import hashlib
import json
import math
import os
import dill
import base64
from sys import exit
import requests
from bson import ObjectId
from Crypto.Cipher import PKCS1_OAEP
from Crypto.Hash import SHA256
from Crypto.PublicKey import RSA
#from cryptography.hazmat.primitives.asymmetric import padding
#from cryptography.hazmat.primitives import serialization, hashes
from tqdm import tqdm
from requests_toolbelt import MultipartEncoder, MultipartEncoderMonitor
from datetime import datetime

from .Model import Model
from .DataLoader import DataLoader
from .Dataset import Dataset
from .saving.saving import save_data, determine_model, TF_str, mxnet_str, pytorch_str
from .web.urls import TOKEN_URL, HASH_URL, UPLOAD_DATA_URL

VERBOSITY = 1
MIN_VERBOSITY = 1
MID_VERBOSITY = 2
FULL_VERBOSITY = 3

_token = ""
_project = ""
_deployed = False

utcnow = datetime.utcnow

with open(os.path.join(os.path.dirname(__file__), "pub_cred_key.pub"), "rb") as key_file:
    #pub_key_encryption = serialization.load_pem_public_key(key_file.read())
    pub_key_encryption = PKCS1_OAEP.new(RSA.importKey(key_file.read()), SHA256)


# from SO
class bcolors:
    PURPLE = '\033[95m'
    OKBLUE = '\033[94m'
    OKCYAN = '\033[96m'
    OKGREEN = '\033[92m'
    WARNING = '\033[93m'
    FAIL = '\033[91m'
    ENDC = '\033[0m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    ORANGE = '\33[38;5;208m'


levels = {"WARNING": bcolors.ORANGE, "INFO": bcolors.PURPLE, "ERROR": bcolors.FAIL}

NEURO_AI_STR = f"[{bcolors.OKBLUE}Neuro Ai{bcolors.ENDC}]"


def api(token_, project_name, verbosity, deployed):
    global _token
    global _project
    global VERBOSITY
    global _deployed
    if token_ == "":
        token_ = os.environ.get("NPU_API_TOKEN", "")
    _token = token_
    VERBOSITY = verbosity
    verbose_print(f"Verbosity level set to {VERBOSITY}", MID_VERBOSITY)
    _deployed = deployed
    if _deployed:
        npu_print("DEPLOYMENT MODE")
    params = {"token": _token, "project_name": project_name}
    response = post(TOKEN_URL, json=params)
    if response.status_code == 200:
        npu_print("Token successfully authenticated")
        _project = response.json()
        npu_print(f"Using project: {project_name}")
        return response
    else:
        raise ValueError(response.text)
    # "API token not valid"


def getToken():
    return _token


def auth_header():
    return {"authorization": "Bearer " + getToken()}


def get_verbosity():
    return VERBOSITY


def get_project():
    return _project


def is_deployed():
    return _deployed


def get_response(response):
    try:
        return response.json()
    except Exception as e:
        raise ConnectionError("Invalid response received. Error: {}".format(response.text))


# https://stackoverflow.com/questions/5194057/better-way-to-convert-file-sizes-in-python
def convert_size(size_bytes):
    if size_bytes == 0:
        return "0B"
    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
    i = int(math.floor(math.log(size_bytes, 1024)))
    p = math.pow(1024, i)
    s = round(size_bytes / p, 2)
    return "%s %s" % (s, size_name[i])


def add_kwargs_to_params(params, **kwargs):
    params = {**params, **kwargs}
    return params


def read_in_chunks(file_object, chunk_size=1024):
    """Lazy function (generator) to read a file piece by piece.
    Default chunk size: 1k."""
    while True:
        data = file_object.read(chunk_size)
        if not data:
            break
        yield data


def check_model(model):
    from .Task import Task
    from .Model import Model
    if not isinstance(model, Task) and not isinstance(model, str) and not isinstance(model, Model):
        raise ValueError("Model is not a valid format. Please make sure you've compiled it first.")


def check_model_type(model, params):
    from .Task import Task
    if isinstance(model, Model):
        params["model_name"] = model.name
        params["model_attr"] = model.attr
    elif isinstance(model, str) and not ObjectId.is_valid(model):
        params["model_name"] = model
    elif model != "" and not isinstance(model, Task):
        params["modelId"] = model


def check_data_type(data, param_name, params):
    from .Task import Task
    if isinstance(data, Dataset):
        params[param_name + "_name"] = data.id
    elif isinstance(data, str) and not ObjectId.is_valid(data):
        params[param_name + "_name"] = data
    elif isinstance(data, HubDataset):
        params[param_name + "Id"] = data.hub_meta
    elif data != "" and not isinstance(data, Task):
        params[param_name + "Id"] = data
    params[f"{param_name}_hub_ds"] = isinstance(data, HubDataset)


def check_data(data, name=""):
    if not isinstance(name, str):
        raise ValueError("Name given is not valid. Please supply a string.")
    if isinstance(data, dict):
        return data
    try:
        import hub
        hub_meta = {}
        if hasattr(data, "dataset"):
            if hasattr(data, "indexes"):
                hub_meta["indexes"] = data.indexes
            if hasattr(data, "subpath"):
                hub_meta["subpath"] = data.subpath
            data = data.dataset
        if isinstance(data, hub.Dataset):
            encrypted_token = base64.b64encode(
                pub_key_encryption.encrypt(
                    json.dumps(data.token).encode()
                )).decode()
                #pub_key_encryption.encrypt(
                #    json.dumps(data.token).encode(),
                #    padding.OAEP(
                #        mgf=padding.MGF1(
                #            algorithm=hashes.SHA256()), algorithm=hashes.SHA256(), label=None))).decode()
            hub_meta = {"url": data.url, "schema": data.schema, "token": encrypted_token, **hub_meta}
            hub_meta = base64.b64encode(dill.dumps(hub_meta)).decode()
            return HubDataset(hub_meta)
    except Exception as e:
        # print(e)
        pass
    if isinstance(data, str) and (data.endswith(("npy", "npz")) or ObjectId.is_valid(data) or data == ""):
        return data
    elif isinstance(data, Dataset):
        return data
    elif isinstance(data, DataLoader):
        response = upload_data_loader(data, name)
    else:
        response = upload_data(data, name)
    status_code = response.status_code
    if status_code not in (204, 200, 201):
        raise ConnectionAbortedError("Data upload has not worked: {}".format(response.content))
    if status_code != 204:
        response = get_response(response)
    if isinstance(response, dict) and status_code == 200:
        message = response.get("message")
        npu_print(message)
        response = response["id"]
    return response


def slice_data(data):
    id = data["id"]
    start = data["indexes"]
    end = None
    if isinstance(start, slice):
        end = start.stop
        start = start.start
    return id, start, end


def gen(dl):
    for data_part in dl.numpy():
        yield save_data(data_part)


def create_callback(encoder):
    encoder_len = encoder.len
    bar = tqdm(desc=f"{NEURO_AI_STR} Uploading", unit="B", unit_scale=True, total=encoder_len, unit_divisor=1024)

    def callback(monitor):
        bar.n = monitor.bytes_read
        bar.refresh()
        if monitor.bytes_read == encoder_len:
            bar.close()

    return callback


def get_progress_bar_uploader(file, json):
    encoder = create_upload(file, json)
    callback = create_callback(encoder)
    monitor = MultipartEncoderMonitor(encoder, callback)
    return monitor


def create_upload(file, _json):
    return MultipartEncoder({
        'file': ('file', file, 'application/octet-stream', {'Content-Transfer-Encoding': 'binary'}),
        'json': (None, json.dumps(_json), 'application/json', {}),
    })


def upload_data_loader(dl, name=""):
    verbose_print("Hashing data locally...", MID_VERBOSITY)
    hash, size, length = dl.hash()
    params = {"token": getToken(), "hash": hash, "collection": 1, "chunked": True, "is_last": False, "size": size,
              "given_name": name, "input_shape": dl.shape, "project": get_project()}
    # params = {"token": getToken(), "hash": hash, "collection": 1, "size": size, "given_name": name}
    verbose_print("Checking if data is on servers...", MID_VERBOSITY)
    response = get(HASH_URL, params=params)
    if response.status_code == 200:
        verbose_print("Data already uploaded. Will not reupload.", MID_VERBOSITY)
        return response
    npu_print("Data not on servers. Starting to upload. Total size of data is {}".format(convert_size(size)))
    if length == 1:
        return upload_data(next(dl.numpy()), name)
    npu_print("{} chunks to upload...".format(length))
    for i, data_part in enumerate(dl.numpy()):
        verbose_print("Uploading chunk {} out of {}...".format(i + 1, length), MID_VERBOSITY)
        if i == length - 1:
            params["is_last"] = True
        file = save_data(data_part)
        monitor = get_progress_bar_uploader(file, params)
        response = post(UPLOAD_DATA_URL, data=monitor,
                        headers={'Content-Type': monitor.content_type})
    return response


