


'''simple docstring'''



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      __UpperCAmelCase      :Dict			    =       "platform"
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel



@require_flax
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       PegasusConfig
     SCREAMING_SNAKE_CASE	:			str							       =       {}
     SCREAMING_SNAKE_CASE	:			Any							       =       "gelu"

     def __init__(    self					:		List[Any]							,			snake_case					:		int							,			snake_case					:		int=13							,			snake_case					:		Optional[Any]=7							,			snake_case					:		Union[str, Any]=True							,			snake_case					:		int=False							,			snake_case					:		int=99							,			snake_case					:		Optional[int]=32							,			snake_case					:		Union[str, Any]=5							,			snake_case					:		List[str]=4							,			snake_case					:		Union[str, Any]=37							,			snake_case					:		Union[str, Any]=0.1							,			snake_case					:		str=0.1							,			snake_case					:		Any=20							,			snake_case					:		int=2							,			snake_case					:		Dict=1							,			snake_case					:		Any=0							,			)						->							Optional[Any]:
          __UpperCAmelCase     :			Optional[int]  							=      parent
          __UpperCAmelCase     :			List[Any]  							=      batch_size
          __UpperCAmelCase     :			str  							=      seq_length
          __UpperCAmelCase     :			List[str]  							=      is_training
          __UpperCAmelCase     :			List[Any]  							=      use_labels
          __UpperCAmelCase     :			Dict  							=      vocab_size
          __UpperCAmelCase     :			Union[str, Any]  							=      hidden_size
          __UpperCAmelCase     :			int  							=      num_hidden_layers
          __UpperCAmelCase     :			Dict  							=      num_attention_heads
          __UpperCAmelCase     :			Optional[Any]  							=      intermediate_size

          __UpperCAmelCase     :			int  							=      hidden_dropout_prob
          __UpperCAmelCase     :			List[Any]  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			Optional[int]  							=      max_position_embeddings
          __UpperCAmelCase     :			Optional[Any]  							=      eos_token_id
          __UpperCAmelCase     :			Dict  							=      pad_token_id
          __UpperCAmelCase     :			Dict  							=      bos_token_id

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      ids_tensor([self.batch_size, self.seq_length - 1]							,			self.vocab_size      ).clip(3							,			self.vocab_size      )
          __UpperCAmelCase     :			int  							=      np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )							,			1      )
          __UpperCAmelCase     :			Optional[Any]  							=      np.concatenate([input_ids, eos_tensor]							,			axis=1      )

          __UpperCAmelCase     :			Optional[int]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          __UpperCAmelCase     :			Optional[int]  							=      self.config_cls(
              vocab_size=self.vocab_size							,			d_model=self.hidden_size							,			encoder_layers=self.num_hidden_layers							,			decoder_layers=self.num_hidden_layers							,			encoder_attention_heads=self.num_attention_heads							,			decoder_attention_heads=self.num_attention_heads							,			encoder_ffn_dim=self.intermediate_size							,			decoder_ffn_dim=self.intermediate_size							,			dropout=self.hidden_dropout_prob							,			attention_dropout=self.attention_probs_dropout_prob							,			max_position_embeddings=self.max_position_embeddings							,			eos_token_ids=[2]							,			bos_token_id=self.bos_token_id							,			pad_token_id=self.pad_token_id							,			decoder_start_token_id=self.pad_token_id							,			**self.config_updates							,			)
          __UpperCAmelCase     :			List[str]  							=      prepare_pegasus_inputs_dict(snake_case							,			snake_case							,			snake_case      )
          return config, inputs_dict

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Dict							,			snake_case					:		Tuple							,			snake_case					:		List[Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[int]  							=      20
          __UpperCAmelCase     :			Tuple  							=      model_class_name(snake_case      )

          __UpperCAmelCase     :			Dict  							=      model.encode(inputs_dict['''input_ids''']      )

          __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      (
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          __UpperCAmelCase     :			Any  							=      model.init_cache(decoder_input_ids.shape[0]							,			snake_case							,			snake_case      )
          __UpperCAmelCase     :			Tuple  							=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,			dtype='''i4'''      )

          __UpperCAmelCase     :			List[Any]  							=      jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]							,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,			)
          __UpperCAmelCase     :			Dict  							=      model.decode(
              decoder_input_ids[:, :-1]							,			snake_case							,			decoder_attention_mask=snake_case							,			past_key_values=snake_case							,			decoder_position_ids=snake_case							,			)

          __UpperCAmelCase     :			List[str]  							=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,			dtype='''i4'''      )
          __UpperCAmelCase     :			Dict  							=      model.decode(
              decoder_input_ids[:, -1:]							,			snake_case							,			decoder_attention_mask=snake_case							,			past_key_values=outputs_cache.past_key_values							,			decoder_position_ids=snake_case							,			)

          __UpperCAmelCase     :			Optional[Any]  							=      model.decode(snake_case							,			snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
          self.parent.assertTrue(diff < 1E-3							,			msg=f'Max diff is {diff}'      )






