




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__ :   Dict					=							get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
   from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCamelCase__ :   int					=							256_047
lowerCamelCase__ :   List[str]					=							256_145





@require_sentencepiece
@require_tokenizers
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    NllbTokenizer
 lowercase_				    =    NllbTokenizerFast
 lowercase_				    =    True
 lowercase_				    =    True
 lowercase_				    =    {}



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       super().setUp()

       # We have a SentencePiece fixture for testing
       SCREAMING_SNAKE_CASE_							=			NllbTokenizer(_lowerCAmelCase				,				keep_accents=_lowerCAmelCase						)
       tokenizer.save_pretrained(self.tmpdirname						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			NllbTokenizer(_lowerCAmelCase				,				keep_accents=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			tokenizer.tokenize('This is a test'						)
       self.assertListEqual(_lowerCAmelCase				,				['▁This', '▁is', '▁a', '▁t', 'est']						)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_lowerCAmelCase						)				,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,				)

       SCREAMING_SNAKE_CASE_							=			tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
       self.assertListEqual(
           _lowerCAmelCase				,				[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '9',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               'é',
               '.',
           ]				,				)
       SCREAMING_SNAKE_CASE_							=			tokenizer.convert_tokens_to_ids(_lowerCAmelCase						)
       self.assertListEqual(
           _lowerCAmelCase				,				[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]				,				)

       SCREAMING_SNAKE_CASE_							=			tokenizer.convert_ids_to_tokens(_lowerCAmelCase						)
       self.assertListEqual(
           _lowerCAmelCase				,				[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '<unk>',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               '<unk>',
               '.',
           ]				,				)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
                   SCREAMING_SNAKE_CASE_							=			self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase				,				**_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			self.tokenizer_class.from_pretrained(_lowerCAmelCase				,				**_lowerCAmelCase						)

                   SCREAMING_SNAKE_CASE_							=			tempfile.mkdtemp()

                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.save_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_p.save_pretrained(_lowerCAmelCase						)

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files						)						)
                   SCREAMING_SNAKE_CASE_							=			tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f						)
                   self.assertSequenceEqual(_lowerCAmelCase				,				_lowerCAmelCase						)

                   # Checks everything loads correctly in the same way
                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.from_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_p.from_pretrained(_lowerCAmelCase						)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_lowerCAmelCase				,				_lowerCAmelCase						)						)

                   shutil.rmtree(_lowerCAmelCase						)

                   # Save tokenizer rust, legacy_format=True
                   SCREAMING_SNAKE_CASE_							=			tempfile.mkdtemp()

                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.save_pretrained(_lowerCAmelCase				,				legacy_format=_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_p.save_pretrained(_lowerCAmelCase						)

                   # Checks it save with the same files
                   self.assertSequenceEqual(_lowerCAmelCase				,				_lowerCAmelCase						)

                   # Checks everything loads correctly in the same way
                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.from_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_p.from_pretrained(_lowerCAmelCase						)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_lowerCAmelCase				,				_lowerCAmelCase						)						)

                   shutil.rmtree(_lowerCAmelCase						)

                   # Save tokenizer rust, legacy_format=False
                   SCREAMING_SNAKE_CASE_							=			tempfile.mkdtemp()

                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.save_pretrained(_lowerCAmelCase				,				legacy_format=_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_p.save_pretrained(_lowerCAmelCase						)

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files						)						)

                   # Checks everything loads correctly in the same way
                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.from_pretrained(_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_p.from_pretrained(_lowerCAmelCase						)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_lowerCAmelCase				,				_lowerCAmelCase						)						)

                   shutil.rmtree(_lowerCAmelCase						)



 @require_torch
 def       lowerCAmelCase_  (							self  :		Any						):
       if not self.test_seqaseq:
             return

