
'''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 )


"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': 15_36,
    'junnyu/roformer_chinese_base': 15_36,
    'junnyu/roformer_chinese_char_small': 5_12,
    'junnyu/roformer_chinese_char_base': 5_12,
    'junnyu/roformer_small_discriminator': 1_28,
    'junnyu/roformer_small_generator': 1_28,
}


UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   int           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Dict           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Union[str, Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Tuple           =     PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase							:   Optional[int]           =     RoFormerTokenizer



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_="[UNK]"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="[PAD]"     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[MASK]"     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	tokenize_chinese_chars=UpperCamelCase_     ,	strip_accents=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              pre_tok_state.get('lowercase'     ,	UpperCamelCase_      ) != do_lower_case
              or pre_tok_state.get('strip_accents'     ,	UpperCamelCase_      ) != strip_accents
          ):
              UpperCAmelCase__	:      Any     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      str     							=					do_lower_case
              UpperCAmelCase__	:      Union[str, Any]     							=					strip_accents
              UpperCAmelCase__	:      Dict     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					do_lower_case



      def __getstate__(   self      ):
          UpperCAmelCase__	:      int     							=					self.__dict__.copy()
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return state



      def __setstate__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					d
          UpperCAmelCase__	:      List[str]     							=					self.__dict__['_tokenizer'].get_vocab()
          UpperCAmelCase__	:      List[Any]     							=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase_      )      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      List[Any]     							=					[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  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      int     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Dict     							=					[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  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Any     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return super().save_pretrained(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )

