






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


UpperCAmelCase_ :						List[str]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						int      =      {
    '''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     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Any     	=  """trajectory_transformer"""
	snake_case__							:     Optional[Any]     	=  ["""past_key_values"""]
	snake_case__							:     Tuple     	=  {
	    """hidden_size""": """n_embd""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					Any=100 ,  __lowerCamelCase				:					str=5 ,  __lowerCamelCase				:					str=1 ,  __lowerCamelCase				:					Optional[int]=1 ,  __lowerCamelCase				:					int=249 ,  __lowerCamelCase				:					str=6 ,  __lowerCamelCase				:					Dict=17 ,  __lowerCamelCase				:					Optional[Any]=25 ,  __lowerCamelCase				:					List[str]=4 ,  __lowerCamelCase				:					str=4 ,  __lowerCamelCase				:					Tuple=128 ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					str=0.1 ,  __lowerCamelCase				:					Any=0.1 ,  __lowerCamelCase				:					int=0.0006 ,  __lowerCamelCase				:					List[str]=512 ,  __lowerCamelCase				:					str=0.02 ,  __lowerCamelCase				:					Any=1E-12 ,  __lowerCamelCase				:					int=1 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					Tuple=1 ,  __lowerCamelCase				:					int=50_256 ,  __lowerCamelCase				:					Union[str, Any]=50_256 ,  **__lowerCamelCase				:					Dict ,  ):
		UpperCamelCase     :Dict          =	vocab_size
		UpperCamelCase     :int          =	action_weight
		UpperCamelCase     :Tuple          =	reward_weight
		UpperCamelCase     :str          =	value_weight
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Tuple          =	block_size
		UpperCamelCase     :Optional[int]          =	action_dim
		UpperCamelCase     :int          =	observation_dim
		UpperCamelCase     :List[str]          =	transition_dim
		UpperCamelCase     :List[Any]          =	learning_rate
		UpperCamelCase     :Optional[Any]          =	n_layer
		UpperCamelCase     :Any          =	n_head
		UpperCamelCase     :List[str]          =	n_embd
		UpperCamelCase     :Any          =	embd_pdrop
		UpperCamelCase     :str          =	attn_pdrop
		UpperCamelCase     :Union[str, Any]          =	resid_pdrop
		UpperCamelCase     :Optional[Any]          =	initializer_range
		UpperCamelCase     :List[Any]          =	layer_norm_eps
		UpperCamelCase     :Optional[int]          =	kaiming_initializer_range
		UpperCamelCase     :Tuple          =	use_cache
		super().__init__(pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase	)








from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


UpperCAmelCase_ :						Dict      =      logging.get_logger(__name__)

UpperCAmelCase_ :						str      =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """layoutlmv3"""





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=50_265 ,  __lowerCamelCase				:					Dict=768 ,  __lowerCamelCase				:					Any=12 ,  __lowerCamelCase				:					int=12 ,  __lowerCamelCase				:					str=3_072 ,  __lowerCamelCase				:					List[Any]="gelu" ,  __lowerCamelCase				:					List[str]=0.1 ,  __lowerCamelCase				:					Optional[int]=0.1 ,  __lowerCamelCase				:					Optional[Any]=512 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					Union[str, Any]=0.02 ,  __lowerCamelCase				:					Union[str, Any]=1E-5 ,  __lowerCamelCase				:					Any=1 ,  __lowerCamelCase				:					Optional[int]=0 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					Dict=1_024 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=128 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					str=32 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=64 ,  __lowerCamelCase				:					List[str]=256 ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					Tuple=224 ,  __lowerCamelCase				:					Tuple=3 ,  __lowerCamelCase				:					Dict=16 ,  __lowerCamelCase				:					Union[str, Any]=None ,  **__lowerCamelCase				:					Optional[Any] ,  ):
		super().__init__(
		    vocab_size=__lowerCamelCase ,  hidden_size=__lowerCamelCase ,  num_hidden_layers=__lowerCamelCase ,  num_attention_heads=__lowerCamelCase ,  intermediate_size=__lowerCamelCase ,  hidden_act=__lowerCamelCase ,  hidden_dropout_prob=__lowerCamelCase ,  attention_probs_dropout_prob=__lowerCamelCase ,  max_position_embeddings=__lowerCamelCase ,  type_vocab_size=__lowerCamelCase ,  initializer_range=__lowerCamelCase ,  layer_norm_eps=__lowerCamelCase ,  pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :int          =	max_ad_position_embeddings
		UpperCamelCase     :Tuple          =	coordinate_size
		UpperCamelCase     :List[Any]          =	shape_size
		UpperCamelCase     :Union[str, Any]          =	has_relative_attention_bias
		UpperCamelCase     :Any          =	rel_pos_bins
		UpperCamelCase     :Optional[Any]          =	max_rel_pos
		UpperCamelCase     :str          =	has_spatial_attention_bias
		UpperCamelCase     :Tuple          =	rel_ad_pos_bins
		UpperCamelCase     :Optional[int]          =	max_rel_ad_pos
		UpperCamelCase     :Tuple          =	text_embed
		UpperCamelCase     :str          =	visual_embed
		UpperCamelCase     :Optional[Any]          =	input_size
		UpperCamelCase     :str          =	num_channels
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Optional[Any]          =	classifier_dropout

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  version.parse("""1.12"""      )





	@property
	def 						_A (		self				:					Optional[int]	):
		# The order of inputs is different for question answering and sequence classification
		if self.task in ["question-answering", "sequence-classification"]:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
			    ]	)
		else:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
			    ]	)





	@property
	def 						_A (		self				:					str	):
		return 1E-5





	@property
	def 						_A (		self				:					Dict	):
		return 12





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					"ProcessorMixin" ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional["TensorType"] = None ,  __lowerCamelCase				:					int = 3 ,  __lowerCamelCase				:					int = 40 ,  __lowerCamelCase				:					int = 40 ,  ):
		setattr(processor.image_processor ,  """apply_ocr""" ,  __lowerCamelCase	)

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[Any]          =	compute_effective_axis_dimension(
		    __lowerCamelCase ,  fixed_dimension=OnnxConfig.default_fixed_batch ,  num_token_to_add=0	)
		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[int]          =	processor.tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
		UpperCamelCase     :int          =	compute_effective_axis_dimension(
		    __lowerCamelCase ,  fixed_dimension=OnnxConfig.default_fixed_sequence ,  num_token_to_add=__lowerCamelCase	)
		# Generate dummy inputs according to compute batch and sequence
		UpperCamelCase     :Any          =	[[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

		# Generate dummy bounding boxes
		UpperCamelCase     :Optional[Any]          =	[[[48, 84, 73, 128]]] * batch_size

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
		UpperCamelCase     :List[str]          =	self._generate_dummy_images(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	dict(
		    processor(
		        __lowerCamelCase ,  text=__lowerCamelCase ,  boxes=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  )	)

		return inputs
