




"""simple docstring"""


import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    np.array:
      lowerCAmelCase__      :			int   =	f"""{sampling_rate}"""
      lowerCAmelCase__      :			List[Any]   =	"""1"""
      lowerCAmelCase__      :			Dict   =	"""f32le"""
      lowerCAmelCase__      :			Optional[Any]   =	[
          """ffmpeg""",
          """-i""",
          """pipe:0""",
          """-ac""",
          ac,
          """-ar""",
          ar,
          """-f""",
          format_for_conversion,
          """-hide_banner""",
          """-loglevel""",
          """quiet""",
          """pipe:1""",
      ]

      try:
            with subprocess.Popen(__UpperCAmelCase    ,		stdin=subprocess.PIPE    ,		stdout=subprocess.PIPE				) as ffmpeg_process:
                  lowerCAmelCase__      :			str   =	ffmpeg_process.communicate(__UpperCAmelCase				)
      except FileNotFoundError as error:
            raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""				) from error
      lowerCAmelCase__      :			Tuple   =	output_stream[0]
      lowerCAmelCase__      :			int   =	np.frombuffer(__UpperCAmelCase    ,		np.floataa				)
      if audio.shape[0] == 0:
            raise ValueError("""Malformed soundfile"""				)
      return audio



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase = "f32le"    ,		)						->    Any:
      lowerCAmelCase__      :			List[Any]   =	f"""{sampling_rate}"""
      lowerCAmelCase__      :			int   =	"""1"""
      if format_for_conversion == "s16le":
            lowerCAmelCase__      :			List[str]   =	2
      elif format_for_conversion == "f32le":
            lowerCAmelCase__      :			Optional[Any]   =	4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""				)

      lowerCAmelCase__      :			Tuple   =	platform.system()
      if system == "Linux":
            lowerCAmelCase__      :			List[Any]   =	"""alsa"""
            lowerCAmelCase__      :			Union[str, Any]   =	"""default"""
      elif system == "Darwin":
            lowerCAmelCase__      :			Dict   =	"""avfoundation"""
            lowerCAmelCase__      :			int   =	""":0"""
      elif system == "Windows":
            lowerCAmelCase__      :			List[str]   =	"""dshow"""
            lowerCAmelCase__      :			List[str]   =	"""default"""

      lowerCAmelCase__      :			Any   =	[
          """ffmpeg""",
          """-f""",
          format_,
          """-i""",
          input_,
          """-ac""",
          ac,
          """-ar""",
          ar,
          """-f""",
          format_for_conversion,
          """-fflags""",
          """nobuffer""",
          """-hide_banner""",
          """-loglevel""",
          """quiet""",
          """pipe:1""",
      ]
      lowerCAmelCase__      :			List[str]   =	int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
      lowerCAmelCase__      :			Optional[int]   =	_ffmpeg_stream(__UpperCAmelCase    ,		__UpperCAmelCase				)
      for item in iterator:
            yield item



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = None    ,		__UpperCAmelCase = "f32le"    ,		)						->    List[str]:
      if stream_chunk_s is not None:
            lowerCAmelCase__      :			List[Any]   =	stream_chunk_s
      else:
            lowerCAmelCase__      :			Tuple   =	chunk_length_s

      lowerCAmelCase__      :			int   =	ffmpeg_microphone(__UpperCAmelCase    ,		__UpperCAmelCase    ,		format_for_conversion=__UpperCAmelCase				)
      if format_for_conversion == "s16le":
            lowerCAmelCase__      :			Optional[int]   =	np.intaa
            lowerCAmelCase__      :			Optional[int]   =	2
      elif format_for_conversion == "f32le":
            lowerCAmelCase__      :			Tuple   =	np.floataa
            lowerCAmelCase__      :			Tuple   =	4
      else:
            raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""				)

      if stride_length_s is None:
            lowerCAmelCase__      :			List[Any]   =	chunk_length_s / 6
      lowerCAmelCase__      :			str   =	int(round(sampling_rate * chunk_length_s				)				) * size_of_sample
      if isinstance(__UpperCAmelCase    ,		(int, float)				):
            lowerCAmelCase__      :			str   =	[stride_length_s, stride_length_s]

      lowerCAmelCase__      :			int   =	int(round(sampling_rate * stride_length_s[0]				)				) * size_of_sample
      lowerCAmelCase__      :			Tuple   =	int(round(sampling_rate * stride_length_s[1]				)				) * size_of_sample
      lowerCAmelCase__      :			List[str]   =	datetime.datetime.now()
      lowerCAmelCase__      :			int   =	datetime.timedelta(seconds=__UpperCAmelCase				)
      for item in chunk_bytes_iter(__UpperCAmelCase    ,		__UpperCAmelCase    ,		stride=(stride_left, stride_right)    ,		stream=__UpperCAmelCase				):
            # Put everything back in numpy scale
            lowerCAmelCase__      :			Union[str, Any]   =	np.frombuffer(item["""raw"""]    ,		dtype=__UpperCAmelCase				)
            lowerCAmelCase__      :			int   =	(
                item["""stride"""][0] // size_of_sample,
                item["""stride"""][1] // size_of_sample,
            )
            lowerCAmelCase__      :			List[Any]   =	sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 10 * delta:
                  # We're late !! SKIP
                  continue
            yield item



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase = False				)						->    str:
      lowerCAmelCase__      :			int   =	B""""""
      lowerCAmelCase__      :			Optional[int]   =	stride
      if stride_left + stride_right >= chunk_len:
            raise ValueError(
                f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""				)
      lowerCAmelCase__      :			str   =	0
      for raw in iterator:
            acc += raw
            if stream and len(__UpperCAmelCase				) < chunk_len:
                  lowerCAmelCase__      :			Union[str, Any]   =	(_stride_left, 0)
                  yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
            else:
                  while len(__UpperCAmelCase				) >= chunk_len:
                        # We are flushing the accumulator
                        lowerCAmelCase__      :			Tuple   =	(_stride_left, stride_right)
                        lowerCAmelCase__      :			Dict   =	{"""raw""": acc[:chunk_len], """stride""": stride}
                        if stream:
                              lowerCAmelCase__      :			Dict   =	False
                        yield item
                        lowerCAmelCase__      :			Optional[Any]   =	stride_left
                        lowerCAmelCase__      :			List[Any]   =	acc[chunk_len - stride_left - stride_right :]
    # Last chunk
      if len(__UpperCAmelCase				) > stride_left:
            lowerCAmelCase__      :			Any   =	{"""raw""": acc, """stride""": (_stride_left, 0)}
            if stream:
                  lowerCAmelCase__      :			Optional[Any]   =	False
            yield item



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      lowerCAmelCase__      :			Tuple   =	2**24  # 16Mo
      try:
            with subprocess.Popen(__UpperCAmelCase    ,		stdout=subprocess.PIPE    ,		bufsize=__UpperCAmelCase				) as ffmpeg_process:
                  while True:
                        lowerCAmelCase__      :			List[Any]   =	ffmpeg_process.stdout.read(__UpperCAmelCase				)
                        if raw == b"":
                              break
                        yield raw
      except FileNotFoundError as error:
            raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""				) from error











"""simple docstring"""


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
      model.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





