


'''simple docstring'''

import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY





@is_pipeline_test
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  MODEL_FOR_MASKED_LM_MAPPING
  _lowerCamelCase		    =  TF_MODEL_FOR_MASKED_LM_MAPPING






  def   snake_case		(    self     ):







   """simple docstring"""
   super().tearDown()
   # clean-up as much as possible GPU memory occupied by PyTorch
   gc.collect()
   if is_torch_available():
    import torch

    torch.cuda.empty_cache()






  @require_tf
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(task="fill-mask"			,  model="sshleifer/tiny-distilroberta-base"			,  top_k=2			,  framework="tf"     )
   lowerCamelCase_      =  unmasker("My name is <mask>"     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           {"sequence": "My name is grouped", "score": 2.1e-05, "token": 3_8015, "token_str": " grouped"},
           {"sequence": "My name is accuser", "score": 2.1e-05, "token": 2_5506, "token_str": " accuser"},
       ]			,  )

   lowerCamelCase_      =  unmasker("The largest city in France is <mask>"     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           {
               "sequence": "The largest city in France is grouped",
               "score": 2.1e-05,
               "token": 3_8015,
               "token_str": " grouped",
           },
           {
               "sequence": "The largest city in France is accuser",
               "score": 2.1e-05,
               "token": 2_5506,
               "token_str": " accuser",
           },
       ]			,  )

   lowerCamelCase_      =  unmasker("My name is <mask>"			,  targets=[" Patrick", " Clara", " Teven"]			,  top_k=3     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           {"sequence": "My name is Clara", "score": 2e-05, "token": 1_3606, "token_str": " Clara"},
           {"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
           {"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
       ]			,  )






  @require_torch
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(task="fill-mask"			,  model="sshleifer/tiny-distilroberta-base"			,  top_k=2			,  framework="pt"     )

   lowerCamelCase_      =  unmasker("My name is <mask>"     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           {"sequence": "My name is Maul", "score": 2.2e-05, "token": 3_5676, "token_str": " Maul"},
           {"sequence": "My name isELS", "score": 2.2e-05, "token": 1_6416, "token_str": "ELS"},
       ]			,  )

   lowerCamelCase_      =  unmasker("The largest city in France is <mask>"     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           {
               "sequence": "The largest city in France is Maul",
               "score": 2.2e-05,
               "token": 3_5676,
               "token_str": " Maul",
           },
           {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 1_6416, "token_str": "ELS"},
       ]			,  )

   lowerCamelCase_      =  unmasker("My name is <mask>"			,  targets=[" Patrick", " Clara", " Teven"]			,  top_k=3     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
           {"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
           {"sequence": "My name is Clara", "score": 2e-05, "token": 1_3606, "token_str": " Clara"},
       ]			,  )

   lowerCamelCase_      =  unmasker("My name is <mask> <mask>"			,  top_k=2     )

   self.assertEqual(
       nested_simplify(_lowerCAmelCase			,  decimals=6     )			,  [
           [
               {
                   "score": 2.2e-05,
                   "token": 3_5676,
                   "token_str": " Maul",
                   "sequence": "<s>My name is Maul<mask></s>",
               },
               {"score": 2.2e-05, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
           ],
           [
               {
                   "score": 2.2e-05,
                   "token": 3_5676,
                   "token_str": " Maul",
                   "sequence": "<s>My name is<mask> Maul</s>",
               },
               {"score": 2.2e-05, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
           ],
       ]			,  )






  @require_torch_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline("fill-mask"			,  model="hf-internal-testing/tiny-random-distilbert"			,  device=0			,  framework="pt"     )

   # convert model to fp16
   pipe.model.half()

   lowerCamelCase_      =  pipe("Paris is the [MASK] of France."     )
   # We actually don't care about the result, we just want to make sure
   # it works, meaning the float16 tensor got casted back to float32
   # for postprocessing.
   self.assertIsInstance(_lowerCAmelCase			,  _lowerCAmelCase     )






  @slow
  @require_torch
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(task="fill-mask"			,  model="distilroberta-base"			,  top_k=2			,  framework="pt"     )
   self.run_large_test(_lowerCAmelCase     )






  @slow
  @require_tf
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(task="fill-mask"			,  model="distilroberta-base"			,  top_k=2			,  framework="tf"     )
   self.run_large_test(_lowerCAmelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  unmasker("My name is <mask>"     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase     )			,  [
           {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
           {"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
       ]			,  )
   lowerCamelCase_      =  unmasker("The largest city in France is <mask>"     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase     )			,  [
           {
               "sequence": "The largest city in France is Paris",
               "score": 0.251,
               "token": 2201,
               "token_str": " Paris",
           },
           {
               "sequence": "The largest city in France is Lyon",
               "score": 0.214,
               "token": 1_2790,
               "token_str": " Lyon",
           },
       ]			,  )

   lowerCamelCase_      =  unmasker("My name is <mask>"			,  targets=[" Patrick", " Clara", " Teven"]			,  top_k=3     )
   self.assertEqual(
       nested_simplify(_lowerCAmelCase     )			,  [
           {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
           {"sequence": "My name is Clara", "score": 0.000, "token": 1_3606, "token_str": " Clara"},
           {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
       ]			,  )






  @require_torch
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(task="fill-mask"			,  model="sshleifer/tiny-distilroberta-base"			,  framework="pt"     )
   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   self.run_pipeline_test(_lowerCAmelCase			,  []     )






  @require_tf
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  pipeline(task="fill-mask"			,  model="sshleifer/tiny-distilroberta-base"			,  framework="tf"     )
   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   self.run_pipeline_test(_lowerCAmelCase			,  []     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if tokenizer is None or tokenizer.mask_token_id is None:
    self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"     )