     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		Optional[Any]							,			snake_case					:		List[str]							,			snake_case					:		int      )						->							Any:
          __UpperCAmelCase     :			Tuple  							=      20
          __UpperCAmelCase     :			Optional[int]  							=      model_class_name(snake_case      )

          __UpperCAmelCase     :			int  							=      model.encode(inputs_dict['''input_ids''']      )

          __UpperCAmelCase							,       __UpperCAmelCase     :			List[Any]  							=      (
              inputs_dict['''decoder_input_ids'''],
              inputs_dict['''decoder_attention_mask'''],
          )

          __UpperCAmelCase     :			Optional[int]  							=      jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
              ]							,			axis=-1							,			)

          __UpperCAmelCase     :			int  							=      model.init_cache(decoder_input_ids.shape[0]							,			snake_case							,			snake_case      )
          __UpperCAmelCase     :			List[str]  							=      jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]							,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,			)

          __UpperCAmelCase     :			Dict  							=      model.decode(
              decoder_input_ids[:, :-1]							,			snake_case							,			decoder_attention_mask=snake_case							,			past_key_values=snake_case							,			decoder_position_ids=snake_case							,			)
          __UpperCAmelCase     :			Optional[Any]  							=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,			dtype='''i4'''      )
          __UpperCAmelCase     :			str  							=      model.decode(
              decoder_input_ids[:, -1:]							,			snake_case							,			past_key_values=outputs_cache.past_key_values							,			decoder_attention_mask=snake_case							,			decoder_position_ids=snake_case							,			)

          __UpperCAmelCase     :			Optional[Any]  							=      model.decode(snake_case							,			snake_case							,			decoder_attention_mask=snake_case      )

          __UpperCAmelCase     :			str  							=      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
          self.parent.assertTrue(diff < 1E-3							,			msg=f'Max diff is {diff}'      )




def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Any      , _lowercase :     int      , _lowercase :     List[Any]=None      , _lowercase :     Union[str, Any]=None      , ):





     '''simple docstring'''






     if attention_mask is None:
          __UpperCAmelCase     :			Optional[Any]  							=      np.not_equal(_lowercase      , config.pad_token_id  ).astype(np.inta  )
     if decoder_attention_mask is None:
          __UpperCAmelCase     :			Tuple  							=      np.concatenate(
              [
                  np.ones(decoder_input_ids[:, :1].shape      , dtype=np.inta  ),
                  np.not_equal(decoder_input_ids[:, 1:]      , config.pad_token_id  ).astype(np.inta  ),
              ]      , axis=-1      , )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
     }



@require_flax
class      a    (		_a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       (
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     SCREAMING_SNAKE_CASE	:			Tuple							       =       (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       True
     SCREAMING_SNAKE_CASE	:			Any							       =       False
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Dict							       =       False

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      FlaxPegasusModelTester(self      )
          __UpperCAmelCase     :			Union[str, Any]  							=      ConfigTester(self							,			config_class=snake_case      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Optional[int]:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Optional[Any]:
          __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          __UpperCAmelCase							,       __UpperCAmelCase     :			List[Any]  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                    __UpperCAmelCase     :			Any  							=      self._prepare_for_class(snake_case							,			snake_case      )
                    __UpperCAmelCase     :			List[str]  							=      model_class(snake_case      )

                    @jax.jit
                    def encode_jitted(snake_case					:		Any							,			snake_case					:		List[str]=None							,			**snake_case					:		List[Any]      ):
                         return model.encode(input_ids=snake_case							,			attention_mask=snake_case      )

                    with self.subTest('''JIT Enabled'''      ):
                         __UpperCAmelCase     :			Optional[Any]  							=      encode_jitted(**snake_case      ).to_tuple()

                    with self.subTest('''JIT Disabled'''      ):
                         with jax.disable_jit():
                              __UpperCAmelCase     :			List[Any]  							=      encode_jitted(**snake_case      ).to_tuple()

                    self.assertEqual(len(snake_case      )							,			len(snake_case      )      )
                    for jitted_output, output in zip(snake_case							,			snake_case      ):
                         self.assertEqual(jitted_output.shape							,			output.shape      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							List[str]:
          __UpperCAmelCase							,       __UpperCAmelCase     :			Optional[int]  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                    __UpperCAmelCase     :			Optional[int]  							=      model_class(snake_case      )
                    __UpperCAmelCase     :			Dict  							=      model.encode(inputs_dict['''input_ids''']							,			inputs_dict['''attention_mask''']      )

                    __UpperCAmelCase     :			List[Any]  							=      {
                        '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                        '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                        '''encoder_outputs''': encoder_outputs,
                    }

