



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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

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



class 	lowerCamelCase      (						__UpperCAmelCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Dict									=  '''layoutlmv3'''



					def __init__(			self    ,	lowerCAmelCase=5_0265    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-5    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=2    ,	lowerCAmelCase=1024    ,	lowerCAmelCase=128    ,	lowerCAmelCase=128    ,	lowerCAmelCase=True    ,	lowerCAmelCase=32    ,	lowerCAmelCase=128    ,	lowerCAmelCase=64    ,	lowerCAmelCase=256    ,	lowerCAmelCase=True    ,	lowerCAmelCase=True    ,	lowerCAmelCase=True    ,	lowerCAmelCase=224    ,	lowerCAmelCase=3    ,	lowerCAmelCase=16    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
						super().__init__(
						    vocab_size=__SCREAMING_SNAKE_CASE    ,	hidden_size=__SCREAMING_SNAKE_CASE    ,	num_hidden_layers=__SCREAMING_SNAKE_CASE    ,	num_attention_heads=__SCREAMING_SNAKE_CASE    ,	intermediate_size=__SCREAMING_SNAKE_CASE    ,	hidden_act=__SCREAMING_SNAKE_CASE    ,	hidden_dropout_prob=__SCREAMING_SNAKE_CASE    ,	attention_probs_dropout_prob=__SCREAMING_SNAKE_CASE    ,	max_position_embeddings=__SCREAMING_SNAKE_CASE    ,	type_vocab_size=__SCREAMING_SNAKE_CASE    ,	initializer_range=__SCREAMING_SNAKE_CASE    ,	layer_norm_eps=__SCREAMING_SNAKE_CASE    ,	pad_token_id=__SCREAMING_SNAKE_CASE    ,	bos_token_id=__SCREAMING_SNAKE_CASE    ,	eos_token_id=__SCREAMING_SNAKE_CASE    ,	**__SCREAMING_SNAKE_CASE    ,	)
						UpperCAmelCase_						=				max_ad_position_embeddings
						UpperCAmelCase_						=				coordinate_size
						UpperCAmelCase_						=				shape_size
						UpperCAmelCase_						=				has_relative_attention_bias
						UpperCAmelCase_						=				rel_pos_bins
						UpperCAmelCase_						=				max_rel_pos
						UpperCAmelCase_						=				has_spatial_attention_bias
						UpperCAmelCase_						=				rel_ad_pos_bins
						UpperCAmelCase_						=				max_rel_ad_pos
						UpperCAmelCase_						=				text_embed
						UpperCAmelCase_						=				visual_embed
						UpperCAmelCase_						=				input_size
						UpperCAmelCase_						=				num_channels
						UpperCAmelCase_						=				patch_size
						UpperCAmelCase_						=				classifier_dropout





class 	lowerCamelCase      (						__UpperCAmelCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    str									=  version.parse('1.12'  )



					@property
					def    A__		(			self     ):

						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     ):
						return 1e-5



					@property
					def    A__		(			self     ):
						return 12



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = -1    ,	lowerCAmelCase = -1    ,	lowerCAmelCase = False    ,	lowerCAmelCase = None    ,	lowerCAmelCase = 3    ,	lowerCAmelCase = 40    ,	lowerCAmelCase = 40    ,	):
						setattr(processor.image_processor    ,	"apply_ocr"    ,	__SCREAMING_SNAKE_CASE     )

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						UpperCAmelCase_						=				compute_effective_axis_dimension(
						    __SCREAMING_SNAKE_CASE    ,	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_						=				processor.tokenizer.num_special_tokens_to_add(__SCREAMING_SNAKE_CASE     )
						UpperCAmelCase_						=				compute_effective_axis_dimension(
						    __SCREAMING_SNAKE_CASE    ,	fixed_dimension=OnnxConfig.default_fixed_sequence    ,	num_token_to_add=__SCREAMING_SNAKE_CASE     )
						# Generate dummy inputs according to compute batch and sequence
						UpperCAmelCase_						=				[[" ".join([processor.tokenizer.unk_token]     ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						UpperCAmelCase_						=				[[[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_						=				self._generate_dummy_images(__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE    ,	__SCREAMING_SNAKE_CASE     )

						UpperCAmelCase_						=				dict(
						    processor(
						        __SCREAMING_SNAKE_CASE    ,	text=__SCREAMING_SNAKE_CASE    ,	boxes=__SCREAMING_SNAKE_CASE    ,	return_tensors=__SCREAMING_SNAKE_CASE    ,	)     )

						return inputs






from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  'xlm-roberta'



					def __init__(			self    ,	lowerCAmelCase=3_0522    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=2    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-1_2    ,	lowerCAmelCase=1    ,	lowerCAmelCase=0    ,	lowerCAmelCase=2    ,	lowerCAmelCase="absolute"    ,	lowerCAmelCase=True    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							super().__init__(pad_token_id=lowerCAmelCase    ,	bos_token_id=lowerCAmelCase    ,	eos_token_id=lowerCAmelCase    ,	**lowerCAmelCase     )

							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				intermediate_size
							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_						=				position_embedding_type
							UpperCAmelCase_						=				use_cache
							UpperCAmelCase_						=				classifier_dropout





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					@property
					def    A__		(			self     ):
							if self.task == "multiple-choice":
									UpperCAmelCase_						=				{0: "batch", 1: "choice", 2: "sequence"}
							else:
									UpperCAmelCase_						=				{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]     )

