
"""simple docstring"""

from typing import TYPE_CHECKING

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


_A						=			{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_A						=			['ViTFeatureExtractor']
					_A						=			['ViTImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_A						=			[
					    'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'ViTForImageClassification',
					    'ViTForMaskedImageModeling',
					    'ViTModel',
					    'ViTPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_A						=			[
					    'TFViTForImageClassification',
					    'TFViTModel',
					    'TFViTPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_A						=			[
					    'FlaxViTForImageClassification',
					    'FlaxViTModel',
					    'FlaxViTPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_vit import ViTFeatureExtractor
										from .image_processing_vit import ViTImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit import (
										    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTForImageClassification,
										    ViTForMaskedImageModeling,
										    ViTModel,
										    ViTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
					import sys

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








"""simple docstring"""

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


_A						=			logging.get_logger(__name__)

_A						=			{
    '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       (    __UpperCAmelCase		):
	lowercase_				=     'trajectory_transformer'
	lowercase_				=     ['past_key_values']
	lowercase_				=     {
	    'hidden_size': 'n_embd',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}




	def __init__(			self		,			UpperCAmelCase_=100		,			UpperCAmelCase_=5		,			UpperCAmelCase_=1		,			UpperCAmelCase_=1		,			UpperCAmelCase_=249		,			UpperCAmelCase_=6		,			UpperCAmelCase_=17		,			UpperCAmelCase_=25		,			UpperCAmelCase_=4		,			UpperCAmelCase_=4		,			UpperCAmelCase_=128		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.0006		,			UpperCAmelCase_=512		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=True		,			UpperCAmelCase_=1		,			UpperCAmelCase_=50256		,			UpperCAmelCase_=50256		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      int          =   vocab_size
			lowerCamelCase					:      List[str]          =   action_weight
			lowerCamelCase					:      List[Any]          =   reward_weight
			lowerCamelCase					:      List[str]          =   value_weight
			lowerCamelCase					:      Tuple          =   max_position_embeddings
			lowerCamelCase					:      List[str]          =   block_size
			lowerCamelCase					:      Any          =   action_dim
			lowerCamelCase					:      List[Any]          =   observation_dim
			lowerCamelCase					:      Any          =   transition_dim
			lowerCamelCase					:      int          =   learning_rate
			lowerCamelCase					:      Union[str, Any]          =   n_layer
			lowerCamelCase					:      Tuple          =   n_head
			lowerCamelCase					:      Any          =   n_embd
			lowerCamelCase					:      Union[str, Any]          =   embd_pdrop
			lowerCamelCase					:      Optional[int]          =   attn_pdrop
			lowerCamelCase					:      int          =   resid_pdrop
			lowerCamelCase					:      Optional[int]          =   initializer_range
			lowerCamelCase					:      List[str]          =   layer_norm_eps
			lowerCamelCase					:      Any          =   kaiming_initializer_range
			lowerCamelCase					:      str          =   use_cache
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )






