






"""simple docstring"""






import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_   : str   			= """▁"""

UpperCAmelCase_   : List[Any]   			= get_tests_dir("""fixtures/test_sentencepiece.model""")




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






      '''simple docstring'''

      __UpperCamelCase	 =  BigBirdTokenizer
      __UpperCamelCase	 =  BigBirdTokenizerFast
      __UpperCamelCase	 =  True
      __UpperCamelCase	 =  True
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          super().setUp()

          SCREAMING_SNAKE_CASE_      :  Any    						=		self.tokenizer_class(lowercase_						,	keep_accents=lowercase_)
          tokenizer.save_pretrained(self.tmpdirname)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		'''<s>'''
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_)						,	lowercase_)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_)						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]						,	'''<unk>''')
          self.assertEqual(vocab_keys[1]						,	'''<s>''')
          self.assertEqual(vocab_keys[-1]						,	'''[MASK]''')
          self.assertEqual(len(lowercase_)						,	1004)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          self.assertEqual(self.get_tokenizer().vocab_size						,	1000)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          if not self.test_rust_tokenizer:
              return

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.get_tokenizer()
          SCREAMING_SNAKE_CASE_      :  int    						=		self.get_rust_tokenizer()

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''I was born in 92000, and this is falsé.'''

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tokenizer.tokenize(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		rust_tokenizer.tokenize(lowercase_)
          self.assertListEqual(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_)
          self.assertListEqual(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_rust_tokenizer()
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		tokenizer.encode(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		rust_tokenizer.encode(lowercase_)
          self.assertListEqual(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		BigBirdTokenizer(lowercase_						,	keep_accents=lowercase_)

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

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowercase_)						,	[285, 46, 10, 170, 382]						,	)

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
          self.assertListEqual(
              lowercase_						,	[
                  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_      :  Dict    						=		tokenizer.convert_tokens_to_ids(lowercase_)
          self.assertListEqual(
              lowercase_						,	[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,	)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		tokenizer.convert_ids_to_tokens(lowercase_)
          self.assertListEqual(
              lowercase_						,	[
                  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>''',
                  '''.''',
              ]						,	)
      @cached_property
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''Hello World!'''
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		[65, 18536, 2260, 101, 66]

          self.assertListEqual(lowercase_						,	self.big_tokenizer.encode(lowercase_))
      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
              '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
              ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
          )
          # fmt: off
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
          # fmt: on
          self.assertListEqual(lowercase_						,	self.big_tokenizer.encode(lowercase_))
      @require_torch
      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          import torch

          from transformers import BigBirdConfig, BigBirdModel

          # Build sequence
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		list(self.big_tokenizer.get_vocab().keys())[:10]
          SCREAMING_SNAKE_CASE_      :  str    						=		''' '''.join(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		self.big_tokenizer.encode_plus(lowercase_						,	return_tensors='''pt'''						,	return_token_type_ids=lowercase_)
          SCREAMING_SNAKE_CASE_      :  int    						=		self.big_tokenizer.batch_encode_plus(
              [sequence + ''' ''' + sequence]						,	return_tensors='''pt'''						,	return_token_type_ids=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		BigBirdConfig(attention_type='''original_full''')
          SCREAMING_SNAKE_CASE_      :  int    						=		BigBirdModel(lowercase_)

          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
              model(**lowercase_)
              model(**lowercase_)
      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base''')
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		tokenizer.decode(tokenizer('''Paris is the [MASK].''').input_ids)

          self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]''')



      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=lowercase_						,	model_name='''google/bigbird-roberta-base'''						,	revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''						,	)








"""simple docstring"""






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

import numpy as np







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Tuple    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			str    =    '1'
				_lowerCamelCase     :			str    =    'f32le'
				_lowerCamelCase     :			Union[str, Any]    =    [
				    'ffmpeg',
				    '-i',
				    'pipe:0',
				    '-ac',
				    ac,
				    '-ar',
				    ar,
				    '-f',
				    format_for_conversion,
				    '-hide_banner',
				    '-loglevel',
				    'quiet',
				    'pipe:1',
				]

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = "f32le"							,    ):
				_lowerCamelCase     :			Optional[Any]    =    f'''{sampling_rate}'''
				_lowerCamelCase     :			List[str]    =    '1'
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    2
				elif format_for_conversion == "f32le":
								_lowerCamelCase     :			List[Any]    =    4
				else:
								raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

				_lowerCamelCase     :			Dict    =    platform.system()
				if system == "Linux":
								_lowerCamelCase     :			Optional[int]    =    'alsa'
								_lowerCamelCase     :			Optional[Any]    =    'default'
				elif system == "Darwin":
								_lowerCamelCase     :			Optional[int]    =    'avfoundation'
								_lowerCamelCase     :			Any    =    ':0'
				elif system == "Windows":
								_lowerCamelCase     :			Tuple    =    'dshow'
								_lowerCamelCase     :			Tuple    =    'default'

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "f32le"							,    ):
				if stream_chunk_s is not None:
								_lowerCamelCase     :			int    =    stream_chunk_s
				else:
								_lowerCamelCase     :			Optional[Any]    =    chunk_length_s

				_lowerCamelCase     :			Optional[Any]    =    ffmpeg_microphone(lowercase__							,    lowercase__							,    format_for_conversion=lowercase__		)
				if format_for_conversion == "s16le":
								_lowerCamelCase     :			List[str]    =    np.intaa
								_lowerCamelCase     :			str    =    2
				elif format_for_conversion == "f32le":
								_lowerCamelCase     :			Any    =    np.floataa
								_lowerCamelCase     :			List[Any]    =    4
				else:
								raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''		)

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

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







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







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