                    @jax.jit
                    def decode_jitted(snake_case					:		Dict							,			snake_case					:		Dict							,			snake_case					:		List[str]      ):
                         return model.decode(
                             decoder_input_ids=snake_case							,			decoder_attention_mask=snake_case							,			encoder_outputs=snake_case							,			)

                    with self.subTest('''JIT Enabled'''      ):
                         __UpperCAmelCase     :			List[str]  							=      decode_jitted(**snake_case      ).to_tuple()

                    with self.subTest('''JIT Disabled'''      ):
                         with jax.disable_jit():
                              __UpperCAmelCase     :			Union[str, Any]  							=      decode_jitted(**snake_case      ).to_tuple()

                    self.assertEqual(len(snake_case      )							,			len(snake_case      )      )
                    for jitted_output, output in zip(snake_case							,			snake_case      ):
                         self.assertEqual(jitted_output.shape							,			output.shape      )

     @slow
     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Union[str, Any]:
          for model_class_name in self.all_model_classes:
               __UpperCAmelCase     :			Dict  							=      model_class_name.from_pretrained('''google/pegasus-large'''							,			from_pt=snake_case      )
               __UpperCAmelCase     :			Union[str, Any]  							=      np.ones((1, 1)      )
               __UpperCAmelCase     :			str  							=      model(snake_case      )
               self.assertIsNotNone(snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''      )
          __UpperCAmelCase     :			List[str]  							=      PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''      )

          __UpperCAmelCase     :			Optional[Any]  							=      [
              ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
              ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
          ]

          __UpperCAmelCase     :			Optional[Any]  							=      [
              '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
              '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
          ]

          __UpperCAmelCase     :			Tuple  							=      tokenizer(snake_case							,			return_tensors='''np'''							,			truncation=snake_case							,			max_length=512							,			padding=snake_case      )
          __UpperCAmelCase     :			Tuple  							=      model.generate(**snake_case							,			num_beams=2      ).sequences
          __UpperCAmelCase     :			List[Any]  							=      tokenizer.batch_decode(snake_case							,			skip_special_tokens=snake_case      )
          assert tgt_text == decoded



'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

__UpperCAmelCase      :str			    =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase      :Optional[int]			    =       {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase      :Union[str, Any]			    =       {
    "bert-base-uncased": 5_1_2,
    "bert-large-uncased": 5_1_2,
    "bert-base-cased": 5_1_2,
    "bert-large-cased": 5_1_2,
    "bert-base-multilingual-uncased": 5_1_2,
    "bert-base-multilingual-cased": 5_1_2,
    "bert-base-chinese": 5_1_2,
    "bert-base-german-cased": 5_1_2,
    "bert-large-uncased-whole-word-masking": 5_1_2,
    "bert-large-cased-whole-word-masking": 5_1_2,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-base-cased-finetuned-mrpc": 5_1_2,
    "bert-base-german-dbmdz-cased": 5_1_2,
    "bert-base-german-dbmdz-uncased": 5_1_2,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_1_2,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_1_2,
    "wietsedv/bert-base-dutch-cased": 5_1_2,
}

__UpperCAmelCase      :str			    =       {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       BertTokenizer

     def __init__(    self					:		Optional[int]							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		str=None							,			snake_case					:		Any=True							,			snake_case					:		Tuple="[UNK]"							,			snake_case					:		int="[SEP]"							,			snake_case					:		Optional[Any]="[PAD]"							,			snake_case					:		int="[CLS]"							,			snake_case					:		Optional[Any]="[MASK]"							,			snake_case					:		Union[str, Any]=True							,			snake_case					:		List[Any]=None							,			**snake_case					:		int							,			)						->							str:
          super().__init__(
              snake_case							,			tokenizer_file=snake_case							,			do_lower_case=snake_case							,			unk_token=snake_case							,			sep_token=snake_case							,			pad_token=snake_case							,			cls_token=snake_case							,			mask_token=snake_case							,			tokenize_chinese_chars=snake_case							,			strip_accents=snake_case							,			**snake_case							,			)

          __UpperCAmelCase     :			List[str]  							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              normalizer_state.get('''lowercase'''							,			snake_case      ) != do_lower_case
              or normalizer_state.get('''strip_accents'''							,			snake_case      ) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''							,			snake_case      ) != tokenize_chinese_chars
          ):
               __UpperCAmelCase     :			List[Any]  							=      getattr(snake_case							,			normalizer_state.pop('''type'''      )      )
               __UpperCAmelCase     :			List[Any]  							=      do_lower_case
               __UpperCAmelCase     :			List[Any]  							=      strip_accents
               __UpperCAmelCase     :			str  							=      tokenize_chinese_chars
               __UpperCAmelCase     :			List[str]  							=      normalizer_class(**snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      do_lower_case

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[str]=None      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

          return output

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			Tuple  							=      [self.cls_token_id]
          if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
          return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Optional[str] = None      )						->							Tuple[str]:
          __UpperCAmelCase     :			Optional[int]  							=      self._tokenizer.model.save(snake_case							,			name=snake_case      )
          return tuple(snake_case      )