"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
Original C++ source file: encode_audio_op_py.cc
"""

import collections as _collections

from tensorflow.python.eager import execute as _execute
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import tensor_shape as _tensor_shape

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.tf_export import tf_export


@tf_export('EncodeAudio')
def encode_audio(sampled_audio, file_format, samples_per_second, bits_per_second=192000, name=None):
  r"""Processes a `Tensor` containing sampled audio with the number of channels

  and length of the audio specified by the dimensions of the `Tensor`. The
  audio is converted into a string that, when saved to disk, will be equivalent
  to the audio in the specified audio format.

  The input audio has one row of the tensor for each channel in the audio file.
  Each channel contains audio samples starting at the beginning of the audio and
  having `1/samples_per_second` time between them. The output file will contain
  all of the audio channels contained in the tensor.

  Args:
    sampled_audio: A `Tensor` of type `float32`.
      A rank 2 tensor containing all tracks of the audio. Dimension 0
      is time and dimension 1 is the channel.
    file_format: A `string`.
      A string describing the audio file format. This must be "wav".
    samples_per_second: An `int`.
      The number of samples per second that the audio should have.
    bits_per_second: An optional `int`. Defaults to `192000`.
      The approximate bitrate of the encoded audio file. This is
      ignored by the "wav" file format.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`. The binary audio file contents.
  """
  file_format = _execute.make_str(file_format, "file_format")
  samples_per_second = _execute.make_int(samples_per_second, "samples_per_second")
  if bits_per_second is None:
    bits_per_second = 192000
  bits_per_second = _execute.make_int(bits_per_second, "bits_per_second")
  _ctx = _context.context()
  if _ctx.in_graph_mode():
    _, _, _op = _op_def_lib._apply_op_helper(
        "EncodeAudio", sampled_audio=sampled_audio, file_format=file_format,
        samples_per_second=samples_per_second,
        bits_per_second=bits_per_second, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("file_format", _op.get_attr("file_format"),
              "samples_per_second", _op.get_attr("samples_per_second"),
              "bits_per_second", _op.get_attr("bits_per_second"))
  else:
    sampled_audio = _ops.convert_to_tensor(sampled_audio, _dtypes.float32)
    _inputs_flat = [sampled_audio]
    _attrs = ("file_format", file_format, "samples_per_second",
              samples_per_second, "bits_per_second", bits_per_second)
    _result = _execute.execute(b"EncodeAudio", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "EncodeAudio", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@tf_export('EncodeAudioV2')
def encode_audio_v2(sampled_audio, file_format, samples_per_second, bits_per_second, name=None):
  r"""Processes a `Tensor` containing sampled audio with the number of channels

  and length of the audio specified by the dimensions of the `Tensor`. The
  audio is converted into a string that, when saved to disk, will be equivalent
  to the audio in the specified audio format.

  The input audio has one row of the tensor for each channel in the audio file.
  Each channel contains audio samples starting at the beginning of the audio and
  having `1/samples_per_second` time between them. The output file will contain
  all of the audio channels contained in the tensor.

