



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

import numpy as np







def    _snake_case (       __snake_case					,  __snake_case    )     ->     np.array:






      '''simple docstring'''







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

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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case = "f32le"					,  )     ->     Union[str, Any]:






      '''simple docstring'''







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

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

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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case = None					,  __snake_case = None					,  __snake_case = "f32le"					,  )     ->     Union[str, Any]:






      '''simple docstring'''







      if stream_chunk_s is not None:
            UpperCAmelCase_  :	Dict				      =		stream_chunk_s
      else:
            UpperCAmelCase_  :	Dict				      =		chunk_length_s

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

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

      UpperCAmelCase_  :	Tuple				      =		int(round(sampling_rate * stride_length_s[0]    )    ) * size_of_sample
      UpperCAmelCase_  :	str				      =		int(round(sampling_rate * stride_length_s[1]    )    ) * size_of_sample
      UpperCAmelCase_  :	Union[str, Any]				      =		datetime.datetime.now()
      UpperCAmelCase_  :	int				      =		datetime.timedelta(seconds=__snake_case    )
      for item in chunk_bytes_iter(__snake_case					,  __snake_case					,  stride=(stride_left, stride_right)					,  stream=__snake_case    ):
            # Put everything back in numpy scale
            UpperCAmelCase_  :	Dict				      =		np.frombuffer(item["raw"]					,  dtype=__snake_case    )
            UpperCAmelCase_  :	List[str]				      =		(
                item['''stride'''][0] // size_of_sample,
                item['''stride'''][1] // size_of_sample,
            )
            UpperCAmelCase_  :	Optional[int]				      =		sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 1_0 * delta:
                  # We're late !! SKIP
                  continue
            yield item







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = False    )     ->     Optional[Any]:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case    )     ->     List[str]:






      '''simple docstring'''







      UpperCAmelCase_  :	Tuple				      =		2**2_4  # 16Mo
      try:
            with subprocess.Popen(__snake_case					,  stdout=subprocess.PIPE					,  bufsize=__snake_case    ) as ffmpeg_process:
                  while True:
                        UpperCAmelCase_  :	Optional[int]				      =		ffmpeg_process.stdout.read(__snake_case    )
                        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





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase         =			False
__lowerCamelCase         =			False







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		parser.add_parser("train"     ,help="CLI tool to train a model on a task.")

             train_parser.add_argument(
                 "--train_data"     ,type=lowercase     ,required=lowercase     ,help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."     ,)
             train_parser.add_argument(
                 "--column_label"     ,type=lowercase     ,default=0     ,help="Column of the dataset csv file with example labels.")
             train_parser.add_argument(
                 "--column_text"     ,type=lowercase     ,default=1     ,help="Column of the dataset csv file with example texts.")
             train_parser.add_argument(
                 "--column_id"     ,type=lowercase     ,default=2     ,help="Column of the dataset csv file with example ids.")
             train_parser.add_argument(
                 "--skip_first_row"     ,action="store_true"     ,help="Skip the first row of the csv file (headers).")

             train_parser.add_argument("--validation_data"     ,type=lowercase     ,default=""     ,help="path to validation dataset.")
             train_parser.add_argument(
                 "--validation_split"     ,type=lowercase     ,default=0.1     ,help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."     ,)

             train_parser.add_argument("--output"     ,type=lowercase     ,default="./"     ,help="path to saved the trained model.")

             train_parser.add_argument(
                 "--task"     ,type=lowercase     ,default="text_classification"     ,help="Task to train the model on.")
             train_parser.add_argument(
                 "--model"     ,type=lowercase     ,default="bert-base-uncased"     ,help="Model's name or path to stored model.")
             train_parser.add_argument("--train_batch_size"     ,type=lowercase     ,default=32     ,help="Batch size for training.")
             train_parser.add_argument("--valid_batch_size"     ,type=lowercase     ,default=64     ,help="Batch size for validation.")
             train_parser.add_argument("--learning_rate"     ,type=lowercase     ,default=3E-5     ,help="Learning rate.")
             train_parser.add_argument("--adam_epsilon"     ,type=lowercase     ,default=1E-08     ,help="Epsilon for Adam optimizer.")
             train_parser.set_defaults(func=lowercase)



       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		TextClassificationPipeline.from_pretrained(args.model)
             elif args.task == "token_classification":
                   raise NotImplementedError
             elif args.task == "question_answering":
                   raise NotImplementedError

             self.logger.info(F"""Loading dataset from {args.train_data}""")
             UpperCAmelCase_  :	str				      =		Processor.create_from_csv(
                 args.train_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)
             UpperCAmelCase_  :	str				      =		None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                   UpperCAmelCase_  :	Union[str, Any]				      =		Processor.create_from_csv(
                       args.validation_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





             if self.framework == "tf":
                   return self.run_tf()
             return self.run_torch()



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """simple docstring"""





             self.pipeline.fit(
                 self.train_dataset     ,validation_data=self.valid_dataset     ,validation_split=self.validation_split     ,learning_rate=self.learning_rate     ,adam_epsilon=self.adam_epsilon     ,train_batch_size=self.train_batch_size     ,valid_batch_size=self.valid_batch_size     ,)

             # Save trained pipeline
             self.pipeline.save_pretrained(self.output)
