



"""simple docstring"""







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


A    =			logging.get_logger(__name__)

A    =			{
    '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      UpperCAmelCase__		(      snake_case__     ):
		lowerCAmelCase_       :	List[str] 							=     """trajectory_transformer"""
		lowerCAmelCase_       :	Any 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Union[str, Any] 							=     {
		    """hidden_size""": """n_embd""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    List[Any]      ,							snake_case  :    Union[str, Any]=100      ,							snake_case  :    Tuple=5      ,							snake_case  :    Optional[int]=1      ,							snake_case  :    Tuple=1      ,							snake_case  :    Any=249      ,							snake_case  :    Union[str, Any]=6      ,							snake_case  :    Union[str, Any]=17      ,							snake_case  :    Optional[int]=25      ,							snake_case  :    int=4      ,							snake_case  :    List[Any]=4      ,							snake_case  :    List[str]=128      ,							snake_case  :    Optional[Any]=0.1      ,							snake_case  :    Dict=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    List[Any]=0.0006      ,							snake_case  :    str=512      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    Tuple=1E-1_2      ,							snake_case  :    Optional[Any]=1      ,							snake_case  :    int=True      ,							snake_case  :    List[str]=1      ,							snake_case  :    Dict=50_256      ,							snake_case  :    List[str]=50_256      ,							**snake_case  :    Any      ,							)			->						Tuple:






									'''simple docstring'''

									A         =					vocab_size
									A         =					action_weight
									A         =					reward_weight
									A         =					value_weight
									A         =					max_position_embeddings
									A         =					block_size
									A         =					action_dim
									A         =					observation_dim
									A         =					transition_dim
									A         =					learning_rate
									A         =					n_layer
									A         =					n_head
									A         =					n_embd
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					resid_pdrop
									A         =					initializer_range
									A         =					layer_norm_eps
									A         =					kaiming_initializer_range
									A         =					use_cache
									super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE      ,							bos_token_id=_SCREAMING_SNAKE_CASE      ,							eos_token_id=_SCREAMING_SNAKE_CASE      ,							**_SCREAMING_SNAKE_CASE				)








"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Tuple 							=     LayoutLMTokenizer
		lowerCAmelCase_       :	Any 							=     LayoutLMTokenizerFast
		lowerCAmelCase_       :	Optional[int] 							=     True
		lowerCAmelCase_       :	List[Any] 							=     True



		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									A         =					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['vocab_file']				)
									with open(self.vocab_file      ,							'w'      ,							encoding='utf-8'				) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)



		def 					A_		( self  :    str      ,							**snake_case  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									return LayoutLMTokenizer.from_pretrained(self.tmpdirname      ,							**snake_case				)



		def 					A_		( self  :    List[str]      ,							snake_case  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'UNwant\u00E9d,running'
									A         =					'unwanted, running'
									return input_text, output_text



		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.tokenizer_class(self.vocab_file				)

									A         =					tokenizer.tokenize('UNwant\u00E9d,running'				)
									self.assertListEqual(snake_case      ,							['un', '##want', '##ed', ',', 'runn', '##ing']				)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case				)      ,							[7, 4, 5, 10, 8, 9]				)



		def 					A_		( self  :    Any				)			->						List[Any]:






									'''simple docstring'''

									pass