  Args:
    sampled_audio: A `Tensor` of type `float32`.
      A rank-2 float tensor containing all tracks of the audio.
      Dimension 0 is time and dimension 1 is the channel.
    file_format: A `Tensor` of type `string`.
      A string or rank-0 string tensor describing the audio file
      format. This value must be `"wav"`.
    samples_per_second: A `Tensor` of type `int32`.
      The number of samples per second that the audio should
      have, as an int or rank-0 `int32` tensor. This value must be
      positive.
    bits_per_second: A `Tensor` of type `int32`.
      The approximate bitrate of the encoded audio file, as
      an int or rank-0 `int32` tensor. This is ignored by the "wav" file
      format.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
    The binary audio file contents, as a rank-0 string tensor.
  """
  _ctx = _context.context()
  if _ctx.in_graph_mode():
    _, _, _op = _op_def_lib._apply_op_helper(
        "EncodeAudioV2", sampled_audio=sampled_audio, file_format=file_format,
        samples_per_second=samples_per_second,
        bits_per_second=bits_per_second, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
  else:
    sampled_audio = _ops.convert_to_tensor(sampled_audio, _dtypes.float32)
    file_format = _ops.convert_to_tensor(file_format, _dtypes.string)
    samples_per_second = _ops.convert_to_tensor(samples_per_second, _dtypes.int32)
    bits_per_second = _ops.convert_to_tensor(bits_per_second, _dtypes.int32)
    _inputs_flat = [sampled_audio, file_format, samples_per_second, bits_per_second]
    _attrs = None
    _result = _execute.execute(b"EncodeAudioV2", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "EncodeAudioV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def _InitOpDefLibrary(op_list_proto_bytes):
  op_list = _op_def_pb2.OpList()
  op_list.ParseFromString(op_list_proto_bytes)
  _op_def_registry.register_op_list(op_list)
  op_def_lib = _op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib
# op {
#   name: "EncodeAudio"
#   input_arg {
#     name: "sampled_audio"
#     type: DT_FLOAT
#   }
#   output_arg {
#     name: "contents"
#     type: DT_STRING
#   }
#   attr {
#     name: "file_format"
#     type: "string"
#   }
#   attr {
#     name: "samples_per_second"
#     type: "int"
#   }
#   attr {
#     name: "bits_per_second"
#     type: "int"
#     default_value {
#       i: 192000
#     }
#   }
# }
# op {
#   name: "EncodeAudioV2"
#   input_arg {
#     name: "sampled_audio"
#     type: DT_FLOAT
#   }
#   input_arg {
#     name: "file_format"
#     type: DT_STRING
#   }
#   input_arg {
#     name: "samples_per_second"
#     type: DT_INT32
#   }
#   input_arg {
#     name: "bits_per_second"
#     type: DT_INT32
#   }
#   output_arg {
#     name: "contents"
#     type: DT_STRING
#   }
# }
_op_def_lib = _InitOpDefLibrary(b"\n~\n\013EncodeAudio\022\021\n\rsampled_audio\030\001\032\014\n\010contents\030\007\"\025\n\013file_format\022\006string\"\031\n\022samples_per_second\022\003int\"\034\n\017bits_per_second\022\003int\032\004\030\200\334\013\nn\n\rEncodeAudioV2\022\021\n\rsampled_audio\030\001\022\017\n\013file_format\030\007\022\026\n\022samples_per_second\030\003\022\023\n\017bits_per_second\030\003\032\014\n\010contents\030\007")

"""Python wrappers around TensorFlow ops.

This file is MACHINE GENERATED! Do not edit.
Original C++ source file: encode_audio_op_py.cc
"""

import collections as _collections

from tensorflow.python.eager import execute as _execute
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import tensor_shape as _tensor_shape

from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.tf_export import tf_export


@tf_export('EncodeAudio')
def encode_audio(sampled_audio, file_format, samples_per_second, bits_per_second=192000, name=None):
  r"""Processes a `Tensor` containing sampled audio with the number of channels

  and length of the audio specified by the dimensions of the `Tensor`. The
  audio is converted into a string that, when saved to disk, will be equivalent
  to the audio in the specified audio format.

  The input audio has one row of the tensor for each channel in the audio file.
  Each channel contains audio samples starting at the beginning of the audio and
  having `1/samples_per_second` time between them. The output file will contain
  all of the audio channels contained in the tensor.

  Args:
    sampled_audio: A `Tensor` of type `float32`.
      A rank 2 tensor containing all tracks of the audio. Dimension 0
      is time and dimension 1 is the channel.
    file_format: A `string`.
      A string describing the audio file format. This must be "wav".
    samples_per_second: An `int`.
      The number of samples per second that the audio should have.
    bits_per_second: An optional `int`. Defaults to `192000`.
      The approximate bitrate of the encoded audio file. This is
      ignored by the "wav" file format.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`. The binary audio file contents.
  """
  file_format = _execute.make_str(file_format, "file_format")
  samples_per_second = _execute.make_int(samples_per_second, "samples_per_second")
  if bits_per_second is None:
    bits_per_second = 192000
  bits_per_second = _execute.make_int(bits_per_second, "bits_per_second")
  _ctx = _context.context()
  if _ctx.in_graph_mode():
    _, _, _op = _op_def_lib._apply_op_helper(
        "EncodeAudio", sampled_audio=sampled_audio, file_format=file_format,
        samples_per_second=samples_per_second,
        bits_per_second=bits_per_second, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = ("file_format", _op.get_attr("file_format"),
              "samples_per_second", _op.get_attr("samples_per_second"),
              "bits_per_second", _op.get_attr("bits_per_second"))
  else:
    sampled_audio = _ops.convert_to_tensor(sampled_audio, _dtypes.float32)
    _inputs_flat = [sampled_audio]
    _attrs = ("file_format", file_format, "samples_per_second",
              samples_per_second, "bits_per_second", bits_per_second)
    _result = _execute.execute(b"EncodeAudio", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "EncodeAudio", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result


