

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


_UpperCAmelCase		:       Dict						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      '''trajectory_transformer'''
				__SCREAMING_SNAKE_CASE :			List[str]	      =      ['''past_key_values''']
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      {
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__( self				,       snake_case=100				,       snake_case=5				,       snake_case=1				,       snake_case=1				,       snake_case=249				,       snake_case=6				,       snake_case=17				,       snake_case=25				,       snake_case=4				,       snake_case=4				,       snake_case=128				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=0.00_06				,       snake_case=512				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=1				,       snake_case=True				,       snake_case=1				,       snake_case=5_0256				,       snake_case=5_0256				,       **snake_case				,       ):
											snake_case_          =   vocab_size
											snake_case_          =   action_weight
											snake_case_          =   reward_weight
											snake_case_          =   value_weight
											snake_case_          =   max_position_embeddings
											snake_case_          =   block_size
											snake_case_          =   action_dim
											snake_case_          =   observation_dim
											snake_case_          =   transition_dim
											snake_case_          =   learning_rate
											snake_case_          =   n_layer
											snake_case_          =   n_head
											snake_case_          =   n_embd
											snake_case_          =   embd_pdrop
											snake_case_          =   attn_pdrop
											snake_case_          =   resid_pdrop
											snake_case_          =   initializer_range
											snake_case_          =   layer_norm_eps
											snake_case_          =   kaiming_initializer_range
											snake_case_          =   use_cache
											super().__init__(pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case				,       **snake_case	)







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 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[str]	      =      BarthezTokenizer
				__SCREAMING_SNAKE_CASE :			str	      =      BarthezTokenizerFast
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      True




				def    a							( self	):
											super().setUp()

											snake_case_          =   BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'	)
											tokenizer.save_pretrained(self.tmpdirname	)
											tokenizer.save_pretrained(self.tmpdirname				,       legacy_format=snake_case	)
											snake_case_          =   tokenizer




				def    a							( self	):
											snake_case_          =   '<pad>'
											snake_case_          =   1

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




				def    a							( self	):
											snake_case_          =   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(snake_case	)				,       10_1122	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       10_1122	)




				@require_torch
				def    a							( self	):
											snake_case_          =   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											snake_case_          =   [0, 57, 3018, 7_0307, 91, 2]

											snake_case_          =   self.tokenizer(
											    snake_case				,       max_length=len(snake_case	)				,       padding=snake_case				,       truncation=snake_case				,       return_tensors='pt'	)
											self.assertIsInstance(snake_case				,       snake_case	)

											self.assertEqual((2, 6)				,       batch.input_ids.shape	)
											self.assertEqual((2, 6)				,       batch.attention_mask.shape	)
											snake_case_          =   batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case				,       snake_case	)




				def    a							( self	):
											if not self.test_rust_tokenizer:
																		return

											snake_case_          =   self.get_tokenizer()
											snake_case_          =   self.get_rust_tokenizer()

											snake_case_          =   'I was born in 92000, and this is falsé.'

											snake_case_          =   tokenizer.tokenize(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case				,       add_special_tokens=snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

											snake_case_          =   self.get_rust_tokenizer()
											snake_case_          =   tokenizer.encode(snake_case	)
											snake_case_          =   rust_tokenizer.encode(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'input_ids': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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_          =   [
											    '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=snake_case				,       model_name='moussaKam/mbarthez'				,       revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'				,       sequences=snake_case				,       )




