



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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			  =			'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )
def 						UpperCamelCase      (  snake_case__			:       Union[str, Any]			,	snake_case__			:       Dict			,	snake_case__			:       int=None			,	snake_case__			:       Optional[int]=None			,	snake_case__			:       Any=None			,	snake_case__			:       Optional[int]=None			,	snake_case__			:       Any=None			,	snake_case__			:       Tuple=None			,	)      ->	Optional[int]:
   if attention_mask is None:
      UpperCamelCase       :    Optional[Any]		      =      np.where(input_ids != config.pad_token_id			,	1			,	0      )
   if decoder_attention_mask is None:
      UpperCamelCase       :    List[Any]		      =      np.where(decoder_input_ids != config.pad_token_id			,	1			,	0      )
   if head_mask is None:
      UpperCamelCase       :    List[str]		      =      np.ones((config.encoder_layers, config.encoder_attention_heads)      )
   if decoder_head_mask is None:
      UpperCamelCase       :    Optional[int]		      =      np.ones((config.decoder_layers, config.decoder_attention_heads)      )
   if cross_attn_head_mask is None:
      UpperCamelCase       :    Optional[int]		      =      np.ones((config.decoder_layers, config.decoder_attention_heads)      )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }



class 							lowerCAmelCase_						:
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=13,     SCREAMING_SNAKE_CASE_=7,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=99,     SCREAMING_SNAKE_CASE_=16,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_=4,     SCREAMING_SNAKE_CASE_="gelu",     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=0.1,     SCREAMING_SNAKE_CASE_=32,     SCREAMING_SNAKE_CASE_=2,     SCREAMING_SNAKE_CASE_=1,     SCREAMING_SNAKE_CASE_=0,     SCREAMING_SNAKE_CASE_=0.02,     )	->						int:
          UpperCamelCase       :    Dict		      =      parent
          UpperCamelCase       :    Dict		      =      batch_size
          UpperCamelCase       :    Optional[int]		      =      seq_length
          UpperCamelCase       :    Any		      =      is_training
          UpperCamelCase       :    Optional[Any]		      =      use_labels
          UpperCamelCase       :    str		      =      vocab_size
          UpperCamelCase       :    Dict		      =      hidden_size
          UpperCamelCase       :    Any		      =      num_hidden_layers
          UpperCamelCase       :    Union[str, Any]		      =      num_attention_heads
          UpperCamelCase       :    str		      =      intermediate_size
          UpperCamelCase       :    List[str]		      =      hidden_act
          UpperCamelCase       :    Optional[Any]		      =      hidden_dropout_prob
          UpperCamelCase       :    List[str]		      =      attention_probs_dropout_prob
          UpperCamelCase       :    Union[str, Any]		      =      max_position_embeddings
          UpperCamelCase       :    Optional[Any]		      =      eos_token_id
          UpperCamelCase       :    int		      =      pad_token_id
          UpperCamelCase       :    Tuple		      =      bos_token_id
          UpperCamelCase       :    List[str]		      =      initializer_range
       def    snake_case_			(     self			)	->						str:
          UpperCamelCase       :    Tuple		      =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size			),     3,     self.vocab_size			)
          UpperCamelCase       :    Any		      =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),     dtype=np.intaa			)),     -1			)

          UpperCamelCase       :    Tuple		      =      shift_tokens_right(SCREAMING_SNAKE_CASE_,     1,     2			)

          UpperCamelCase       :    Union[str, Any]		      =      BlenderbotConfig(
              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_id=self.eos_token_id,     bos_token_id=self.bos_token_id,     pad_token_id=self.pad_token_id,     initializer_range=self.initializer_range,     use_cache=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    Any		      =      prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          return config, inputs_dict
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      self.prepare_config_and_inputs()
          return config, inputs_dict
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    str		      =      20
          UpperCamelCase       :    Union[str, Any]		      =      model_class_name(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[Any]		      =      model.encode(inputs_dict['input_ids']			)

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

          UpperCamelCase       :    int		      =      model.init_cache(decoder_input_ids.shape[0],     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    List[Any]		      =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length),     dtype='i4'			)

          UpperCamelCase       :    Optional[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       :    str		      =      model.decode(
              decoder_input_ids[:, :-1],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    Optional[Any]		      =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='i4'			)
          UpperCamelCase       :    Tuple		      =      model.decode(
              decoder_input_ids[:, -1:],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=outputs_cache.past_key_values,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    List[Any]		      =      model.decode(SCREAMING_SNAKE_CASE_,     SCREAMING_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    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						str:
          UpperCamelCase       :    str		      =      20
          UpperCamelCase       :    List[str]		      =      model_class_name(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      model.encode(inputs_dict['input_ids']			)

          UpperCamelCase      ,	UpperCamelCase       :    Optional[Any]		      =      (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCamelCase       :    Any		      =      jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
              ],     axis=-1,     )

          UpperCamelCase       :    Dict		      =      model.init_cache(decoder_input_ids.shape[0],     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, 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       :    Tuple		      =      model.decode(
              decoder_input_ids[:, :-1],     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     past_key_values=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )
          UpperCamelCase       :    List[Any]		      =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],     dtype='i4'			)
          UpperCamelCase       :    int		      =      model.decode(
              decoder_input_ids[:, -1:],     SCREAMING_SNAKE_CASE_,     past_key_values=outputs_cache.past_key_values,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     decoder_position_ids=SCREAMING_SNAKE_CASE_,     )

          UpperCamelCase       :    List[Any]		      =      model.decode(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    List[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}"""			)






@require_flax
class 							lowerCAmelCase_						(    unittest.TestCase				):
       UpperCAmelCase__							:   List[str]  	=    99
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    List[str]		      =      np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ],     dtype=np.intaa,     )

          UpperCamelCase       :    int		      =      input_ids.shape[0]
          UpperCamelCase       :    Optional[int]		      =      BlenderbotConfig(
              vocab_size=self.vocab_size,     d_model=24,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=32,     decoder_ffn_dim=32,     max_position_embeddings=48,     eos_token_id=2,     pad_token_id=1,     bos_token_id=0,     )
          return config, input_ids, batch_size
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase       :    List[Any]		      =      self._get_config_and_data()
          UpperCamelCase       :    Union[str, Any]		      =      FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      lm_model(input_ids=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Union[str, Any]		      =      (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						int:
          UpperCamelCase       :    List[str]		      =      BlenderbotConfig(
              vocab_size=self.vocab_size,     d_model=14,     encoder_layers=2,     decoder_layers=2,     encoder_attention_heads=2,     decoder_attention_heads=2,     encoder_ffn_dim=8,     decoder_ffn_dim=8,     max_position_embeddings=48,     )
          UpperCamelCase       :    Dict		      =      FlaxBlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],     dtype=np.intaa			)
          UpperCamelCase       :    Dict		      =      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],     dtype=np.intaa			)
          UpperCamelCase       :    Any		      =      lm_model(input_ids=SCREAMING_SNAKE_CASE_,     decoder_input_ids=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[int]		      =      (*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape,     SCREAMING_SNAKE_CASE_			)






       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    int		      =      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],     dtype=np.intaa			)
          UpperCamelCase       :    int		      =      shift_tokens_right(SCREAMING_SNAKE_CASE_,     1,     2			)
          UpperCamelCase       :    Tuple		      =      np.equal(SCREAMING_SNAKE_CASE_,     1			).astype(np.floataa			).sum()
          UpperCamelCase       :    int		      =      np.equal(SCREAMING_SNAKE_CASE_,     1			).astype(np.floataa			).sum()
          self.assertEqual(shifted.shape,     input_ids.shape			)
          self.assertEqual(SCREAMING_SNAKE_CASE_,     n_pad_before - 1			)
          self.assertTrue(np.equal(shifted[:, 0],     2			).all()			)






@require_flax
class 							lowerCAmelCase_						(    a__			,       unittest.TestCase			,       a__				):
       UpperCAmelCase__							:   Any  	=    True
       UpperCAmelCase__							:   List[Any]  	=    (
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCAmelCase__							:   Optional[Any]  	=    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
       def    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Dict		      =      FlaxBlenderbotModelTester(self			)
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase      ,	UpperCamelCase       :    Dict		      =      self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
       def    snake_case_			(     self			)	->						Optional[Any]:
          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       :    Tuple		      =      self._prepare_for_class(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    List[str]		      =      model_class(SCREAMING_SNAKE_CASE_			)

                @jax.jit
                def encode_jitted(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=None,     **SCREAMING_SNAKE_CASE_			):
                   return model.encode(input_ids=SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_			)

                with self.subTest('JIT Enabled'			):
                   UpperCamelCase       :    Optional[int]		      =      encode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                with self.subTest('JIT Disabled'			):
                   with jax.disable_jit():
                      UpperCamelCase       :    int		      =      encode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE_			),     len(SCREAMING_SNAKE_CASE_			)			)
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                   self.assertEqual(jitted_output.shape,     output.shape			)
       def    snake_case_			(     self			)	->						Dict:
          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       :    List[str]		      =      model_class(SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    Union[str, Any]		      =      model.encode(inputs_dict['input_ids'],     inputs_dict['attention_mask']			)

                UpperCamelCase       :    int		      =      {
                    '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(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                   return model.decode(
                       decoder_input_ids=SCREAMING_SNAKE_CASE_,     decoder_attention_mask=SCREAMING_SNAKE_CASE_,     encoder_outputs=SCREAMING_SNAKE_CASE_,     )

                with self.subTest('JIT Enabled'			):
                   UpperCamelCase       :    Dict		      =      decode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                with self.subTest('JIT Disabled'			):
                   with jax.disable_jit():
                      UpperCamelCase       :    Optional[Any]		      =      decode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()

                self.assertEqual(len(SCREAMING_SNAKE_CASE_			),     len(SCREAMING_SNAKE_CASE_			)			)
                for jitted_output, output in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                   self.assertEqual(jitted_output.shape,     output.shape			)
       @slow
       def    snake_case_			(     self			)	->						Dict:
          for model_class_name in self.all_model_classes:
             UpperCamelCase       :    Optional[Any]		      =      model_class_name.from_pretrained('facebook/blenderbot-400M-distill'			)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             UpperCamelCase       :    List[str]		      =      np.ones((1, 1)			) * model.config.eos_token_id
             UpperCamelCase       :    str		      =      model(SCREAMING_SNAKE_CASE_			)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)






       @unittest.skipUnless(jax_device != 'cpu',     '3B test too slow on CPU.'			)
       @slow
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Tuple		      =      {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
          UpperCamelCase       :    Any		      =      {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

          UpperCamelCase       :    str		      =      FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B',     from_pt=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)

          UpperCamelCase       :    List[str]		      =      ['Sam']
          UpperCamelCase       :    str		      =      tokenizer(SCREAMING_SNAKE_CASE_,     return_tensors='jax'			)

          UpperCamelCase       :    int		      =      model.generate(**SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Optional[Any]		      =      'Sam is a great name. It means "sun" in Gaelic.'

          UpperCamelCase       :    Union[str, Any]		      =      tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_			)
          assert generated_txt[0].strip() == tgt_text







"""simple docstring"""

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


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

       self.assertEqual(vocab_keys[0]   ,							"""<s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<pad>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<mask>"""				)
       self.assertEqual(len(A_				)   ,							1_0_1_1_2_2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						self.tokenizer(
           A_   ,							max_length=len(A_				)   ,							padding=A_   ,							truncation=A_   ,							return_tensors="""pt"""				)
       self.assertIsInstance(A_   ,							A_				)

       self.assertEqual((2, 6)   ,							batch.input_ids.shape				)
       self.assertEqual((2, 6)   ,							batch.attention_mask.shape				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

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

       __lowercase						=						tokenizer.tokenize(A_				)
       __lowercase						=						rust_tokenizer.tokenize(A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.encode(A_   ,							add_special_tokens=A_				)
       __lowercase						=						rust_tokenizer.encode(A_   ,							add_special_tokens=A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						self.get_rust_tokenizer()
       __lowercase						=						tokenizer.encode(A_				)
       __lowercase						=						rust_tokenizer.encode(A_				)
       self.assertListEqual(A_   ,							A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 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, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       __lowercase						=						[
           """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
           """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
           """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
           """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
           """telles que la traduction et la synthèse de texte.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""moussaKam/mbarthez"""   ,							revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""   ,							sequences=A_   ,							)
