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


A     :	str    = logging.get_logger(__name__)

A     :	Tuple    = {
    '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 			__A( a			):
				snake_case_						   =    '''decision_transformer'''
				snake_case_						   =    ['''past_key_values''']
				snake_case_						   =    {
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(     self		,     _snake_case=17		,     _snake_case=4		,     _snake_case=128		,     _snake_case=4_096		,     _snake_case=True		,     _snake_case=1		,     _snake_case=1_024		,     _snake_case=3		,     _snake_case=1		,     _snake_case=None		,     _snake_case="relu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=1E-5		,     _snake_case=0.02		,     _snake_case=True		,     _snake_case=True		,     _snake_case=50_256		,     _snake_case=50_256		,     _snake_case=False		,     _snake_case=False		,     **_snake_case		,     )    ->      List[Any]:


								'''simple docstring'''




								__a										=    state_dim
								__a										=    act_dim
								__a										=    hidden_size
								__a										=    max_ep_len
								__a										=    action_tanh
								__a										=    vocab_size
								__a										=    n_positions
								__a										=    n_layer
								__a										=    n_head
								__a										=    n_inner
								__a										=    activation_function
								__a										=    resid_pdrop
								__a										=    embd_pdrop
								__a										=    attn_pdrop
								__a										=    layer_norm_epsilon
								__a										=    initializer_range
								__a										=    scale_attn_weights
								__a										=    use_cache
								__a										=    scale_attn_by_inverse_layer_idx
								__a										=    reorder_and_upcast_attn

								__a										=    bos_token_id
								__a										=    eos_token_id

								super().__init__(bos_token_id=_snake_case		,     eos_token_id=_snake_case		,     **_snake_case				)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	str    = {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = ['BlenderbotTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Optional[Any]    = [
					    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'BlenderbotForCausalLM',
					    'BlenderbotForConditionalGeneration',
					    'BlenderbotModel',
					    'BlenderbotPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'TFBlenderbotForConditionalGeneration',
					    'TFBlenderbotModel',
					    'TFBlenderbotPreTrainedModel',
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Dict    = [
					    'FlaxBlenderbotForConditionalGeneration',
					    'FlaxBlenderbotModel',
					    'FlaxBlenderbotPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_blenderbot import (
					    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotConfig,
					    BlenderbotOnnxConfig,
					)
					from .tokenization_blenderbot import BlenderbotTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot import (
										    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotForCausalLM,
										    BlenderbotForConditionalGeneration,
										    BlenderbotModel,
										    BlenderbotPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot import (
										    TFBlenderbotForConditionalGeneration,
										    TFBlenderbotModel,
										    TFBlenderbotPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot import (
										    FlaxBlenderbotForConditionalGeneration,
										    FlaxBlenderbotModel,
										    FlaxBlenderbotPreTrainedModel,
										)

else:
					import sys

					A     :	List[Any]    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)