       SCREAMING_SNAKE_CASE_							=			self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}"						):
                   # Longer text that will definitely require truncation.
                   SCREAMING_SNAKE_CASE_							=			[
                       ' UN Chief Says There Is No Military Solution in Syria',
                       ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
                       ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
                       ' will only worsen the violence and misery for millions of people.',
                   ]
                   SCREAMING_SNAKE_CASE_							=			[
                       'Şeful ONU declară că nu există o soluţie militară în Siria',
                       'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
                       ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
                       ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
                   ]
                   try:
                         SCREAMING_SNAKE_CASE_							=			tokenizer.prepare_seqaseq_batch(
                             src_texts=_lowerCAmelCase				,				tgt_texts=_lowerCAmelCase				,				max_length=3				,				max_target_length=10				,				return_tensors='pt'				,				src_lang='eng_Latn'				,				tgt_lang='ron_Latn'				,				)
                   except NotImplementedError:
                         return
                   self.assertEqual(batch.input_ids.shape[1]				,				3						)
                   self.assertEqual(batch.labels.shape[1]				,				10						)
                   # max_target_length will default to max_length if not specified
                   SCREAMING_SNAKE_CASE_							=			tokenizer.prepare_seqaseq_batch(
                       _lowerCAmelCase				,				tgt_texts=_lowerCAmelCase				,				max_length=3				,				return_tensors='pt'						)
                   self.assertEqual(batch.input_ids.shape[1]				,				3						)
                   self.assertEqual(batch.labels.shape[1]				,				3						)

                   SCREAMING_SNAKE_CASE_							=			tokenizer.prepare_seqaseq_batch(
                       src_texts=_lowerCAmelCase				,				max_length=3				,				max_target_length=10				,				return_tensors='pt'						)
                   self.assertEqual(batch_encoder_only.input_ids.shape[1]				,				3						)
                   self.assertEqual(batch_encoder_only.attention_mask.shape[1]				,				3						)
                   self.assertNotIn('decoder_input_ids'				,				_lowerCAmelCase						)



 @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'						)
 def       lowerCAmelCase_  (							self  :		List[str]						):
       pass




 def       lowerCAmelCase_  (							self  :		Tuple						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
                   SCREAMING_SNAKE_CASE_							=			[AddedToken('<special>'				,				lstrip=_lowerCAmelCase						)]

                   SCREAMING_SNAKE_CASE_							=			self.rust_tokenizer_class.from_pretrained(
                       _lowerCAmelCase				,				additional_special_tokens=_lowerCAmelCase				,				**_lowerCAmelCase						)
                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.encode('Hey this is a <special> token'						)

                   SCREAMING_SNAKE_CASE_							=			tokenizer_r.encode('<special>'				,				add_special_tokens=_lowerCAmelCase						)[0]

                   self.assertTrue(special_token_id in r_output						)

                   if self.test_slow_tokenizer:
                         SCREAMING_SNAKE_CASE_							=			self.rust_tokenizer_class.from_pretrained(
                             _lowerCAmelCase				,				additional_special_tokens=_lowerCAmelCase				,				**_lowerCAmelCase				,				)
                         SCREAMING_SNAKE_CASE_							=			self.tokenizer_class.from_pretrained(
                             _lowerCAmelCase				,				additional_special_tokens=_lowerCAmelCase				,				**_lowerCAmelCase						)

                         SCREAMING_SNAKE_CASE_							=			tokenizer_p.encode('Hey this is a <special> token'						)

                         SCREAMING_SNAKE_CASE_							=			tokenizer_cr.encode('Hey this is a <special> token'						)

                         self.assertEqual(_lowerCAmelCase				,				_lowerCAmelCase						)
                         self.assertEqual(_lowerCAmelCase				,				_lowerCAmelCase						)
                         self.assertTrue(special_token_id in p_output						)
                         self.assertTrue(special_token_id in cr_output						)





@require_torch
@require_sentencepiece
@require_tokenizers
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    "facebook/nllb-200-distilled-600M"
 lowercase_				    =    [
     " UN Chief Says There Is No Military Solution in Syria",
     " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
 ]
 lowercase_				    =    [
     "Şeful ONU declară că nu există o soluţie militară în Siria",
     "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
     " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
     " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
 ]
 lowercase_				    =    [
     256_047,
     16_297,
     134_408,
     8_165,
     248_066,
     14_734,
     950,
     1_135,
     105_721,
     3_573,
     83,
     27_352,
     108,
     49_486,
     2,
 ]



