


'''simple docstring'''


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


UpperCAmelCase_		:   int							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Dict							   =		{
    '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__  (						_snake_case					):





							'''simple docstring'''


							A_							:      Optional[Any]      	=  """trajectory_transformer"""
							A_							:      Optional[Any]      	=  ["""past_key_values"""]
							A_							:      str      	=  {
							    """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=0.0006    ,					__snake_case=512    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=1    ,					__snake_case=True    ,					__snake_case=1    ,					__snake_case=5_0256    ,					__snake_case=5_0256    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      action_weight
											_SCREAMING_SNAKE_CASE :       List[str]     		=      reward_weight
											_SCREAMING_SNAKE_CASE :       List[str]     		=      value_weight
											_SCREAMING_SNAKE_CASE :       List[str]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      block_size
											_SCREAMING_SNAKE_CASE :       str     		=      action_dim
											_SCREAMING_SNAKE_CASE :       str     		=      observation_dim
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      transition_dim
											_SCREAMING_SNAKE_CASE :       int     		=      learning_rate
											_SCREAMING_SNAKE_CASE :       int     		=      n_layer
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      n_head
											_SCREAMING_SNAKE_CASE :       int     		=      n_embd
											_SCREAMING_SNAKE_CASE :       List[str]     		=      embd_pdrop
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      attn_pdrop
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      resid_pdrop
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Dict     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       str     		=      kaiming_initializer_range
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      use_cache
											super().__init__(pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					**__snake_case  )








'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:   Union[str, Any]							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      str      	=  """big_bird"""






							def __init__(    self    ,					__snake_case=5_0358    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu_new"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=4096    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=True    ,					__snake_case=0    ,					__snake_case=1    ,					__snake_case=2    ,					__snake_case=66    ,					__snake_case="block_sparse"    ,					__snake_case=True    ,					__snake_case=False    ,					__snake_case=64    ,					__snake_case=3    ,					__snake_case=None    ,					**__snake_case    ,					):
											super().__init__(
											    pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					sep_token_id=__snake_case    ,					**__snake_case    ,					)

											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Any     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      layer_norm_eps
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      use_cache

											_SCREAMING_SNAKE_CASE :       List[Any]     		=      rescale_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      attention_type
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_bias
											_SCREAMING_SNAKE_CASE :       int     		=      block_size
											_SCREAMING_SNAKE_CASE :       Any     		=      num_random_blocks
											_SCREAMING_SNAKE_CASE :       List[str]     		=      classifier_dropout





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@property
							def UpperCAmelCase_	(    self  ):
											if self.task == "multiple-choice":
															_SCREAMING_SNAKE_CASE :       int     		=      {0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {0: """batch""", 1: """sequence"""}
											return OrderedDict(
											    [
											        ("""input_ids""", dynamic_axis),
											        ("""attention_mask""", dynamic_axis),
											    ]  )




