


"""simple docstring"""







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

import numpy as np


def 					lowercase      (    A_  ,       A_	)->				np.array:
		'''simple docstring'''







		a						:		Tuple											=      F'''{sampling_rate}'''
		a						:		Tuple											=      "1"
		a						:		Any											=      "f32le"
		a						:		Union[str, Any]											=      [
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

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


def 					lowercase      (    A_  ,       A_  ,       A_ = "f32le"  ,       )->				Optional[int]:
		'''simple docstring'''







		a						:		Any											=      F'''{sampling_rate}'''
		a						:		Dict											=      "1"
		if format_for_conversion == "s16le":
				a						:		List[Any]											=      2
		elif format_for_conversion == "f32le":
				a						:		str											=      4
		else:
				raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''	)

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

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


def 					lowercase      (    A_  ,       A_  ,       A_ = None  ,       A_ = None  ,       A_ = "f32le"  ,       )->				Optional[Any]:
		'''simple docstring'''







		if stream_chunk_s is not None:
				a						:		Any											=      stream_chunk_s
		else:
				a						:		Union[str, Any]											=      chunk_length_s

		a						:		Optional[Any]											=      ffmpeg_microphone(A_  ,       A_  ,       format_for_conversion=A_	)
		if format_for_conversion == "s16le":
				a						:		int											=      np.intaa
				a						:		Tuple											=      2
		elif format_for_conversion == "f32le":
				a						:		str											=      np.floataa
				a						:		Any											=      4
		else:
				raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''	)

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

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


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_ = False	)->				int:
		'''simple docstring'''







		a						:		Dict											=      B""
		a						:		List[Any]											=      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}'''	)
		a						:		str											=      0
		for raw in iterator:
				acc += raw
				if stream and len(A_	) < chunk_len:
						a						:		Union[str, Any]											=      (_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(A_	) >= chunk_len:
								# We are flushing the accumulator
								a						:		Tuple											=      (_stride_left, stride_right)
								a						:		Union[str, Any]											=      {"raw": acc[:chunk_len], "stride": stride}
								if stream:
										a						:		Dict											=      False
								yield item
								a						:		Any											=      stride_left
								a						:		str											=      acc[chunk_len - stride_left - stride_right :]
				# Last chunk
		if len(A_	) > stride_left:
				a						:		List[str]											=      {"raw": acc, "stride": (_stride_left, 0)}
				if stream:
						a						:		Union[str, Any]											=      False
				yield item







def 					lowercase      (    A_  ,       A_	)->				List[str]:
		'''simple docstring'''







		a						:		Dict											=      2**24  # 16Mo
		try:
				with subprocess.Popen(A_  ,       stdout=subprocess.PIPE  ,       bufsize=A_	) as ffmpeg_process:
						while True:
								a						:		Any											=      ffmpeg_process.stdout.read(A_	)
								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 (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  else:
    raise ValueError(F'''Task {task} not supported.'''	)

  print(F'''Building PyTorch model from configuration: {config}'''	)
  # Load weights from tf checkpoint
  load_tf_weights_in_tapas(A_  ,       A_  ,       A_	)

  # Save pytorch-model (weights and configuration)
  print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
  model.save_pretrained(A_	)

  # Save tokenizer files
  print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
  a						:		Optional[int]											=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"  ,       model_max_length=512	)
  tokenizer.save_pretrained(A_	)

  print("Used relative position embeddings:"  ,       model.config.reset_position_index_per_cell	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
 )
 parser.add_argument(
     """--reset_position_index_per_cell""",
     default=False,
     action="""store_true""",
     help="""Whether to use relative position embeddings or not. Defaults to True.""",
 )
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--tapas_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained TAPAS model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __lowercase    =				parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )






