[key], list):
# 							if isinstance(sample[key][0], dict):
# 								data = NS_collate(sample[key])
# 							else:
# 								data = NS_collate([sample[key][i]])[0]
# 								if isinstance(data, torch.Tensor):
# 									data = data.type(def_type)
# 						else:
# 							data = NS_collate([sample[key]])[0]
# 							if isinstance(data, torch.Tensor):
# 								data = data.type(def_type)
# 						if key not in f:
# 							f[key] = []
# 						f[key].append(data)
#
# 			#print(full[0].rewards)
#
# 			return full  # returns NS containing collated paths
#
# 		return NS_multi_list_type_collate
#
# 	return collate_fn
#
#
# def NS_multi_list_collate(batch):  # no default tensor type
# 	r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 	num_agents = len(next(batch[0].values()))
#
# 	full = [TreeSpace() for _ in range(num_agents)] # a separate batch of paths for each agent
#
# 	for i, f in enumerate(full):
# 		for key in batch[0].keys():
# 			f[key] = [NS_collate([sample[key][i]])[0] for sample in batch]
#
# 	return full  # returns NS containing collated paths
#
# def NS_list_collate(batch): # no default tensor type
# 	r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 	full = TreeSpace()
#
# 	if isinstance(batch, dict):
# 		full = TreeSpace(**{k:NS_list_collate(batch[k]) for k in batch.keys()})
# 		return full
#
# 	for key in batch[0].keys():
# 		full[key] = [NS_collate([sample[key]])[0] for sample in batch]
#
# 	return full # returns NS containing collated paths
#
# def NS_collate(batch):
# 	r"""Puts each data field into a tensor with outer dimension batch size"""
#
# 	error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
#
# 	if isinstance(batch, dict):
# 		return {k:NS_collate(batch[k]) for k in batch.keys()}
#
# 	elem_type = type(batch[0])
# 	if isinstance(batch[0], torch.Tensor):
# 		out = None
# 		if _use_shared_memory:
# 			# If we're in a background process, concatenate directly into a
# 			# shared memory tensor to avoid an extra copy
# 			numel = sum([x.numel() for x in batch])
# 			storage = batch[0].storage()._new_shared(numel)
# 			out = batch[0].new(storage)
# 		return torch.stack(batch, 0, out=out)
# 	elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
# 			and elem_type.__name__ != 'string_':
# 		elem = batch[0]
# 		if elem_type.__name__ == 'ndarray':
# 			# array of string classes and object
# 			if re.search('[SaUO]', elem.dtype.str) is not None:
# 				raise TypeError(error_msg.format(elem.dtype))
#
# 			return torch.stack([torch.from_numpy(b) for b in batch], 0)
# 		if elem.shape == ():  # scalars
# 			py_type = float if elem.dtype.name.startswith('float') else int
# 			return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
# 	elif isinstance(batch[0], int_classes):
# 		return torch.LongTensor(batch)
# 	elif isinstance(batch[0], float):
# 		return torch.DoubleTensor(batch)
# 	elif isinstance(batch[0], string_classes):
# 		return batch
# 	elif isinstance(batch[0], TreeSpace):
# 		return TreeSpace(**{key: NS_collate([d[key] for d in batch]) for key in batch[0]})
# 	elif isinstance(batch[0], collections.Mapping):
# 		return {key: NS_collate([d[key] for d in batch]) for key in batch[0]}
# 	elif isinstance(batch[0], torch.distributions.Distribution):
# 		return [distrib for distrib in batch]
# 	elif isinstance(batch[0], collections.Sequence):
# 		transposed = zip(*batch)
# 		return [NS_collate(samples) for samples in transposed]
#
# 	raise TypeError((error_msg.format(type(batch[0]))))
# from torch.utils.data.dataloader import re, numpy_type_map, _use_shared_memory, string_classes, int_classes#, :param N:
 		:param extract: callable input: raw batch; output: mergable data with len()
 		:param merge: callable input: list of extracted batches; output: whatever is expected