@tf_export('EncodeAudioV2')
def encode_audio_v2(sampled_audio, file_format, samples_per_second, bits_per_second, name=None):
  r"""Processes a `Tensor` containing sampled audio with the number of channels

  and length of the audio specified by the dimensions of the `Tensor`. The
  audio is converted into a string that, when saved to disk, will be equivalent
  to the audio in the specified audio format.

  The input audio has one row of the tensor for each channel in the audio file.
  Each channel contains audio samples starting at the beginning of the audio and
  having `1/samples_per_second` time between them. The output file will contain
  all of the audio channels contained in the tensor.

  Args:
    sampled_audio: A `Tensor` of type `float32`.
      A rank-2 float tensor containing all tracks of the audio.
      Dimension 0 is time and dimension 1 is the channel.
    file_format: A `Tensor` of type `string`.
      A string or rank-0 string tensor describing the audio file
      format. This value must be `"wav"`.
    samples_per_second: A `Tensor` of type `int32`.
      The number of samples per second that the audio should
      have, as an int or rank-0 `int32` tensor. This value must be
      positive.
    bits_per_second: A `Tensor` of type `int32`.
      The approximate bitrate of the encoded audio file, as
      an int or rank-0 `int32` tensor. This is ignored by the "wav" file
      format.
    name: A name for the operation (optional).

  Returns:
    A `Tensor` of type `string`.
    The binary audio file contents, as a rank-0 string tensor.
  """
  _ctx = _context.context()
  if _ctx.in_graph_mode():
    _, _, _op = _op_def_lib._apply_op_helper(
        "EncodeAudioV2", sampled_audio=sampled_audio, file_format=file_format,
        samples_per_second=samples_per_second,
        bits_per_second=bits_per_second, name=name)
    _result = _op.outputs[:]
    _inputs_flat = _op.inputs
    _attrs = None
  else:
    sampled_audio = _ops.convert_to_tensor(sampled_audio, _dtypes.float32)
    file_format = _ops.convert_to_tensor(file_format, _dtypes.string)
    samples_per_second = _ops.convert_to_tensor(samples_per_second, _dtypes.int32)
    bits_per_second = _ops.convert_to_tensor(bits_per_second, _dtypes.int32)
    _inputs_flat = [sampled_audio, file_format, samples_per_second, bits_per_second]
    _attrs = None
    _result = _execute.execute(b"EncodeAudioV2", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
  _execute.record_gradient(
      "EncodeAudioV2", _inputs_flat, _attrs, _result, name)
  _result, = _result
  return _result

def _InitOpDefLibrary(op_list_proto_bytes):
  op_list = _op_def_pb2.OpList()
  op_list.ParseFromString(op_list_proto_bytes)
  _op_def_registry.register_op_list(op_list)
  op_def_lib = _op_def_library.OpDefLibrary()
  op_def_lib.add_op_list(op_list)
  return op_def_lib
# op {
#   name: "EncodeAudio"
#   input_arg {
#     name: "sampled_audio"
#     type: DT_FLOAT
#   }
#   output_arg {
#     name: "contents"
#     type: DT_STRING
#   }
#   attr {
#     name: "file_format"
#     type: "string"
#   }
#   attr {
#     name: "samples_per_second"
#     type: "int"
#   }
#   attr {
#     name: "bits_per_second"
#     type: "int"
#     default_value {
#       i: 192000
#     }
#   }
# }
# op {
#   name: "EncodeAudioV2"
#   input_arg {
#     name: "sampled_audio"
#     type: DT_FLOAT
#   }
#   input_arg {
#     name: "file_format"
#     type: DT_STRING
#   }
#   input_arg {
#     name: "samples_per_second"
#     type: DT_INT32
#   }
#   input_arg {
#     name: "bits_per_second"
#     type: DT_INT32
#   }
#   output_arg {
#     name: "contents"
#     type: DT_STRING
#   }
# }
_op_def_lib = _InitOpDefLibrary(b"\n~\n\013EncodeAudio\022\021\n\rsampled_audio\030\001\032\014\n\010contents\030\007\"\025\n\013file_format\022\006string\"\031\n\022samples_per_second\022\003int\"\034\n\017bits_per_second\022\003int\032\004\030\200\334\013\nn\n\rEncodeAudioV2\022\021\n\rsampled_audio\030\001\022\017\n\013file_format\030\007\022\026\n\022samples_per_second\030\003\022\023\n\017bits_per_second\030\003\032\014\n\010contents\030\007")

