
'''simple docstring'''


import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_ :  Optional[int]     =					logging.get_logger(__name__)

snake_case_ :  Dict     =					{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class 						lowercase__	(  __UpperCAmelCase   ):






   '''simple docstring'''







   _snake_case		   =							'''data2vec-audio'''







   def __init__(			self ,   lowerCamelCase__=3_2 ,   lowerCamelCase__=7_6_8 ,   lowerCamelCase__=1_2 ,   lowerCamelCase__=1_2 ,   lowerCamelCase__=3_0_7_2 ,   lowerCamelCase__="gelu" ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.02 ,   lowerCamelCase__=1e-5 ,   lowerCamelCase__="gelu" ,   lowerCamelCase__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) ,   lowerCamelCase__=(5, 2, 2, 2, 2, 2, 2) ,   lowerCamelCase__=(1_0, 3, 3, 3, 3, 2, 2) ,   lowerCamelCase__=False ,   lowerCamelCase__=1_6 ,   lowerCamelCase__=1_9 ,   lowerCamelCase__=5 ,   lowerCamelCase__=0.05 ,   lowerCamelCase__=1_0 ,   lowerCamelCase__=2 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=1_0 ,   lowerCamelCase__=0 ,   lowerCamelCase__="sum" ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   lowerCamelCase__=2_5_6 ,   lowerCamelCase__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) ,   lowerCamelCase__=(5, 3, 3, 1, 1) ,   lowerCamelCase__=(1, 2, 3, 1, 1) ,   lowerCamelCase__=5_1_2 ,   lowerCamelCase__=0 ,   lowerCamelCase__=1 ,   lowerCamelCase__=2 ,   lowerCamelCase__=False ,   lowerCamelCase__=3 ,   lowerCamelCase__=2 ,   lowerCamelCase__=3 ,   lowerCamelCase__=None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__(**lowerCAmelCase_ ,   pad_token_id=lowerCAmelCase_ ,   bos_token_id=lowerCAmelCase_ ,   eos_token_id=lowerCAmelCase_   )
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  feat_extract_activation
      UpperCamelCase       =  list(lowerCAmelCase_   )
      UpperCamelCase       =  list(lowerCAmelCase_   )
      UpperCamelCase       =  list(lowerCAmelCase_   )
      UpperCamelCase       =  conv_bias
      UpperCamelCase       =  num_conv_pos_embeddings
      UpperCamelCase       =  num_conv_pos_embedding_groups
      UpperCamelCase       =  conv_pos_kernel_size
      UpperCamelCase       =  len(self.conv_dim   )
      UpperCamelCase       =  num_hidden_layers
      UpperCamelCase       =  intermediate_size
      UpperCamelCase       =  hidden_act
      UpperCamelCase       =  num_attention_heads
      UpperCamelCase       =  hidden_dropout
      UpperCamelCase       =  attention_dropout
      UpperCamelCase       =  activation_dropout
      UpperCamelCase       =  feat_proj_dropout
      UpperCamelCase       =  final_dropout
      UpperCamelCase       =  layerdrop
      UpperCamelCase       =  layer_norm_eps
      UpperCamelCase       =  initializer_range
      UpperCamelCase       =  vocab_size
      UpperCamelCase       =  use_weighted_layer_sum

      if (
          (len(self.conv_stride   ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
          or (len(self.conv_dim   ) != self.num_feat_extract_layers)
      ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             f' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'
             f' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'   )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      UpperCamelCase       =  mask_time_prob
      UpperCamelCase       =  mask_time_length
      UpperCamelCase       =  mask_time_min_masks
      UpperCamelCase       =  mask_feature_prob
      UpperCamelCase       =  mask_feature_length
      UpperCamelCase       =  mask_feature_min_masks