 		:return: # by default, assume batch is a tuple and pull first element # no wrapping here # batches an iterable (batches are not lazy) fill in `new` in `src` at pixel coords `locs` (B,2) (batched) # linear - discretize continuous values into N classes # TODO: implement log space ## load image ######################### # Util to convert images to strings for compressed saving # Wrapper to create a DataLoader with NS_collate # def loader(dataset, def_type=None, multi_agent=False, **kwargs): # switches default_collate to NS_collate # 	if 'collate_fn' not in kwargs: # 		kwargs['collate_fn'] = make_collate(def_type, multi_agent) # 	if 'batch_size' in kwargs and kwargs['batch_size'] is None: # 		kwargs['batch_size'] = len(dataset) # 	return DataLoader(dataset, **kwargs) ### old # multi traj gen: [N] x {NS} x [A]  --> [A] x {NS} x [N] # # # def make_collate(stack=True): # 	merge_fn = torch.stack if stack else torch.cat # # 	def collate(batch): # 		r"""Puts each data field into a tensor with outer dimension batch size""" # # 		error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" # 		elem_type = type(batch[0]) # 		if isinstance(batch[0], torch.Tensor): # 			out = None # 			if True: # 				# If we're in a background process, concatenate directly into a # 				# shared memory tensor to avoid an extra copy # 				numel = sum([x.numel() for x in batch]) # 				storage = batch[0].storage()._new_shared(numel) # 				out = batch[0].new(storage) # 			try: # 				return merge_fn(batch, 0, out=out) # 			except RuntimeError: # 				return batch # 		elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ # 				and elem_type.__name__ != 'string_': # 			elem = batch[0] # 			if elem_type.__name__ == 'ndarray': # 				# array of string classes and object # 				if re.search('[SaUO]', elem.dtype.str) is not None: # 					raise TypeError(error_msg.format(elem.dtype)) # # 				return torch.stack([torch.from_numpy(b) for b in batch], 0) # 			if elem.shape == ():  # scalars # 				# py_type = float if elem.dtype.name.startswith('float') else int # 				# return numpy_type_map[elem.dtype.name](list(map(py_type, batch))) # 				return torch.tensor(batch) # 		elif isinstance(batch[0], int): # 			return torch.LongTensor(batch) # 		elif isinstance(batch[0], float): # 			return torch.DoubleTensor(batch) # 		elif isinstance(batch[0], str): # 			return batch # 		elif isinstance(batch[0], container_abcs.Mapping): # 			return {key: collate([d[key] for d in batch]) for key in batch[0]} # 		elif isinstance(batch[0], container_abcs.Sequence): # 			transposed = zip(*batch) # 			return [collate(samples) for samples in transposed] # # 		raise TypeError((error_msg.format(type(batch[0])))) # # 	return collate # # # # def NS_make_collate(def_type=None, multi_agent=False): # # 	collate_fn = None # # 	if def_type is None: # 		collate_fn = NS_list_collate # 		return collate_fn # # 	def NS_list_type_collate(batch): # 		r"""Puts each data field into a tensor with outer dimension batch size""" # # 		full = TreeSpace() # # 		for key in batch[0].keys(): # 			full[key] = [] # 			for sample in batch: # 				data = NS_collate(sample[key]) if key == 'env_infos' else NS_collate([sample[key]])[0] # TODO: clean up # 				if isinstance(data, torch.Tensor): # 					data = data.type(def_type) # 				full[key].append(data) # # 		return full  # returns NS containing collated paths # # 	collate_fn = NS_list_type_collate # # 	if multi_agent: # 		if def_type is None: # 			return NS_multi_list_collate # # 		def NS_multi_list_type_collate(batch):  # no default tensor type # 			r"""Puts each data field into a tensor with outer dimension batch size""" # # 			num_agents = len(next(iter(batch[0].values()))) # # 			full = [TreeSpace() for _ in range(num_agents)]  # a separate batch of paths for each agent # # 			for i, f in enumerate(full): # 				for key in batch[0].keys(): # 					for sample in batch: # 						if isinstance(sample[key], list): # 							if isinstance(sample[key][0], dict): # 								data = NS_collate(sample[key]) # 							else: # 								data = NS_collate([sample[key][i]])[0] # 								if isinstance(data, torch.Tensor): # 									data = data.type(def_type) # 						else: # 							data = NS_collate([sample[key]])[0] # 							if isinstance(data, torch.Tensor): # 								data = data.type(def_type) # 						if key not in f: # 							f[key] = [] # 						f[key].append(data) # # 			#print(full[0].rewards) # # 			return full  # returns NS containing collated paths # # 		return NS_multi_list_type_collate # # 	return collate_fn # # # def NS_multi_list_collate(batch):  # no default tensor type # 	r"""Puts each data field into a tensor with outer dimension batch size""" # # 	num_agents = len(next(batch[0].values())) # # 	full = [TreeSpace() for _ in range(num_agents)] # a separate batch of paths for each agent # # 	for i, f in enumerate(full): # 		for key in batch[0].keys(): # 			f[key] = [NS_collate([sample[key][i]])[0] for sample in batch] # # 	return full  # returns NS containing collated paths # # def NS_list_collate(batch): # no default tensor type # 	r"""Puts each data field into a tensor with outer dimension batch size""" # # 	full = TreeSpace() # # 	if isinstance(batch, dict): # 		full = TreeSpace(**{k:NS_list_collate(batch[k]) for k in batch.keys()}) # 		return full # # 	for key in batch[0].keys(): # 		full[key] = [NS_collate([sample[key]])[0] for sample in batch] # # 	return full # returns NS containing collated paths # # def NS_collate(batch): # 	r"""Puts each data field into a tensor with outer dimension batch size""" # # 	error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" # # 	if isinstance(batch, dict): # 		return {k:NS_collate(batch[k]) for k in batch.keys()} # # 	elem_type = type(batch[0]) # 	if isinstance(batch[0], torch.Tensor): # 		out = None # 		if _use_shared_memory: # 			# If we're in a background process, concatenate directly into a # 			# shared memory tensor to avoid an extra copy # 			numel = sum([x.numel() for x in batch]) # 			storage = batch[0].storage()._new_shared(numel) # 			out = batch[0].new(storage) # 		return torch.stack(batch, 0, out=out) # 	elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ # 			and elem_type.__name__ != 'string_': # 		elem = batch[0] # 		if elem_type.__name__ == 'ndarray': # 			# array of string classes and object # 			if re.search('[SaUO]', elem.dtype.str) is not None: # 				raise TypeError(error_msg.format(elem.dtype)) # # 			return torch.stack([torch.from_numpy(b) for b in batch], 0) # 		if elem.shape == ():  # scalars # 			py_type = float if elem.dtype.name.startswith('float') else int # 			return numpy_type_map[elem.dtype.name](list(map(py_type, batch))) # 	elif isinstance(batch[0], int_classes): # 		return torch.LongTensor(batch) # 	elif isinstance(batch[0], float): # 		return torch.DoubleTensor(batch) # 	elif isinstance(batch[0], string_classes): # 		return batch # 	elif isinstance(batch[0], TreeSpace): # 		return TreeSpace(**{key: NS_collate([d[key] for d in batch]) for key in batch[0]}) # 	elif isinstance(batch[0], collections.Mapping): # 		return {key: NS_collate([d[key] for d in batch]) for key in batch[0]} # 	elif isinstance(batch[0], torch.distributions.Distribution): # 		return [distrib for distrib in batch] # 	elif isinstance(batch[0], collections.Sequence): # 		transposed = zip(*batch) # 		return [NS_collate(samples) for samples in transposed] # # 	raise TypeError((error_msg.format(type(batch[0]))))