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

import numpy as np




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : List[Any]					)    ->  np.array:
  __UpperCAmelCase						    =       F'''{sampling_rate}'''
  __UpperCAmelCase						    =       "1"
  __UpperCAmelCase						    =       "f32le"
  __UpperCAmelCase						    =       [
      "ffmpeg",
      "-i",
      "pipe:0",
      "-ac",
      ac,
      "-ar",
      ar,
      "-f",
      format_for_conversion,
      "-hide_banner",
      "-loglevel",
      "quiet",
      "pipe:1",
  ]

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




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : Dict						,	A_   : List[str] = "f32le"						,	)    ->  Dict:
  __UpperCAmelCase						    =       F'''{sampling_rate}'''
  __UpperCAmelCase						    =       "1"
  if format_for_conversion == "s16le":
    __UpperCAmelCase						    =       2
  elif format_for_conversion == "f32le":
    __UpperCAmelCase						    =       4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

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

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




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : Any						,	A_   : Tuple = None						,	A_   : str = None						,	A_   : str = "f32le"						,	)    ->  Optional[int]:



  if stream_chunk_s is not None:
    __UpperCAmelCase						    =       stream_chunk_s
  else:
    __UpperCAmelCase						    =       chunk_length_s

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

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

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




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





def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : Tuple					)    ->  Optional[int]:
  __UpperCAmelCase						    =       2**24  # 16Mo
  try:
    with subprocess.Popen(A_						,	stdout=subprocess.PIPE						,	bufsize=A_					) as ffmpeg_process:
      while True:
        __UpperCAmelCase						    =       ffmpeg_process.stdout.read(A_					)
        if raw == b"":
          break
        yield raw
  except FileNotFoundError as error:
    raise ValueError("ffmpeg was not found but is required to stream audio files from filename"					) from error





import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_							=      get_tests_dir("""fixtures/spiece.model""")


@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= AlbertTokenizer
  lowerCAmelCase__		:	int    		= AlbertTokenizerFast
  lowerCAmelCase__		:	str    		= True
  lowerCAmelCase__		:	Union[str, Any]    		= True
  lowerCAmelCase__		:	int    		= True






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




    '''simple docstring'''




    super().setUp()

    # We have a SentencePiece fixture for testing
    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase   )
    tokenizer.save_pretrained(self.tmpdirname   )






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "this is a test"
    __UpperCAmelCase						    =       "this is a test"
    return input_text, output_text






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "<pad>"
    __UpperCAmelCase						    =       0

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase   )				,				__lowerCAmelCase   )
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase   )				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       list(self.get_tokenizer().get_vocab().keys()   )

    self.assertEqual(vocab_keys[0]				,				"<pad>"   )
    self.assertEqual(vocab_keys[1]				,				"<unk>"   )
    self.assertEqual(vocab_keys[-1]				,				"▁eloquent"   )
    self.assertEqual(len(__lowerCAmelCase   )				,				30_000   )






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




    '''simple docstring'''




    self.assertEqual(self.get_tokenizer().vocab_size				,				30_000   )






  def 	_UpperCAmelCase				(				self:			str   ) -> int:




    '''simple docstring'''




    if not self.test_rust_tokenizer:
      return

    __UpperCAmelCase						    =       self.get_tokenizer()
    __UpperCAmelCase						    =       self.get_rust_tokenizer()

    __UpperCAmelCase						    =       "I was born in 92000, and this is falsé."

    __UpperCAmelCase						    =       tokenizer.tokenize(__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.tokenize(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       self.get_rust_tokenizer()
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.encode(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase				,				keep_accents=__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.tokenize("This is a test"   )
    self.assertListEqual(__lowerCAmelCase				,				["▁this", "▁is", "▁a", "▁test"]   )

    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )				,				[48, 25, 21, 1_289]   )

    __UpperCAmelCase						    =       tokenizer.tokenize("I was born in 92000, and this is falsé."   )
    self.assertListEqual(
        __lowerCAmelCase				,				["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]   )
    __UpperCAmelCase						    =       tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]   )

    __UpperCAmelCase						    =       tokenizer.convert_ids_to_tokens(__lowerCAmelCase   )
    self.assertListEqual(
        __lowerCAmelCase				,				["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]				,				)






  def 	_UpperCAmelCase				(				self:			str   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.encode("sequence builders"   )
    __UpperCAmelCase						    =       tokenizer.encode("multi-sequence build"   )

    __UpperCAmelCase						    =       tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				,				__lowerCAmelCase   )

    assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
    assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
        tokenizer.sep_token_id
    ]






  @slow
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       {"attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "input_ids": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=__lowerCAmelCase				,				model_name="albert-base-v2"				,				revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"				,				)



