





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


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    '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_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"decision_transformer"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{
							    "max_position_embeddings": "n_positions",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}


							def __init__(							self    :str							,				__A    :List[Any]=17							,				__A    :Optional[int]=4							,				__A    :str=128							,				__A    :int=4096							,				__A    :Any=True							,				__A    :int=1							,				__A    :int=1024							,				__A    :int=3							,				__A    :Union[str, Any]=1							,				__A    :int=None							,				__A    :str="relu"							,				__A    :Union[str, Any]=0.1							,				__A    :Tuple=0.1							,				__A    :Optional[int]=0.1							,				__A    :Optional[int]=1E-5							,				__A    :int=0.0_2							,				__A    :Any=True							,				__A    :Dict=True							,				__A    :Optional[int]=5_0256							,				__A    :Dict=5_0256							,				__A    :str=False							,				__A    :Optional[Any]=False							,				**__A    :Any							,				)   ->   List[str]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      bos_token_id
									SCREAMING_SNAKE_CASE__			=      eos_token_id

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






from typing import TYPE_CHECKING

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


_lowerCamelCase   				=			{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3TokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'LayoutLMv3ForQuestionAnswering',
	    'LayoutLMv3ForSequenceClassification',
	    'LayoutLMv3ForTokenClassification',
	    'LayoutLMv3Model',
	    'LayoutLMv3PreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLayoutLMv3ForQuestionAnswering',
	    'TFLayoutLMv3ForSequenceClassification',
	    'TFLayoutLMv3ForTokenClassification',
	    'TFLayoutLMv3Model',
	    'TFLayoutLMv3PreTrainedModel',
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3FeatureExtractor']
	_lowerCamelCase   				=			['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
	from .configuration_layoutlmva import (
	    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    LayoutLMvaConfig,
	    LayoutLMvaOnnxConfig,
	)
	from .processing_layoutlmva import LayoutLMvaProcessor
	from .tokenization_layoutlmva import LayoutLMvaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		    LayoutLMvaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_layoutlmva import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		    TFLayoutLMvaPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
		from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
	import sys

	_lowerCamelCase   				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)