def upload_data(data, name=""):
    verbose_print("Saving data locally...", FULL_VERBOSITY)
    generic_file = False
    if isinstance(data, str):
        file = open(data, "rb")
        generic_file = True
    else:
        file = save_data(data)
    verbose_print("Hashing...", FULL_VERBOSITY)
    hash = hashlib.md5()
    for piece in read_in_chunks(file):
        hash.update(piece)
    size = file.tell()
    hash = hash.hexdigest()
    verbose_print("Checking if data is on servers...", MID_VERBOSITY)
    params = {"token": getToken(), "hash": hash, "collection": 1, "given_name": name, "project": get_project(),
              "generic_file": generic_file}
    response = get(HASH_URL, params=params, json=params)
    if response.status_code == 200:
        verbose_print("Data already on servers. Returning result...", MID_VERBOSITY)
        file.close()
        return response
    npu_print("Data not found on servers. Total size of data is {}. Uploading now...".format(convert_size(size)))
    file.seek(0)

    monitor = get_progress_bar_uploader(file=file, json=params)
    response = post(UPLOAD_DATA_URL, data=monitor,
                    headers={'Content-Type': monitor.content_type})
    if isinstance(data, str):
        file.close()
    return response


def upload_sample(data, params):
    required = (len(data[0]) if isinstance(data, (tuple, list)) else len(data)) > 10
    if not required:
        return False
    data = [d[:10] for d in data] if isinstance(data, (tuple, list)) else data[:10]


def hash_file(file):
    hash = hashlib.md5()
    for piece in read_in_chunks(file):
        hash.update(piece)
        # break
    hash = hash.hexdigest()
    return hash


def validate_model(model, data):
    library = determine_model(model)
    if isinstance(data, str):
        return
    # data = convert_to_numpy(data)
    if library == pytorch_str:
        from torch import ones
    elif library == mxnet_str:
        from mxnet import nd
        ones = nd.ones
    elif library == TF_str:
        from numpy import ones
    else:
        return
        # raise ValueError("Cannot validate library: {} .".format(library))
    placeholder_data = ones(data.shape)
    model(placeholder_data)


def determine_data(data):
    start = end = None
    name = ""
    if isinstance(data, dict):
        data, start, end = slice_data(data)
    if isinstance(data, Dataset):
        name = data.id
        data = data
    return data, name, start, end


def npu_print(val, level="INFO"):
    log_str = f"{NEURO_AI_STR} {utcnow_formatted()} - [{levels[level]}{level}{bcolors.ENDC}]: {val}"
    print(f"{log_str}")


def verbose_print(str, verbosity):
    if VERBOSITY >= verbosity:
        npu_print(str)


def utcnow_formatted():
    return utcnow().strftime("%H:%M:%S")


def make_request(request_type_function, url, data, headers, json, params, **kwargs):
    if params is None:
        params = {}
    if json is None:
        json = {}
    if data is None:
        data = {}
    if headers is None:
        headers = {}
    try:
        response = request_type_function(url, data=data, headers={**headers, **auth_header()}, json=json,
                                         params=params, **kwargs)
        response.raise_for_status()
        return response
    except requests.exceptions.RequestException as _:
        response = response.json()
        if "error" in response:
            npu_print(f"Error: {response['error']}", level="ERROR")
        elif "message" in response:
            npu_print(f"Error: {response['message']}", level="ERROR")
        raise Exception
    # exit(1)


def post(url, data=None, headers=None, json=None, params=None, **kwargs):
    return make_request(requests.post, url, data, headers, json, params, **kwargs)


def get(url, data=None, headers=None, json=None, params=None, **kwargs):
    return make_request(requests.get, url, data, headers, json, params, **kwargs)


class HubDataset:
    def __init__(self, hub_meta):
        self.hub_meta = hub_meta

import hashlib
import json
import math
import os
import dill
import base64
from sys import exit
import requests
from bson import ObjectId
from Crypto.Cipher import PKCS1_OAEP
from Crypto.Hash import SHA256
from Crypto.PublicKey import RSA
#from cryptography.hazmat.primitives.asymmetric import padding
#from cryptography.hazmat.primitives import serialization, hashes
from tqdm import tqdm
from requests_toolbelt import MultipartEncoder, MultipartEncoderMonitor
from datetime import datetime

from .Model import Model
from .DataLoader import DataLoader
from .Dataset import Dataset
from .saving.saving import save_data, determine_model, TF_str, mxnet_str, pytorch_str
from .web.urls import TOKEN_URL, HASH_URL, UPLOAD_DATA_URL

VERBOSITY = 1
MIN_VERBOSITY = 1
MID_VERBOSITY = 2
FULL_VERBOSITY = 3

_token = ""
_project = ""
_deployed = False

utcnow = datetime.utcnow

with open(os.path.join(os.path.dirname(__file__), "pub_cred_key.pub"), "rb") as key_file:
    #pub_key_encryption = serialization.load_pem_public_key(key_file.read())
    pub_key_encryption = PKCS1_OAEP.new(RSA.importKey(key_file.read()), SHA256)


# from SO
class bcolors:
    PURPLE = '\033[95m'
    OKBLUE = '\033[94m'
    OKCYAN = '\033[96m'
    OKGREEN = '\033[92m'
    WARNING = '\033[93m'
    FAIL = '\033[91m'
    ENDC = '\033[0m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    ORANGE = '\33[38;5;208m'


levels = {"WARNING": bcolors.ORANGE, "INFO": bcolors.PURPLE, "ERROR": bcolors.FAIL}

NEURO_AI_STR = f"[{bcolors.OKBLUE}Neuro Ai{bcolors.ENDC}]"


def api(token_, project_name, verbosity, deployed):
    global _token
    global _project
    global VERBOSITY
    global _deployed
    if token_ == "":
        token_ = os.environ.get("NPU_API_TOKEN", "")
    _token = token_
    VERBOSITY = verbosity
    verbose_print(f"Verbosity level set to {VERBOSITY}", MID_VERBOSITY)
    _deployed = deployed
    if _deployed:
        npu_print("DEPLOYMENT MODE")
    params = {"token": _token, "project_name": project_name}
    response = post(TOKEN_URL, json=params)
    if response.status_code == 200:
        npu_print("Token successfully authenticated")
        _project = response.json()
        npu_print(f"Using project: {project_name}")
        return response
    else:
        raise ValueError(response.text)
    # "API token not valid"


def getToken():
    return _token


def auth_header():
    return {"authorization": "Bearer " + getToken()}


def get_verbosity():
    return VERBOSITY


def get_project():
    return _project


def is_deployed():
    return _deployed


def get_response(response):
    try:
        return response.json()
    except Exception as e:
        raise ConnectionError("Invalid response received. Error: {}".format(response.text))


# https://stackoverflow.com/questions/5194057/better-way-to-convert-file-sizes-in-python
def convert_size(size_bytes):
    if size_bytes == 0:
        return "0B"
    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
    i = int(math.floor(math.log(size_bytes, 1024)))
    p = math.pow(1024, i)
    s = round(size_bytes / p, 2)
    return "%s %s" % (s, size_name[i])


def add_kwargs_to_params(params, **kwargs):
    params = {**params, **kwargs}
    return params


def read_in_chunks(file_object, chunk_size=1024):
    """Lazy function (generator) to read a file piece by piece.
    Default chunk size: 1k."""
    while True:
        data = file_object.read(chunk_size)
        if not data:
            break
        yield data


def check_model(model):
    from .Task import Task
    from .Model import Model
    if not isinstance(model, Task) and not isinstance(model, str) and not isinstance(model, Model):
        raise ValueError("Model is not a valid format. Please make sure you've compiled it first.")


def check_model_type(model, params):
    from .Task import Task
    if isinstance(model, Model):
        params["model_name"] = model.name
        params["model_attr"] = model.attr
    elif isinstance(model, str) and not ObjectId.is_valid(model):
        params["model_name"] = model
    elif model != "" and not isinstance(model, Task):
        params["modelId"] = model


def check_data_type(data, param_name, params):
    from .Task import Task
    if isinstance(data, Dataset):
        params[param_name + "_name"] = data.id
    elif isinstance(data, str) and not ObjectId.is_valid(data):
        params[param_name + "_name"] = data
    elif isinstance(data, HubDataset):
        params[param_name + "Id"] = data.hub_meta
    elif data != "" and not isinstance(data, Task):
        params[param_name + "Id"] = data
    params[f"{param_name}_hub_ds"] = isinstance(data, HubDataset)


