

'''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       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE   ,unittest.TestCase       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					Tuple     =					BarthezTokenizer
      SCREAMING_SNAKE_CASE__			:					Union[str, Any]     =					BarthezTokenizerFast
      SCREAMING_SNAKE_CASE__			:					Any     =					True
      SCREAMING_SNAKE_CASE__			:					Optional[int]     =					True




      def 						__UpperCAmelCase      (       self			:		Union[str, Any] ):



          """simple docstring"""





          super().setUp()

          _snake_case			:			Tuple				       =		BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
          tokenizer.save_pretrained(self.tmpdirname )
          tokenizer.save_pretrained(self.tmpdirname  ,       legacy_format=_lowercase )
          _snake_case			:			str				       =		tokenizer




      def 						__UpperCAmelCase      (       self			:		Union[str, Any] ):



          """simple docstring"""





          _snake_case			:			List[str]				       =		'<pad>'
          _snake_case			:			Tuple				       =		1

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




      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





          _snake_case			:			Optional[Any]				       =		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(_lowercase )  ,       101122 )




      def 						__UpperCAmelCase      (       self			:		List[str] ):



          """simple docstring"""





          self.assertEqual(self.get_tokenizer().vocab_size  ,       101122 )




      @require_torch
      def 						__UpperCAmelCase      (       self			:		Union[str, Any] ):



          """simple docstring"""





          _snake_case			:			Dict				       =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
          _snake_case			:			List[str]				       =		[0, 57, 3018, 70307, 91, 2]

          _snake_case			:			str				       =		self.tokenizer(
              _lowercase  ,       max_length=len(_lowercase )  ,       padding=_lowercase  ,       truncation=_lowercase  ,       return_tensors='pt' )
          self.assertIsInstance(_lowercase  ,       _lowercase )

          self.assertEqual((2, 6)  ,       batch.input_ids.shape )
          self.assertEqual((2, 6)  ,       batch.attention_mask.shape )
          _snake_case			:			List[str]				       =		batch.input_ids.tolist()[0]
          self.assertListEqual(_lowercase  ,       _lowercase )




      def 						__UpperCAmelCase      (       self			:		Dict ):



          """simple docstring"""





          if not self.test_rust_tokenizer:
              return

          _snake_case			:			Union[str, Any]				       =		self.get_tokenizer()
          _snake_case			:			Optional[int]				       =		self.get_rust_tokenizer()

          _snake_case			:			Union[str, Any]				       =		'I was born in 92000, and this is falsé.'

          _snake_case			:			Any				       =		tokenizer.tokenize(_lowercase )
          _snake_case			:			Dict				       =		rust_tokenizer.tokenize(_lowercase )
          self.assertListEqual(_lowercase  ,       _lowercase )

          _snake_case			:			Optional[Any]				       =		tokenizer.encode(_lowercase  ,       add_special_tokens=_lowercase )
          _snake_case			:			Any				       =		rust_tokenizer.encode(_lowercase  ,       add_special_tokens=_lowercase )
          self.assertListEqual(_lowercase  ,       _lowercase )

          _snake_case			:			Tuple				       =		self.get_rust_tokenizer()
          _snake_case			:			str				       =		tokenizer.encode(_lowercase )
          _snake_case			:			Union[str, Any]				       =		rust_tokenizer.encode(_lowercase )
          self.assertListEqual(_lowercase  ,       _lowercase )




      @slow
      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			Optional[int]				       =		{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 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.
          _snake_case			:			Optional[Any]				       =		[
              '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=_lowercase  ,       model_name='moussaKam/mbarthez'  ,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'  ,       sequences=_lowercase  ,       )





'''simple docstring'''



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


_lowercase          =						logging.get_logger(__name__)

_lowercase          =						{
    """edbeeching/decision-transformer-gym-hopper-medium""": (
        """https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  '''decision_transformer'''
						_lowercase						: Optional[Any]										=  ['''past_key_values''']
						_lowercase						: str										=  {
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}




						def __init__(   self       ,  _lowercase=17       ,  _lowercase=4       ,  _lowercase=128       ,  _lowercase=4_096       ,  _lowercase=True       ,  _lowercase=1       ,  _lowercase=1_024       ,  _lowercase=3       ,  _lowercase=1       ,  _lowercase=None       ,  _lowercase="relu"       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=0.1       ,  _lowercase=1e-5       ,  _lowercase=0.02       ,  _lowercase=True       ,  _lowercase=True       ,  _lowercase=50_256       ,  _lowercase=50_256       ,  _lowercase=False       ,  _lowercase=False       ,  **_lowercase       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					state_dim
												_lowerCAmelCase    		=					act_dim
												_lowerCAmelCase    		=					hidden_size
												_lowerCAmelCase    		=					max_ep_len
												_lowerCAmelCase    		=					action_tanh
												_lowerCAmelCase    		=					vocab_size
												_lowerCAmelCase    		=					n_positions
												_lowerCAmelCase    		=					n_layer
												_lowerCAmelCase    		=					n_head
												_lowerCAmelCase    		=					n_inner
												_lowerCAmelCase    		=					activation_function
												_lowerCAmelCase    		=					resid_pdrop
												_lowerCAmelCase    		=					embd_pdrop
												_lowerCAmelCase    		=					attn_pdrop
												_lowerCAmelCase    		=					layer_norm_epsilon
												_lowerCAmelCase    		=					initializer_range
												_lowerCAmelCase    		=					scale_attn_weights
												_lowerCAmelCase    		=					use_cache
												_lowerCAmelCase    		=					scale_attn_by_inverse_layer_idx
												_lowerCAmelCase    		=					reorder_and_upcast_attn

												_lowerCAmelCase    		=					bos_token_id
												_lowerCAmelCase    		=					eos_token_id

												super().__init__(bos_token_id=_lowercase       ,  eos_token_id=_lowercase       ,  **_lowercase )
