





"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]			):







      '''simple docstring'''




      for attribute in key.split("""."""			):
            lowerCAmelCase  						=       getattr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      if weight_type is not None:
            lowerCAmelCase  						=       getattr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			).shape
      else:
            lowerCAmelCase  						=       hf_pointer.shape

      assert hf_shape == value.shape, (
          F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
          F' {value.shape} for {full_name}'
      )

      if weight_type == "weight":
            lowerCAmelCase  						=       value
      elif weight_type == "weight_g":
            lowerCAmelCase  						=       value
      elif weight_type == "weight_v":
            lowerCAmelCase  						=       value
      elif weight_type == "bias":
            lowerCAmelCase  						=       value
      else:
            lowerCAmelCase  						=       value

      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      lowerCAmelCase  						=       []
      lowerCAmelCase  						=       fairseq_model.state_dict()

      lowerCAmelCase  						=       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCAmelCase  						=       False
            if "conv_layers" in name:
                  load_conv_layer(
                      SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						hf_model.config.feat_extract_norm == """group"""							,						)
                  lowerCAmelCase  						=       True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCAmelCase  						=       """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

                        if key in name or key.split("""w2v_model."""			)[-1] == name.split("""."""			)[0]:
                              lowerCAmelCase  						=       True
                              if "*" in mapped_key:
                                    lowerCAmelCase  						=       name.split(SCREAMING_SNAKE_CASE			)[0].split("""."""			)[-2]
                                    lowerCAmelCase  						=       mapped_key.replace("""*"""							,						SCREAMING_SNAKE_CASE			)
                              if "weight_g" in name:
                                    lowerCAmelCase  						=       """weight_g"""
                              elif "weight_v" in name:
                                    lowerCAmelCase  						=       """weight_v"""
                              elif "weight" in name:
                                    lowerCAmelCase  						=       """weight"""
                              elif "bias" in name:
                                    lowerCAmelCase  						=       """bias"""
                              else:
                                    lowerCAmelCase  						=       None
                              set_recursively(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
                        continue
            if not is_used:
                  unused_weights.append(SCREAMING_SNAKE_CASE			)

      logger.warning(F'Unused weights: {unused_weights}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]							,						SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       full_name.split("""conv_layers."""			)[-1]
      lowerCAmelCase  						=       name.split("""."""			)
      lowerCAmelCase  						=       int(items[0]			)
      lowerCAmelCase  						=       int(items[1]			)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F'{full_name} has size {value.shape}, but'
                      F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
                  )
                  lowerCAmelCase  						=       value
                  logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F'{full_name} has size {value.shape}, but'
                      F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
                  )
                  lowerCAmelCase  						=       value
                  logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                      " found."
                  )
                  lowerCAmelCase  						=       value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F'{full_name} has size {value.shape}, but'
                      F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
                  )
                  lowerCAmelCase  						=       value
                  logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
      else:
            unused_weights.append(SCREAMING_SNAKE_CASE			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       SEWConfig()
      if is_finetuned:
            lowerCAmelCase  						=       model.wav_encoder.wav_model.cfg
      else:
            lowerCAmelCase  						=       model.cfg

      lowerCAmelCase  						=       fs_config.conv_bias
      lowerCAmelCase  						=       eval(fs_config.conv_feature_layers			)
      lowerCAmelCase  						=       [x[0] for x in conv_layers]
      lowerCAmelCase  						=       [x[1] for x in conv_layers]
      lowerCAmelCase  						=       [x[2] for x in conv_layers]
      lowerCAmelCase  						=       """gelu"""
      lowerCAmelCase  						=       """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      lowerCAmelCase  						=       0.0
      lowerCAmelCase  						=       fs_config.activation_fn.name
      lowerCAmelCase  						=       fs_config.encoder_embed_dim
      lowerCAmelCase  						=       0.02
      lowerCAmelCase  						=       fs_config.encoder_ffn_embed_dim
      lowerCAmelCase  						=       1e-5
      lowerCAmelCase  						=       fs_config.encoder_layerdrop
      lowerCAmelCase  						=       fs_config.encoder_attention_heads
      lowerCAmelCase  						=       fs_config.conv_pos_groups
      lowerCAmelCase  						=       fs_config.conv_pos
      lowerCAmelCase  						=       len(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       fs_config.encoder_layers
      lowerCAmelCase  						=       fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            lowerCAmelCase  						=       model.cfg
            lowerCAmelCase  						=       fs_config.final_dropout
            lowerCAmelCase  						=       fs_config.layerdrop
      lowerCAmelCase  						=       fs_config.activation_dropout
      lowerCAmelCase  						=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      lowerCAmelCase  						=       fs_config.attention_dropout
      lowerCAmelCase  						=       fs_config.dropout_input
      lowerCAmelCase  						=       fs_config.dropout
      lowerCAmelCase  						=       fs_config.mask_channel_length
      lowerCAmelCase  						=       fs_config.mask_channel_prob
      lowerCAmelCase  						=       fs_config.mask_length
      lowerCAmelCase  						=       fs_config.mask_prob

      lowerCAmelCase  						=       """Wav2Vec2FeatureExtractor"""
      lowerCAmelCase  						=       """Wav2Vec2CTCTokenizer"""

      return config


@torch.no_grad()
def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any=None							,						SCREAMING_SNAKE_CASE     :						Any=None							,						SCREAMING_SNAKE_CASE     :						int=True			):







