



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertConfig,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
      )



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			parent
    UpperCAmelCase			:      str			=			13
    UpperCAmelCase			:      int			=			7
    UpperCAmelCase			:      int			=			True
    UpperCAmelCase			:      Any			=			True
    UpperCAmelCase			:      Optional[Any]			=			True
    UpperCAmelCase			:      List[Any]			=			True
    UpperCAmelCase			:      List[str]			=			True
    UpperCAmelCase			:      List[Any]			=			False
    UpperCAmelCase			:      str			=			False
    UpperCAmelCase			:      Optional[Any]			=			False
    UpperCAmelCase			:      Optional[int]			=			2
    UpperCAmelCase			:      List[str]			=			99
    UpperCAmelCase			:      Optional[int]			=			0
    UpperCAmelCase			:      int			=			32
    UpperCAmelCase			:      Union[str, Any]			=			2
    UpperCAmelCase			:      Union[str, Any]			=			4
    UpperCAmelCase			:      Any			=			0.1
    UpperCAmelCase			:      Dict			=			0.1
    UpperCAmelCase			:      str			=			512
    UpperCAmelCase			:      str			=			16
    UpperCAmelCase			:      Optional[Any]			=			2
    UpperCAmelCase			:      Any			=			0.02
    UpperCAmelCase			:      List[str]			=			3
    UpperCAmelCase			:      str			=			4
    UpperCAmelCase			:      Optional[int]			=			"""last"""
    UpperCAmelCase			:      Tuple			=			True
    UpperCAmelCase			:      Optional[int]			=			None
    UpperCAmelCase			:      Any			=			0

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size		)
    UpperCAmelCase			:      List[Any]			=			random_attention_mask([self.batch_size, self.seq_length]   ,  dtype=tf.floataa		)

    UpperCAmelCase			:      Union[str, Any]			=			None
    if self.use_input_lengths:
     UpperCAmelCase			:      Optional[Any]			=			(
         ids_tensor([self.batch_size]   ,  vocab_size=2		) + self.seq_length - 2
     )  # small variation of seq_length

    UpperCAmelCase			:      Any			=			None
    if self.use_token_type_ids:
     UpperCAmelCase			:      Any			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.n_langs		)

    UpperCAmelCase			:      Union[str, Any]			=			None
    UpperCAmelCase			:      Union[str, Any]			=			None
    UpperCAmelCase			:      Dict			=			None
    if self.use_labels:
     UpperCAmelCase			:      str			=			ids_tensor([self.batch_size]   ,  self.type_sequence_label_size		)
     UpperCAmelCase			:      int			=			ids_tensor([self.batch_size, self.seq_length]   ,  self.num_labels		)
     UpperCAmelCase			:      List[str]			=			ids_tensor([self.batch_size]   ,  2   ,  dtype=tf.floataa		)
     UpperCAmelCase			:      Any			=			ids_tensor([self.batch_size]   ,  self.num_choices		)

    UpperCAmelCase			:      Union[str, Any]			=			FlaubertConfig(
        vocab_size=self.vocab_size   ,  n_special=self.n_special   ,  emb_dim=self.hidden_size   ,  n_layers=self.num_hidden_layers   ,  n_heads=self.num_attention_heads   ,  dropout=self.hidden_dropout_prob   ,  attention_dropout=self.attention_probs_dropout_prob   ,  gelu_activation=self.gelu_activation   ,  sinusoidal_embeddings=self.sinusoidal_embeddings   ,  asm=self.asm   ,  causal=self.causal   ,  n_langs=self.n_langs   ,  max_position_embeddings=self.max_position_embeddings   ,  initializer_range=self.initializer_range   ,  summary_type=self.summary_type   ,  use_proj=self.use_proj   ,  bos_token_id=self.bos_token_id   ,  )

    return (
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    )

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			TFFlaubertModel(config=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
    UpperCAmelCase			:      Optional[int]			=			model(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[Any]			=			[input_ids, input_mask]
    UpperCAmelCase			:      Dict			=			model(_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			TFFlaubertWithLMHeadModel(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[Any]			=			{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
    UpperCAmelCase			:      Dict			=			model(_SCREAMING_SNAKE_CASE		)

    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      List[str]			=			TFFlaubertForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Dict			=			{"""input_ids""": input_ids, """lengths""": input_lengths}

    UpperCAmelCase			:      Any			=			model(_SCREAMING_SNAKE_CASE		)

    self.parent.assertEqual(result.start_logits.shape   ,  (self.batch_size, self.seq_length)		)
    self.parent.assertEqual(result.end_logits.shape   ,  (self.batch_size, self.seq_length)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			TFFlaubertForSequenceClassification(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      int			=			{"""input_ids""": input_ids, """lengths""": input_lengths}

    UpperCAmelCase			:      Optional[int]			=			model(_SCREAMING_SNAKE_CASE		)

    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.num_labels
    UpperCAmelCase			:      Tuple			=			TFFlaubertForTokenClassification(config=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      int			=			{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    UpperCAmelCase			:      Optional[Any]			=			model(_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.num_labels)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  )						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.num_choices
    UpperCAmelCase			:      Optional[Any]			=			TFFlaubertForMultipleChoice(config=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE   ,  1		)   ,  (1, self.num_choices, 1)		)
    UpperCAmelCase			:      List[Any]			=			tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE   ,  1		)   ,  (1, self.num_choices, 1)		)
    UpperCAmelCase			:      Union[str, Any]			=			tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE   ,  1		)   ,  (1, self.num_choices, 1)		)
    UpperCAmelCase			:      Optional[int]			=			{
        """input_ids""": multiple_choice_inputs_ids,
        """attention_mask""": multiple_choice_input_mask,
        """token_type_ids""": multiple_choice_token_type_ids,
    }
    UpperCAmelCase			:      Optional[int]			=			model(_SCREAMING_SNAKE_CASE		)
    self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_choices)		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.prepare_config_and_inputs()
    (
        (
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	(
        UpperCAmelCase
    )			,	
    )			:      Tuple			=			config_and_inputs
    UpperCAmelCase			:      str			=			{
        """input_ids""": input_ids,
        """token_type_ids""": token_type_ids,
        """langs""": token_type_ids,
        """lengths""": input_lengths,
    }
    return config, inputs_dict



@require_tf
class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Any		=		(
       (
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
           TFFlaubertForSequenceClassification,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForTokenClassification,
           TFFlaubertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase						:     Tuple		=		(
       (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   __lowerCAmelCase						:     Union[str, Any]		=		(
       {
           'feature-extraction': TFFlaubertModel,
           'fill-mask': TFFlaubertWithLMHeadModel,
           'question-answering': TFFlaubertForQuestionAnsweringSimple,
           'text-classification': TFFlaubertForSequenceClassification,
           'token-classification': TFFlaubertForTokenClassification,
           'zero-shot': TFFlaubertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __lowerCAmelCase						:     str		=		False
   __lowerCAmelCase						:     Dict		=		False

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    if (
        pipeline_test_casse_name == "QAPipelineTests"
        and tokenizer_name is not None
        and not tokenizer_name.endswith("""Fast"""		)
    ):
     # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
     # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
     # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
     return True

    return False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			TFFlaubertModelTester(self		)
    UpperCAmelCase			:      int			=			ConfigTester(self   ,  config_class=_SCREAMING_SNAKE_CASE   ,  emb_dim=37		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   str:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_model(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_lm_head(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      Tuple			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_qa(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_sequence_classif(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_for_token_classification(*_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_for_multiple_choice(*_SCREAMING_SNAKE_CASE		)




   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase			:      Union[str, Any]			=			TFFlaubertModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)



@require_tf
@require_sentencepiece
@require_tokenizers
class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   int:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""		)

    UpperCAmelCase			:      Optional[int]			=			tf.convert_to_tensor(
        [[0, 158, 735, 2592, 1424, 6727, 82, 1]]   ,  dtype=tf.intaa   ,  )  # "J'aime flaubert !"

    UpperCAmelCase			:      List[str]			=			model(_SCREAMING_SNAKE_CASE		)[0]
    UpperCAmelCase			:      str			=			tf.TensorShape((1, 8, 512)		)
    self.assertEqual(output.shape   ,  _SCREAMING_SNAKE_CASE		)
    # compare the actual values for a slice.
    UpperCAmelCase			:      int			=			tf.convert_to_tensor(
        [
            [
                [-1.876_8773, -1.56_6555, 0.2707_2418],
                [-1.692_0038, -0.587_3505, 1.932_9599],
                [-2.956_3985, -1.699_3835, 1.797_2052],
            ]
        ]   ,  dtype=tf.floataa   ,  )

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,  expected_slice.numpy()   ,  atol=1E-4		)		)





"""simple docstring"""
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def    _snake_case		(		UpperCamelCase		:	bytes    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      Optional[int]			=			"""1"""
 UpperCAmelCase			:      Dict			=			"""f32le"""
 UpperCAmelCase			:      Optional[int]			=			[
     """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:
   UpperCAmelCase			:      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
 UpperCAmelCase			:      Tuple			=			output_stream[0]
 UpperCAmelCase			:      Union[str, Any]			=			np.frombuffer(UpperCamelCase    ,	np.floataa		)
 if audio.shape[0] == 0:
  raise ValueError("""Malformed soundfile"""		)
 return audio

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	str = "f32le"    ,	):
 UpperCAmelCase			:      Tuple			=			F"{sampling_rate}"
 UpperCAmelCase			:      List[str]			=			"""1"""
 if format_for_conversion == "s16le":
  UpperCAmelCase			:      int			=			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			:      Tuple			=			"""alsa"""
  UpperCAmelCase			:      Any			=			"""default"""
 elif system == "Darwin":
  UpperCAmelCase			:      Any			=			"""avfoundation"""
  UpperCAmelCase			:      int			=			""":0"""
 elif system == "Windows":
  UpperCAmelCase			:      Optional[int]			=			"""dshow"""
  UpperCAmelCase			:      str			=			"""default"""

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	float    ,	UpperCamelCase		:	Optional[int] = None    ,	UpperCamelCase		:	Optional[Union[Tuple[float, float], float]] = None    ,	UpperCamelCase		:	str = "f32le"    ,	):
 if stream_chunk_s is not None:
  UpperCAmelCase			:      List[Any]			=			stream_chunk_s
 else:
  UpperCAmelCase			:      List[Any]			=			chunk_length_s

 UpperCAmelCase			:      Union[str, Any]			=			ffmpeg_microphone(UpperCamelCase    ,	UpperCamelCase    ,	format_for_conversion=UpperCamelCase		)
 if format_for_conversion == "s16le":
  UpperCAmelCase			:      Dict			=			np.intaa
  UpperCAmelCase			:      Optional[Any]			=			2
 elif format_for_conversion == "f32le":
  UpperCAmelCase			:      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			:      Tuple			=			chunk_length_s / 6
 UpperCAmelCase			:      Optional[Any]			=			int(round(sampling_rate * chunk_length_s		)		) * size_of_sample
 if isinstance(UpperCamelCase    ,	(int, float)		):
  UpperCAmelCase			:      str			=			[stride_length_s, stride_length_s]

 UpperCAmelCase			:      List[Any]			=			int(round(sampling_rate * stride_length_s[0]		)		) * size_of_sample
 UpperCAmelCase			:      Optional[int]			=			int(round(sampling_rate * stride_length_s[1]		)		) * size_of_sample
 UpperCAmelCase			:      List[Any]			=			datetime.datetime.now()
 UpperCAmelCase			:      Tuple			=			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
  UpperCAmelCase			:      Dict			=			np.frombuffer(item["""raw"""]    ,	dtype=UpperCamelCase		)
  UpperCAmelCase			:      List[str]			=			(
      item["""stride"""][0] // size_of_sample,
      item["""stride"""][1] // size_of_sample,
  )
  UpperCAmelCase			:      Optional[Any]			=			sampling_rate
  audio_time += delta
  if datetime.datetime.now() > audio_time + 10 * delta:
   # We're late !! SKIP
   continue
  yield item

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

def    _snake_case		(		UpperCamelCase		:	int    ,	UpperCamelCase		:	int		):
 UpperCAmelCase			:      List[Any]			=			2**24  # 16Mo
 try:
  with subprocess.Popen(UpperCamelCase    ,	stdout=subprocess.PIPE    ,	bufsize=UpperCamelCase		) as ffmpeg_process:
   while True:
    UpperCAmelCase			:      Optional[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