def check_data(data, name=""):
    if not isinstance(name, str):
        raise ValueError("Name given is not valid. Please supply a string.")
    if isinstance(data, dict):
        return data
    try:
        import hub
        hub_meta = {}
        if hasattr(data, "dataset"):
            if hasattr(data, "indexes"):
                hub_meta["indexes"] = data.indexes
            if hasattr(data, "subpath"):
                hub_meta["subpath"] = data.subpath
            data = data.dataset
        if isinstance(data, hub.Dataset):
            encrypted_token = base64.b64encode(
                pub_key_encryption.encrypt(
                    json.dumps(data.token).encode()
                )).decode()
                #pub_key_encryption.encrypt(
                #    json.dumps(data.token).encode(),
                #    padding.OAEP(
                #        mgf=padding.MGF1(
                #            algorithm=hashes.SHA256()), algorithm=hashes.SHA256(), label=None))).decode()
            hub_meta = {"url": data.url, "schema": data.schema, "token": encrypted_token, **hub_meta}
            hub_meta = base64.b64encode(dill.dumps(hub_meta)).decode()
            return HubDataset(hub_meta)
    except Exception as e:
        # print(e)
        pass
    if isinstance(data, str) and (data.endswith(("npy", "npz")) or ObjectId.is_valid(data) or data == ""):
        return data
    elif isinstance(data, Dataset):
        return data
    elif isinstance(data, DataLoader):
        response = upload_data_loader(data, name)
    else:
        response = upload_data(data, name)
    status_code = response.status_code
    if status_code not in (204, 200, 201):
        raise ConnectionAbortedError("Data upload has not worked: {}".format(response.content))
    if status_code != 204:
        response = get_response(response)
    if isinstance(response, dict) and status_code == 200:
        message = response.get("message")
        npu_print(message)
        response = response["id"]
    return response


def slice_data(data):
    id = data["id"]
    start = data["indexes"]
    end = None
    if isinstance(start, slice):
        end = start.stop
        start = start.start
    return id, start, end


def gen(dl):
    for data_part in dl.numpy():
        yield save_data(data_part)


def create_callback(encoder):
    encoder_len = encoder.len
    bar = tqdm(desc=f"{NEURO_AI_STR} Uploading", unit="B", unit_scale=True, total=encoder_len, unit_divisor=1024)

    def callback(monitor):
        bar.n = monitor.bytes_read
        bar.refresh()
        if monitor.bytes_read == encoder_len:
            bar.close()

    return callback


def get_progress_bar_uploader(file, json):
    encoder = create_upload(file, json)
    callback = create_callback(encoder)
    monitor = MultipartEncoderMonitor(encoder, callback)
    return monitor


def create_upload(file, _json):
    return MultipartEncoder({
        'file': ('file', file, 'application/octet-stream', {'Content-Transfer-Encoding': 'binary'}),
        'json': (None, json.dumps(_json), 'application/json', {}),
    })


def upload_data_loader(dl, name=""):
    verbose_print("Hashing data locally...", MID_VERBOSITY)
    hash, size, length = dl.hash()
    params = {"token": getToken(), "hash": hash, "collection": 1, "chunked": True, "is_last": False, "size": size,
              "given_name": name, "input_shape": dl.shape, "project": get_project()}
    # params = {"token": getToken(), "hash": hash, "collection": 1, "size": size, "given_name": name}
    verbose_print("Checking if data is on servers...", MID_VERBOSITY)
    response = get(HASH_URL, params=params)
    if response.status_code == 200:
        verbose_print("Data already uploaded. Will not reupload.", MID_VERBOSITY)
        return response
    npu_print("Data not on servers. Starting to upload. Total size of data is {}".format(convert_size(size)))
    if length == 1:
        return upload_data(next(dl.numpy()), name)
    npu_print("{} chunks to upload...".format(length))
    for i, data_part in enumerate(dl.numpy()):
        verbose_print("Uploading chunk {} out of {}...".format(i + 1, length), MID_VERBOSITY)
        if i == length - 1:
            params["is_last"] = True
        file = save_data(data_part)
        monitor = get_progress_bar_uploader(file, params)
        response = post(UPLOAD_DATA_URL, data=monitor,
                        headers={'Content-Type': monitor.content_type})
    return response


