


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


__snake_case            =   logging.get_logger(__name__)

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




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= """layoutlmv3"""




			def __init__(						self							:		int       ,       __magic_name__							:		Tuple=5_0_2_6_5       ,       __magic_name__							:		Any=7_6_8       ,       __magic_name__							:		Tuple=1_2       ,       __magic_name__							:		Union[str, Any]=1_2       ,       __magic_name__							:		List[str]=3_0_7_2       ,       __magic_name__							:		int="gelu"       ,       __magic_name__							:		List[str]=0.1       ,       __magic_name__							:		Dict=0.1       ,       __magic_name__							:		List[Any]=5_1_2       ,       __magic_name__							:		Dict=2       ,       __magic_name__							:		str=0.02       ,       __magic_name__							:		Tuple=1e-5       ,       __magic_name__							:		List[str]=1       ,       __magic_name__							:		Optional[int]=0       ,       __magic_name__							:		Union[str, Any]=2       ,       __magic_name__							:		List[str]=1_0_2_4       ,       __magic_name__							:		Any=1_2_8       ,       __magic_name__							:		Optional[Any]=1_2_8       ,       __magic_name__							:		Dict=True       ,       __magic_name__							:		Union[str, Any]=3_2       ,       __magic_name__							:		Optional[int]=1_2_8       ,       __magic_name__							:		str=6_4       ,       __magic_name__							:		Tuple=2_5_6       ,       __magic_name__							:		int=True       ,       __magic_name__							:		str=True       ,       __magic_name__							:		Dict=True       ,       __magic_name__							:		str=2_2_4       ,       __magic_name__							:		Optional[int]=3       ,       __magic_name__							:		List[Any]=1_6       ,       __magic_name__							:		Any=None       ,       **__magic_name__							:		Union[str, Any]       ,       ):







						"""simple docstring"""







						super().__init__(
						    vocab_size=__magic_name__       ,       hidden_size=__magic_name__       ,       num_hidden_layers=__magic_name__       ,       num_attention_heads=__magic_name__       ,       intermediate_size=__magic_name__       ,       hidden_act=__magic_name__       ,       hidden_dropout_prob=__magic_name__       ,       attention_probs_dropout_prob=__magic_name__       ,       max_position_embeddings=__magic_name__       ,       type_vocab_size=__magic_name__       ,       initializer_range=__magic_name__       ,       layer_norm_eps=__magic_name__       ,       pad_token_id=__magic_name__       ,       bos_token_id=__magic_name__       ,       eos_token_id=__magic_name__       ,       **__magic_name__       ,       )
						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    UpperCAmelCase					(       __snake_case    ):
			lowercase						= version.parse("""1.12"""    )




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""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   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						return 1e-5




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







						"""simple docstring"""







						return 1_2





			def   lowerCamelCase_					(						self							:		str       ,       __magic_name__							:		"ProcessorMixin"       ,       __magic_name__							:		int = -1       ,       __magic_name__							:		int = -1       ,       __magic_name__							:		bool = False       ,       __magic_name__							:		Optional["TensorType"] = None       ,       __magic_name__							:		int = 3       ,       __magic_name__							:		int = 4_0       ,       __magic_name__							:		int = 4_0       ,       ):







						"""simple docstring"""







						setattr(processor.image_processor       ,       """apply_ocr"""       ,       __magic_name__						)

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

						# Generate dummy bounding boxes
						UpperCamelCase         =					[[[4_8, 8_4, 7_3, 1_2_8]]] * 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(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					dict(
						    processor(
						        __magic_name__       ,       text=__magic_name__       ,       boxes=__magic_name__       ,       return_tensors=__magic_name__       ,       )						)

						return inputs








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