Python wrappers around TensorFlow ops.  This file is MACHINE GENERATED! Do not edit. Original C++ source file: encode_audio_op_py.cc # Needed to trigger the call to _set_call_cpp_shape_fn. Processes a `Tensor` containing sampled audio with the number of channels    and length of the audio specified by the dimensions of the `Tensor`. The   audio is converted into a string that, when saved to disk, will be equivalent   to the audio in the specified audio format.    The input audio has one row of the tensor for each channel in the audio file.   Each channel contains audio samples starting at the beginning of the audio and   having `1/samples_per_second` time between them. The output file will contain   all of the audio channels contained in the tensor.    Args:     sampled_audio: A `Tensor` of type `float32`.       A rank 2 tensor containing all tracks of the audio. Dimension 0       is time and dimension 1 is the channel.     file_format: A `string`.       A string describing the audio file format. This must be "wav".     samples_per_second: An `int`.       The number of samples per second that the audio should have.     bits_per_second: An optional `int`. Defaults to `192000`.       The approximate bitrate of the encoded audio file. This is       ignored by the "wav" file format.     name: A name for the operation (optional).    Returns:     A `Tensor` of type `string`. The binary audio file contents. Processes a `Tensor` containing sampled audio with the number of channels    and length of the audio specified by the dimensions of the `Tensor`. The   audio is converted into a string that, when saved to disk, will be equivalent   to the audio in the specified audio format.    The input audio has one row of the tensor for each channel in the audio file.   Each channel contains audio samples starting at the beginning of the audio and   having `1/samples_per_second` time between them. The output file will contain   all of the audio channels contained in the tensor.    Args:     sampled_audio: A `Tensor` of type `float32`.       A rank-2 float tensor containing all tracks of the audio.       Dimension 0 is time and dimension 1 is the channel.     file_format: A `Tensor` of type `string`.       A string or rank-0 string tensor describing the audio file       format. This value must be `"wav"`.     samples_per_second: A `Tensor` of type `int32`.       The number of samples per second that the audio should       have, as an int or rank-0 `int32` tensor. This value must be       positive.     bits_per_second: A `Tensor` of type `int32`.       The approximate bitrate of the encoded audio file, as       an int or rank-0 `int32` tensor. This is ignored by the "wav" file       format.     name: A name for the operation (optional).    Returns:     A `Tensor` of type `string`.     The binary audio file contents, as a rank-0 string tensor. # op { #   name: "EncodeAudio" #   input_arg { #     name: "sampled_audio" #     type: DT_FLOAT #   } #   output_arg { #     name: "contents" #     type: DT_STRING #   } #   attr { #     name: "file_format" #     type: "string" #   } #   attr { #     name: "samples_per_second" #     type: "int" #   } #   attr { #     name: "bits_per_second" #     type: "int" #     default_value { #       i: 192000 #     } #   } # } # op { #   name: "EncodeAudioV2" #   input_arg { #     name: "sampled_audio" #     type: DT_FLOAT #   } #   input_arg { #     name: "file_format" #     type: DT_STRING #   } #   input_arg { #     name: "samples_per_second" #     type: DT_INT32 #   } #   input_arg { #     name: "bits_per_second" #     type: DT_INT32 #   } #   output_arg { #     name: "contents" #     type: DT_STRING #   } # }