def upload_data(data, name=""):
    verbose_print("Saving data locally...", FULL_VERBOSITY)
    generic_file = False
    if isinstance(data, str):
        file = open(data, "rb")
        generic_file = True
    else:
        file = save_data(data)
    verbose_print("Hashing...", FULL_VERBOSITY)
    hash = hashlib.md5()
    for piece in read_in_chunks(file):
        hash.update(piece)
    size = file.tell()
    hash = hash.hexdigest()
    verbose_print("Checking if data is on servers...", MID_VERBOSITY)
    params = {"token": getToken(), "hash": hash, "collection": 1, "given_name": name, "project": get_project(),
              "generic_file": generic_file}
    response = get(HASH_URL, params=params, json=params)
    if response.status_code == 200:
        verbose_print("Data already on servers. Returning result...", MID_VERBOSITY)
        file.close()
        return response
    npu_print("Data not found on servers. Total size of data is {}. Uploading now...".format(convert_size(size)))
    file.seek(0)

    monitor = get_progress_bar_uploader(file=file, json=params)
    response = post(UPLOAD_DATA_URL, data=monitor,
                    headers={'Content-Type': monitor.content_type})
    if isinstance(data, str):
        file.close()
    return response


def upload_sample(data, params):
    required = (len(data[0]) if isinstance(data, (tuple, list)) else len(data)) > 10
    if not required:
        return False
    data = [d[:10] for d in data] if isinstance(data, (tuple, list)) else data[:10]


def hash_file(file):
    hash = hashlib.md5()
    for piece in read_in_chunks(file):
        hash.update(piece)
        # break
    hash = hash.hexdigest()
    return hash


def validate_model(model, data):
    library = determine_model(model)
    if isinstance(data, str):
        return
    # data = convert_to_numpy(data)
    if library == pytorch_str:
        from torch import ones
    elif library == mxnet_str:
        from mxnet import nd
        ones = nd.ones
    elif library == TF_str:
        from numpy import ones
    else:
        return
        # raise ValueError("Cannot validate library: {} .".format(library))
    placeholder_data = ones(data.shape)
    model(placeholder_data)


def determine_data(data):
    start = end = None
    name = ""
    if isinstance(data, dict):
        data, start, end = slice_data(data)
    if isinstance(data, Dataset):
        name = data.id
        data = data
    return data, name, start, end


def npu_print(val, level="INFO"):
    log_str = f"{NEURO_AI_STR} {utcnow_formatted()} - [{levels[level]}{level}{bcolors.ENDC}]: {val}"
    print(f"{log_str}")


def verbose_print(str, verbosity):
    if VERBOSITY >= verbosity:
        npu_print(str)


def utcnow_formatted():
    return utcnow().strftime("%H:%M:%S")


def make_request(request_type_function, url, data, headers, json, params, **kwargs):
    if params is None:
        params = {}
    if json is None:
        json = {}
    if data is None:
        data = {}
    if headers is None:
        headers = {}
    try:
        response = request_type_function(url, data=data, headers={**headers, **auth_header()}, json=json,
                                         params=params, **kwargs)
        response.raise_for_status()
        return response
    except requests.exceptions.RequestException as _:
        response = response.json()
        if "error" in response:
            npu_print(f"Error: {response['error']}", level="ERROR")
        elif "message" in response:
            npu_print(f"Error: {response['message']}", level="ERROR")
        raise Exception
    # exit(1)


def post(url, data=None, headers=None, json=None, params=None, **kwargs):
    return make_request(requests.post, url, data, headers, json, params, **kwargs)


def get(url, data=None, headers=None, json=None, params=None, **kwargs):
    return make_request(requests.get, url, data, headers, json, params, **kwargs)


class HubDataset:
    def __init__(self, hub_meta):
        self.hub_meta = hub_meta

#from cryptography.hazmat.primitives.asymmetric import padding #from cryptography.hazmat.primitives import serialization, hashes #pub_key_encryption = serialization.load_pem_public_key(key_file.read()) # from SO # "API token not valid" # https://stackoverflow.com/questions/5194057/better-way-to-convert-file-sizes-in-python Lazy function (generator) to read a file piece by piece.     Default chunk size: 1k. #pub_key_encryption.encrypt( #    json.dumps(data.token).encode(), #    padding.OAEP( #        mgf=padding.MGF1( #            algorithm=hashes.SHA256()), algorithm=hashes.SHA256(), label=None))).decode() # print(e) # params = {"token": getToken(), "hash": hash, "collection": 1, "size": size, "given_name": name} # break # data = convert_to_numpy(data) # raise ValueError("Cannot validate library: {} .".format(library)) # exit(1)