      '''simple docstring'''




      if is_finetuned:
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			)}			)
      else:
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

      if config_path is not None:
            lowerCAmelCase  						=       SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE			)
      else:
            lowerCAmelCase  						=       convert_config(model[0]							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       model[0].eval()

      lowerCAmelCase  						=       True if config.feat_extract_norm == """layer""" else False
      lowerCAmelCase  						=       WavaVecaFeatureExtractor(
          feature_size=1							,						sampling_rate=1_60_00							,						padding_value=0							,						do_normalize=SCREAMING_SNAKE_CASE							,						return_attention_mask=SCREAMING_SNAKE_CASE							,						)

      if is_finetuned:
            if dict_path:
                  lowerCAmelCase  						=       Dictionary.load(SCREAMING_SNAKE_CASE			)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCAmelCase  						=       target_dict.pad_index
                  lowerCAmelCase  						=       target_dict.bos_index
                  lowerCAmelCase  						=       target_dict.pad_index
                  lowerCAmelCase  						=       target_dict.bos_index
                  lowerCAmelCase  						=       target_dict.eos_index
                  lowerCAmelCase  						=       len(target_dict.symbols			)
                  lowerCAmelCase  						=       os.path.join(SCREAMING_SNAKE_CASE							,						"""vocab.json"""			)
                  if not os.path.isdir(SCREAMING_SNAKE_CASE			):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE			)			)
                        return
                  os.makedirs(SCREAMING_SNAKE_CASE							,						exist_ok=SCREAMING_SNAKE_CASE			)
                  with open(SCREAMING_SNAKE_CASE							,						"""w"""							,						encoding="""utf-8"""			) as vocab_handle:
                        json.dump(target_dict.indices							,						SCREAMING_SNAKE_CASE			)
                  lowerCAmelCase  						=       WavaVecaCTCTokenizer(
                      SCREAMING_SNAKE_CASE							,						unk_token=target_dict.unk_word							,						pad_token=target_dict.pad_word							,						bos_token=target_dict.bos_word							,						eos_token=target_dict.eos_word							,						word_delimiter_token="""|"""							,						do_lower_case=SCREAMING_SNAKE_CASE							,						)
                  lowerCAmelCase  						=       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE							,						tokenizer=SCREAMING_SNAKE_CASE			)
                  processor.save_pretrained(SCREAMING_SNAKE_CASE			)

            lowerCAmelCase  						=       SEWForCTC(SCREAMING_SNAKE_CASE			)
      else:
            lowerCAmelCase  						=       SEWModel(SCREAMING_SNAKE_CASE			)
            feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE			)

      recursively_load_weights(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      hf_model.save_pretrained(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
       parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )













"""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


SCREAMING_SNAKE_CASE__					=   "▁"

SCREAMING_SNAKE_CASE__					=   get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class 		lowercase					(					_UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizer
      _SCREAMING_SNAKE_CASE					=      BigBirdTokenizerFast
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      True





      def     _snake_case			(       self )		->     List[str]:
            super().setUp()

            lowerCAmelCase  						=       self.tokenizer_class(lowercase      ,						keep_accents=lowercase )
            tokenizer.save_pretrained(self.tmpdirname )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       """<s>"""
            lowerCAmelCase  						=       1

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





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       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 )      ,						1_004 )





      def     _snake_case			(       self )		->     Union[str, Any]:
            self.assertEqual(self.get_tokenizer().vocab_size      ,						1_000 )





      def     _snake_case			(       self )		->     List[str]:
            if not self.test_rust_tokenizer:
                  return

            lowerCAmelCase  						=       self.get_tokenizer()
            lowerCAmelCase  						=       self.get_rust_tokenizer()

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

            lowerCAmelCase  						=       tokenizer.tokenize(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.tokenize(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase      ,						add_special_tokens=lowercase )
            self.assertListEqual(lowercase      ,						lowercase )

            lowerCAmelCase  						=       self.get_rust_tokenizer()
            lowerCAmelCase  						=       tokenizer.encode(lowercase )
            lowerCAmelCase  						=       rust_tokenizer.encode(lowercase )
            self.assertListEqual(lowercase      ,						lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       BigBirdTokenizer(lowercase      ,						keep_accents=lowercase )

            lowerCAmelCase  						=       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]      ,						)

            lowerCAmelCase  						=       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""",
                    """.""",
                ]      ,						)
            lowerCAmelCase  						=       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]      ,						)

            lowerCAmelCase  						=       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     _snake_case			(       self )		->     Tuple:
            return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )





      @slow
      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       """Hello World!"""
            lowerCAmelCase  						=       [65, 18_536, 2_260, 101, 66]

            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @slow
      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       (
                """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
            lowerCAmelCase  						=       [65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
            # fmt: on
            self.assertListEqual(lowercase      ,						self.big_tokenizer.encode(lowercase ) )





      @require_torch
      @slow
      def     _snake_case			(       self )		->     Tuple:
            import torch

            from transformers import BigBirdConfig, BigBirdModel

            # Build sequence
            lowerCAmelCase  						=       list(self.big_tokenizer.get_vocab().keys() )[:10]
            lowerCAmelCase  						=       """ """.join(lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.encode_plus(lowercase      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )
            lowerCAmelCase  						=       self.big_tokenizer.batch_encode_plus(
                [sequence + """ """ + sequence]      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowercase )

            lowerCAmelCase  						=       BigBirdConfig(attention_type="""original_full""" )
            lowerCAmelCase  						=       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     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base""" )
            lowerCAmelCase  						=       tokenizer.decode(tokenizer("""Paris is the [MASK].""" ).input_ids )

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





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            # fmt: off
            lowerCAmelCase  						=       {"""input_ids""": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 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, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 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"""      ,						)