   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase     )
   lowerCamelCase_      =  [
       f'''This is another {tokenizer.mask_token} test''',
   ]
   return fill_masker, examples






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  fill_masker.tokenizer
   lowerCamelCase_      =  fill_masker.model

   lowerCamelCase_      =  fill_masker(
       f'''This is a {tokenizer.mask_token}'''			,  )
   self.assertEqual(
       _lowerCAmelCase			,  [
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
       ]			,  )

   lowerCamelCase_      =  fill_masker([f'''This is a {tokenizer.mask_token}''']     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
       ]			,  )

   lowerCamelCase_      =  fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.''']     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           [
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           ],
           [
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           ],
       ]			,  )

   with self.assertRaises(_lowerCAmelCase     ):
    fill_masker([None]     )
   # No mask_token is not supported
   with self.assertRaises(_lowerCAmelCase     ):
    fill_masker("This is"     )

   self.run_test_top_k(_lowerCAmelCase			,  _lowerCAmelCase     )
   self.run_test_targets(_lowerCAmelCase			,  _lowerCAmelCase     )
   self.run_test_top_k_targets(_lowerCAmelCase			,  _lowerCAmelCase     )
   self.fill_mask_with_duplicate_targets_and_top_k(_lowerCAmelCase			,  _lowerCAmelCase     )
   self.fill_mask_with_multiple_masks(_lowerCAmelCase			,  _lowerCAmelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  tokenizer.get_vocab()
   lowerCamelCase_      =  sorted(vocab.keys()     )[:2]
   # Pipeline argument
   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase			,  targets=_lowerCAmelCase     )
   lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
       ]			,  )
   lowerCamelCase_      =  {vocab[el] for el in targets}
   self.assertEqual({el["token"] for el in outputs}			,  _lowerCAmelCase     )
   lowerCamelCase_      =  [tokenizer.decode([x]     ) for x in target_ids]
   self.assertEqual({el["token_str"] for el in outputs}			,  set(_lowerCAmelCase     )     )

   # Call argument
   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase     )
   lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  targets=_lowerCAmelCase     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
       ]			,  )
   lowerCamelCase_      =  {vocab[el] for el in targets}
   self.assertEqual({el["token"] for el in outputs}			,  _lowerCAmelCase     )
   lowerCamelCase_      =  [tokenizer.decode([x]     ) for x in target_ids]
   self.assertEqual({el["token_str"] for el in outputs}			,  set(_lowerCAmelCase     )     )

   # Score equivalence
   lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  targets=_lowerCAmelCase     )
   lowerCamelCase_      =  [top_mask["token_str"] for top_mask in outputs]
   lowerCamelCase_      =  [top_mask["score"] for top_mask in outputs]

   # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
   if set(_lowerCAmelCase     ) == set(_lowerCAmelCase     ):
    lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  targets=_lowerCAmelCase     )
    lowerCamelCase_      =  [top_mask["score"] for top_mask in unmasked_targets]
    self.assertEqual(nested_simplify(_lowerCAmelCase     )			,  nested_simplify(_lowerCAmelCase     )     )

   # Raises with invalid
   with self.assertRaises(_lowerCAmelCase     ):
    lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  targets=[]     )
   # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
   if "" not in tokenizer.get_vocab():
    with self.assertRaises(_lowerCAmelCase     ):
     lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  targets=[""]     )
    with self.assertRaises(_lowerCAmelCase     ):
     lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  targets=""     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase			,  top_k=2     )
   lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
       ]			,  )

   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase     )
   lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  top_k=2     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
       ]			,  )
   self.assertEqual(nested_simplify(_lowerCAmelCase     )			,  nested_simplify(_lowerCAmelCase     )     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  tokenizer.get_vocab()
   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase     )

   # top_k=2, ntargets=3
   lowerCamelCase_      =  sorted(vocab.keys()     )[:3]
   lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  top_k=2			,  targets=_lowerCAmelCase     )

   # If we use the most probably targets, and filter differently, we should still
   # have the same results
   lowerCamelCase_      =  [el["token_str"] for el in sorted(_lowerCAmelCase			,  key=lambda UpperCamelCase     : x["score"]			,  reverse=_lowerCAmelCase     )]
   # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
   if set(_lowerCAmelCase     ).issubset(_lowerCAmelCase     ):
    lowerCamelCase_      =  fill_masker(f'''This is a {tokenizer.mask_token}'''			,  top_k=3			,  targets=_lowerCAmelCase     )
    # They should yield exactly the same result
    self.assertEqual(nested_simplify(_lowerCAmelCase     )			,  nested_simplify(_lowerCAmelCase     )     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase     )
   lowerCamelCase_      =  tokenizer.get_vocab()
   # String duplicates + id duplicates
   lowerCamelCase_      =  sorted(vocab.keys()     )[:3]
   lowerCamelCase_      =  [targets[0], targets[1], targets[0], targets[2], targets[1]]
   lowerCamelCase_      =  fill_masker(f'''My name is {tokenizer.mask_token}'''			,  targets=_lowerCAmelCase			,  top_k=10     )

   # The target list contains duplicates, so we can't output more
   # than them
   self.assertEqual(len(_lowerCAmelCase     )			,  3     )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  FillMaskPipeline(model=_lowerCAmelCase			,  tokenizer=_lowerCAmelCase     )

   lowerCamelCase_      =  fill_masker(
       f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''			,  top_k=2     )
   self.assertEqual(
       _lowerCAmelCase			,  [
           [
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           ],
           [
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           ],
           [
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
               {"sequence": ANY(_lowerCAmelCase     ), "score": ANY(_lowerCAmelCase     ), "token": ANY(_lowerCAmelCase     ), "token_str": ANY(_lowerCAmelCase     )},
           ],
       ]			,  )









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