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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase	: Dict    		=  logging.get_logger(__name__)

__UpperCamelCase	: int    		=  {
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""gptj"""
						UpperCamelCase_  =	{
						    """max_position_embeddings""": """n_positions""",
						    """hidden_size""": """n_embd""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]=5_0400					,	UpperCamelCase__			:       Tuple=2048					,	UpperCamelCase__			:       Optional[int]=4096					,	UpperCamelCase__			:       List[str]=28					,	UpperCamelCase__			:       List[Any]=16					,	UpperCamelCase__			:       str=64					,	UpperCamelCase__			:       str=None					,	UpperCamelCase__			:       Tuple="gelu_new"					,	UpperCamelCase__			:       List[str]=0.0					,	UpperCamelCase__			:       Any=0.0					,	UpperCamelCase__			:       List[Any]=0.0					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       str=0.02					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Dict=5_0256					,	UpperCamelCase__			:       Any=5_0256					,	UpperCamelCase__			:       int=False					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							n_positions
												SCREAMING_SNAKE_CASE							:					int           =							n_embd
												SCREAMING_SNAKE_CASE							:					Tuple           =							n_layer
												SCREAMING_SNAKE_CASE							:					Any           =							n_head
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							n_inner
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rotary_dim
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							activation_function
												SCREAMING_SNAKE_CASE							:					Dict           =							resid_pdrop
												SCREAMING_SNAKE_CASE							:					Dict           =							embd_pdrop
												SCREAMING_SNAKE_CASE							:					Dict           =							attn_pdrop
												SCREAMING_SNAKE_CASE							:					Any           =							layer_norm_epsilon
												SCREAMING_SNAKE_CASE							:					int           =							initializer_range
												SCREAMING_SNAKE_CASE							:					str           =							use_cache

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							bos_token_id
												SCREAMING_SNAKE_CASE							:					str           =							eos_token_id

												super().__init__(
												    bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	tie_word_embeddings=UpperCamelCase__					,	**UpperCamelCase__		)






class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       PretrainedConfig					,	UpperCamelCase__			:       str = "default"					,	UpperCamelCase__			:       List[PatchingSpec] = None					,	UpperCamelCase__			:       bool = False					,	):





												'''simple docstring'''





												super().__init__(UpperCamelCase__					,	task=UpperCamelCase__					,	patching_specs=UpperCamelCase__					,	use_past=UpperCamelCase__		)
												if not getattr(self._config					,	'''pad_token_id'''					,	UpperCamelCase__		):
																		# TODO: how to do that better?
																		SCREAMING_SNAKE_CASE							:					Any           =							0


						@property
						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}		)
												if self.use_past:
																		self.fill_with_past_key_values_(UpperCamelCase__					,	direction='''inputs'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''past_sequence + sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							{0: '''batch''', 1: '''sequence'''}

												return common_inputs


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return self._config.n_layer


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return self._config.n_head


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       PreTrainedTokenizer					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional[TensorType] = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							super(UpperCamelCase__					,	self		).generate_dummy_inputs(
												    UpperCamelCase__					,	batch_size=UpperCamelCase__					,	seq_length=UpperCamelCase__					,	is_pair=UpperCamelCase__					,	framework=UpperCamelCase__		)

												# We need to order the input in the way they appears in the forward()
												SCREAMING_SNAKE_CASE							:					Any           =							OrderedDict({'''input_ids''': common_inputs['''input_ids''']}		)

												# Need to add the past_keys
												if self.use_past:
																		if not is_torch_available():
																								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
																		else:
																								import torch

																								SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							common_inputs['''input_ids'''].shape
																								# Not using the same length for past_key_values
																								SCREAMING_SNAKE_CASE							:					int           =							seqlen + 2
																								SCREAMING_SNAKE_CASE							:					Optional[int]           =							(
																								    batch,
																								    self.num_attention_heads,
																								    past_key_values_length,
																								    self._config.hidden_size // self.num_attention_heads,
																								)
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[
																								    (torch.zeros(UpperCamelCase__		), torch.zeros(UpperCamelCase__		)) for _ in range(self.num_layers		)
																								]

												SCREAMING_SNAKE_CASE							:					str           =							common_inputs['''attention_mask''']
												if self.use_past:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							ordered_inputs['''attention_mask'''].dtype
																		SCREAMING_SNAKE_CASE							:					int           =							torch.cat(
																		    [ordered_inputs['''attention_mask'''], torch.ones(UpperCamelCase__					,	UpperCamelCase__					,	dtype=UpperCamelCase__		)]					,	dim=1		)

												return ordered_inputs



						@property
						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return 13






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	: str    		=  logging.get_logger(__name__)

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





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=UpperCamelCase__					,	hidden_size=UpperCamelCase__					,	num_hidden_layers=UpperCamelCase__					,	num_attention_heads=UpperCamelCase__					,	intermediate_size=UpperCamelCase__					,	hidden_act=UpperCamelCase__					,	hidden_dropout_prob=UpperCamelCase__					,	attention_probs_dropout_prob=UpperCamelCase__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												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			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	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
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=UpperCamelCase__		)
												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					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)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