 @classmethod
 def       lowerCAmelCase_  (							cls  :		Any						):
       SCREAMING_SNAKE_CASE_							=			NllbTokenizer.from_pretrained(
           cls.checkpoint_name				,				src_lang='eng_Latn'				,				tgt_lang='ron_Latn'						)
       SCREAMING_SNAKE_CASE_							=			1
       return cls



 def       lowerCAmelCase_  (							self  :		str						):
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']				,				256_001						)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']				,				256_002						)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']				,				256_057						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
       self.assertListEqual(self.expected_src_tokens				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       self.assertIn(_lowerCAmelCase				,				self.tokenizer.all_special_ids						)
       # fmt: off
       SCREAMING_SNAKE_CASE_							=			[RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
       # fmt: on

       SCREAMING_SNAKE_CASE_							=			self.tokenizer.decode(_lowerCAmelCase				,				skip_special_tokens=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			self.tokenizer.decode(generated_ids[1:]				,				skip_special_tokens=_lowerCAmelCase						)
       self.assertEqual(_lowerCAmelCase				,				_lowerCAmelCase						)
       self.assertNotIn(self.tokenizer.eos_token				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			['this is gunna be a long sentence ' * 20]
       assert isinstance(src_text[0]				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			10
       SCREAMING_SNAKE_CASE_							=			self.tokenizer(_lowerCAmelCase				,				max_length=_lowerCAmelCase				,				truncation=_lowerCAmelCase						).input_ids[0]
       self.assertEqual(ids[-1]				,				2						)
       self.assertEqual(ids[0]				,				_lowerCAmelCase						)
       self.assertEqual(len(_lowerCAmelCase						)				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)				,				[256_203, 3]						)



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			tempfile.mkdtemp()
       SCREAMING_SNAKE_CASE_							=			self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			NllbTokenizer.from_pretrained(_lowerCAmelCase						)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,				_lowerCAmelCase						)



 @require_torch
 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.tokenizer(
           self.src_text				,				text_target=self.tgt_text				,				padding=_lowerCAmelCase				,				truncation=_lowerCAmelCase				,				max_length=len(self.expected_src_tokens						)				,				return_tensors='pt'				,				)
       SCREAMING_SNAKE_CASE_							=			shift_tokens_right(
           batch['labels']				,				self.tokenizer.pad_token_id				,				self.tokenizer.lang_code_to_id['ron_Latn']						)

       self.assertIsInstance(_lowerCAmelCase				,				_lowerCAmelCase						)

       self.assertEqual((2, 15)				,				batch.input_ids.shape						)
       self.assertEqual((2, 15)				,				batch.attention_mask.shape						)
       SCREAMING_SNAKE_CASE_							=			batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens				,				_lowerCAmelCase						)
       self.assertEqual(_lowerCAmelCase				,				batch.decoder_input_ids[0, 0]						)  # EOS
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens				,				[EN_CODE]						)
       self.assertEqual(self.tokenizer.suffix_tokens				,				[self.tokenizer.eos_token_id]						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.tokenizer(self.src_text				,				padding=_lowerCAmelCase				,				truncation=_lowerCAmelCase				,				max_length=3				,				return_tensors='pt'						)
       SCREAMING_SNAKE_CASE_							=			self.tokenizer(
           text_target=self.tgt_text				,				padding=_lowerCAmelCase				,				truncation=_lowerCAmelCase				,				max_length=10				,				return_tensors='pt'						)
       SCREAMING_SNAKE_CASE_							=			targets['input_ids']
       SCREAMING_SNAKE_CASE_							=			shift_tokens_right(
           _lowerCAmelCase				,				self.tokenizer.pad_token_id				,				decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]				,				)

       self.assertEqual(batch.input_ids.shape[1]				,				3						)
       self.assertEqual(batch.decoder_input_ids.shape[1]				,				10						)



 @require_torch
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.tokenizer._build_translation_inputs(
           'A test'				,				return_tensors='pt'				,				src_lang='eng_Latn'				,				tgt_lang='fra_Latn'						)

       self.assertEqual(
           nested_simplify(_lowerCAmelCase						)				,				{
               # A, test, EOS, en_XX
               'input_ids': [[256_047, 70, 7_356, 2]],
               'attention_mask': [[1, 1, 1, 1]],
               # ar_AR
               'forced_bos_token_id': 256_057,
           }				,				)




 @require_torch
 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			self.tokenizer(
           'UN Chief says there is no military solution in Syria'				,				src_lang='eng_Latn'				,				tgt_lang='fra_Latn'						)
       self.assertEqual(
           inputs.input_ids				,				[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]						)

       SCREAMING_SNAKE_CASE_							=			False
       SCREAMING_SNAKE_CASE_							=			self.tokenizer(
           'UN Chief says there is no military solution in Syria'				,				src_lang='eng_Latn'				,				tgt_lang='fra_Latn'						)
       self.assertEqual(
           inputs.input_ids				,				[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]						)





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

import numpy as np

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	bytes			,			__UpperCAmelCase						:	int							)					-> np.array:
      SCREAMING_SNAKE_CASE_							=			f"{sampling_rate}"
      SCREAMING_SNAKE_CASE_							=			'1'
      SCREAMING_SNAKE_CASE_							=			'f32le'
      SCREAMING_SNAKE_CASE_							=			[
          '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:
                  SCREAMING_SNAKE_CASE_							=			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
      SCREAMING_SNAKE_CASE_							=			output_stream[0]
      SCREAMING_SNAKE_CASE_							=			np.frombuffer(__UpperCAmelCase			,			np.floataa							)
      if audio.shape[0] == 0:
            raise ValueError('Malformed soundfile'							)
      return audio

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	float			,			__UpperCAmelCase						:	str = "f32le"			,			)					-> int:
      SCREAMING_SNAKE_CASE_							=			f"{sampling_rate}"
      SCREAMING_SNAKE_CASE_							=			'1'
      if format_for_conversion == "s16le":
            SCREAMING_SNAKE_CASE_							=			2
      elif format_for_conversion == "f32le":
            SCREAMING_SNAKE_CASE_							=			4
      else:
            raise ValueError(f"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"							)

      SCREAMING_SNAKE_CASE_							=			platform.system()
      if system == "Linux":
            SCREAMING_SNAKE_CASE_							=			'alsa'
            SCREAMING_SNAKE_CASE_							=			'default'
      elif system == "Darwin":
            SCREAMING_SNAKE_CASE_							=			'avfoundation'
            SCREAMING_SNAKE_CASE_							=			':0'
      elif system == "Windows":
            SCREAMING_SNAKE_CASE_							=			'dshow'
            SCREAMING_SNAKE_CASE_							=			'default'

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	float			,			__UpperCAmelCase						:	Optional[int] = None			,			__UpperCAmelCase						:	Optional[Union[Tuple[float, float], float]] = None			,			__UpperCAmelCase						:	str = "f32le"			,			)					-> Tuple:
      if stream_chunk_s is not None:
            SCREAMING_SNAKE_CASE_							=			stream_chunk_s
      else:
            SCREAMING_SNAKE_CASE_							=			chunk_length_s

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

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

      SCREAMING_SNAKE_CASE_							=			int(round(sampling_rate * stride_length_s[0]							)							) * size_of_sample
      SCREAMING_SNAKE_CASE_							=			int(round(sampling_rate * stride_length_s[1]							)							) * size_of_sample
      SCREAMING_SNAKE_CASE_							=			datetime.datetime.now()
      SCREAMING_SNAKE_CASE_							=			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
            SCREAMING_SNAKE_CASE_							=			np.frombuffer(item['raw']			,			dtype=__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_							=			(
                item['stride'][0] // size_of_sample,
                item['stride'][1] // size_of_sample,
            )
            SCREAMING_SNAKE_CASE_							=			sampling_rate
            audio_time += delta
            if datetime.datetime.now() > audio_time + 10 * delta:
                  # We're late !! SKIP
                  continue
            yield item

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





def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	int							)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			2**24  # 16Mo
      try:
            with subprocess.Popen(__UpperCAmelCase			,			stdout=subprocess.PIPE			,			bufsize=__UpperCAmelCase							) as ffmpeg_process:
                  while True:
                        SCREAMING_SNAKE_CASE_							=			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