





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"trajectory_transformer"
 __lowerCamelCase						:							int         =					["past_key_values"]
 __lowerCamelCase						:							str         =					{
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }




 def __init__(	self			,     _lowerCAmelCase=100			,     _lowerCAmelCase=5			,     _lowerCAmelCase=1			,     _lowerCAmelCase=1			,     _lowerCAmelCase=249			,     _lowerCAmelCase=6			,     _lowerCAmelCase=17			,     _lowerCAmelCase=25			,     _lowerCAmelCase=4			,     _lowerCAmelCase=4			,     _lowerCAmelCase=128			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0006			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=1			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=50256			,     _lowerCAmelCase=50256			,     **_lowerCAmelCase			,     )    ->							Optional[Any]:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      action_weight
     _lowerCAmelCase				=      reward_weight
     _lowerCAmelCase				=      value_weight
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      block_size
     _lowerCAmelCase				=      action_dim
     _lowerCAmelCase				=      observation_dim
     _lowerCAmelCase				=      transition_dim
     _lowerCAmelCase				=      learning_rate
     _lowerCAmelCase				=      n_layer
     _lowerCAmelCase				=      n_head
     _lowerCAmelCase				=      n_embd
     _lowerCAmelCase				=      embd_pdrop
     _lowerCAmelCase				=      attn_pdrop
     _lowerCAmelCase				=      resid_pdrop
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      kaiming_initializer_range
     _lowerCAmelCase				=      use_cache
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)









'''simple docstring'''


from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
       from ..models.auto import AutoTokenizer

class    lowerCAmelCase_		:




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     raise NotImplementedError()





 def 							_snake_case   (	self		)    ->							Optional[int]:
     raise NotImplementedError()







class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False			,     **_lowerCAmelCase		)    ->							str:
     _lowerCAmelCase				=      tokenizer
     _lowerCAmelCase				=      skip_prompt
     _lowerCAmelCase				=      decode_kwargs

     # variables used in the streaming process
     _lowerCAmelCase				=      []
     _lowerCAmelCase				=      0
     _lowerCAmelCase				=      True




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[Any]:
     if len(value.shape		) > 1 and value.shape[0] > 1:
         raise ValueError("TextStreamer only supports batch size 1"		)
     elif len(value.shape		) > 1:
         _lowerCAmelCase				=      value[0]

     if self.skip_prompt and self.next_tokens_are_prompt:
         _lowerCAmelCase				=      False
         return

     # Add the new token to the cache and decodes the entire thing.
     self.token_cache.extend(value.tolist()		)
     _lowerCAmelCase				=      self.tokenizer.decode(self.token_cache			,     **self.decode_kwargs		)

     # After the symbol for a new line, we flush the cache.
     if text.endswith("\n"		):
         _lowerCAmelCase				=      text[self.print_len :]
         _lowerCAmelCase				=      []
         _lowerCAmelCase				=      0
     # If the last token is a CJK character, we print the characters.
     elif len(_lowerCAmelCase		) > 0 and self._is_chinese_char(ord(text[-1]		)		):
         _lowerCAmelCase				=      text[self.print_len :]
         self.print_len += len(_lowerCAmelCase		)
     # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
     # which may change with the subsequent token -- there are probably smarter ways to do this!)
     else:
         _lowerCAmelCase				=      text[self.print_len : text.rfind(" "		) + 1]
         self.print_len += len(_lowerCAmelCase		)

     self.on_finalized_text(_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Union[str, Any]:
     # Flush the cache, if it exists
     if len(self.token_cache		) > 0:
         _lowerCAmelCase				=      self.tokenizer.decode(self.token_cache			,     **self.decode_kwargs		)
         _lowerCAmelCase				=      text[self.print_len :]
         _lowerCAmelCase				=      []
         _lowerCAmelCase				=      0
     else:
         _lowerCAmelCase				=      ""

     _lowerCAmelCase				=      True
     self.on_finalized_text(_lowerCAmelCase			,     stream_end=_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							List[Any]:
     print(_lowerCAmelCase			,     flush=_lowerCAmelCase			,     end="" if not stream_end else None		)





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     # This defines a "chinese character" as anything in the CJK Unicode block:
     #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
     #
     # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
     # despite its name. The modern Korean Hangul alphabet is a different block,
     # as is Japanese Hiragana and Katakana. Those alphabets are used to write
     # space-separated words, so they are not treated specially and handled
     # like the all of the other languages.
     if (
         (cp >= 0X4E_00 and cp <= 0X9F_FF)
         or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
         or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
         or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
         or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
         or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
         or (cp >= 0XF9_00 and cp <= 0XFA_FF)
         or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
     ):  #
         return True

     return False







class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False			,     _lowerCAmelCase = None			,     **_lowerCAmelCase		)    ->							Dict:
     super().__init__(_lowerCAmelCase			,     _lowerCAmelCase			,     **_lowerCAmelCase		)
     _lowerCAmelCase				=      Queue()
     _lowerCAmelCase				=      None
     _lowerCAmelCase				=      timeout




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = False		)    ->							Any:
     self.text_queue.put(_lowerCAmelCase			,     timeout=self.timeout		)
     if stream_end:
         self.text_queue.put(self.stop_signal			,     timeout=self.timeout		)




 def __iter__(	self		)    ->							int:
     return self





 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.text_queue.get(timeout=self.timeout		)
     if value == self.stop_signal:
         raise StopIteration()
     else:
         return value