      # ctc loss
      UpperCamelCase       =  ctc_loss_reduction
      UpperCamelCase       =  ctc_zero_infinity

      # adapter
      UpperCamelCase       =  add_adapter
      UpperCamelCase       =  adapter_kernel_size
      UpperCamelCase       =  adapter_stride
      UpperCamelCase       =  num_adapter_layers
      UpperCamelCase       =  output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      UpperCamelCase       =  classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      UpperCamelCase       =  list(lowerCAmelCase_   )
      UpperCamelCase       =  list(lowerCAmelCase_   )
      UpperCamelCase       =  list(lowerCAmelCase_   )
      UpperCamelCase       =  xvector_output_dim







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return math.prod(self.conv_stride   )







'''simple docstring'''


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


snake_case_ :  Tuple     =					get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							PegasusTokenizerFast
   _snake_case		   =							True
   _snake_case		   =							True







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase       =  PegasusTokenizer(lowerCamelCase__   )
      tokenizer.save_pretrained(self.tmpdirname   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained('''google/pegasus-large'''   )







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return ("This is a test", "This is a test")







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''</s>'''
      UpperCamelCase       =  1

      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   ):

      '''simple docstring'''







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

      self.assertEqual(vocab_keys[0] ,   '''<pad>'''   )
      self.assertEqual(vocab_keys[1] ,   '''</s>'''   )
      self.assertEqual(vocab_keys[-1] ,   '''v'''   )
      self.assertEqual(len(lowerCamelCase__   ) ,   1_1_0_3   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.assertEqual(self.get_tokenizer().vocab_size ,   1_1_0_3   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  (
          '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
          ''' </s> <pad> <pad> <pad>'''
      )
      UpperCamelCase       =  rust_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      UpperCamelCase       =  py_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      UpperCamelCase       =  '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
      UpperCamelCase       =  [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      UpperCamelCase       =  tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6_1_0_3
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_0_3
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_0_2_4
      UpperCamelCase       =  '''To ensure a smooth flow of bank resolutions.'''
      UpperCamelCase       =  [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      UpperCamelCase       =  tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ['''This is going to be way too long.''' * 1_5_0, '''short example''']
      UpperCamelCase       =  ['''not super long but more than 5 tokens''', '''tiny''']
      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )
      UpperCamelCase       =  self._large_tokenizer(
          text_target=lowerCamelCase__ ,   max_length=5 ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )

      assert batch.input_ids.shape == (2, 1_0_2_4)
      assert batch.attention_mask.shape == (2, 1_0_2_4)
      assert targets["input_ids"].shape == (2, 5)
      assert len(lowerCamelCase__   ) == 2  # input_ids, attention_mask.







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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='''google/bigbird-pegasus-large-arxiv''' ,   revision='''ba85d0851d708441f91440d509690f1ab6353415''' ,   )




@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							PegasusTokenizerFast
   _snake_case		   =							True
   _snake_case		   =							True







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase       =  PegasusTokenizer(lowerCamelCase__ ,   offset=0 ,   mask_token_sent=lowerCamelCase__ ,   mask_token='''[MASK]'''   )
      tokenizer.save_pretrained(self.tmpdirname   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''   )







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return ("This is a test", "This is a test")







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  (
          '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
          ''' <pad> <pad> <pad>'''
      )
      UpperCamelCase       =  rust_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      UpperCamelCase       =  py_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
      UpperCamelCase       =  ['''not super long but more than 5 tokens''', '''tiny''']
      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )
      UpperCamelCase       =  self._large_tokenizer(
          text_target=lowerCamelCase__ ,   max_length=5 ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )

      assert batch.input_ids.shape == (2, 4_0_9_6)
      assert batch.attention_mask.shape == (2, 4_0_9_6)
      assert targets["input_ids"].shape == (2, 5)
      assert len(lowerCamelCase__   ) == 2  # input_ids, attention_mask.







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  (
          '''This is an example string that is used to test the original TF implementation against the HF'''
          ''' implementation'''
      )

      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__   ).input_ids

      self.assertListEqual(
          lowerCamelCase__ ,   [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] ,   )





