



'''simple docstring'''



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


lowerCAmelCase	:		Union[str, Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Union[str, Any]				=   {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Any        =							"""layoutlmv3"""



					def __init__(    self			,				UpperCamelCase=5_0265			,				UpperCamelCase=768			,				UpperCamelCase=12			,				UpperCamelCase=12			,				UpperCamelCase=3072			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=2			,				UpperCamelCase=0.02			,				UpperCamelCase=1E-5			,				UpperCamelCase=1			,				UpperCamelCase=0			,				UpperCamelCase=2			,				UpperCamelCase=1024			,				UpperCamelCase=128			,				UpperCamelCase=128			,				UpperCamelCase=True			,				UpperCamelCase=32			,				UpperCamelCase=128			,				UpperCamelCase=64			,				UpperCamelCase=256			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=True			,				UpperCamelCase=224			,				UpperCamelCase=3			,				UpperCamelCase=16			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			Optional[int]:
						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			,				)
						__lowerCAmelCase												=  max_ad_position_embeddings
						__lowerCAmelCase												=  coordinate_size
						__lowerCAmelCase												=  shape_size
						__lowerCAmelCase												=  has_relative_attention_bias
						__lowerCAmelCase												=  rel_pos_bins
						__lowerCAmelCase												=  max_rel_pos
						__lowerCAmelCase												=  has_spatial_attention_bias
						__lowerCAmelCase												=  rel_ad_pos_bins
						__lowerCAmelCase												=  max_rel_ad_pos
						__lowerCAmelCase												=  text_embed
						__lowerCAmelCase												=  visual_embed
						__lowerCAmelCase												=  input_size
						__lowerCAmelCase												=  num_channels
						__lowerCAmelCase												=  patch_size
						__lowerCAmelCase												=  classifier_dropout






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     List[Any]        =							version.parse("""1.12""" )



					@property
					def        UpperCAmelCase_ (    self     )     ->			Mapping[str, Mapping[int, str]]:
						# 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        UpperCAmelCase_ (    self     )     ->			float:
						return 1E-5



					@property
					def        UpperCAmelCase_ (    self     )     ->			int:
						return 12



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = -1			,				UpperCamelCase = -1			,				UpperCamelCase = False			,				UpperCamelCase = None			,				UpperCamelCase = 3			,				UpperCamelCase = 40			,				UpperCamelCase = 40			,				)     ->			Mapping[str, Any]:
						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
						__lowerCAmelCase												=  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
						__lowerCAmelCase												=  processor.tokenizer.num_special_tokens_to_add(UpperCamelCase     )
						__lowerCAmelCase												=  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
						__lowerCAmelCase												=  [[" ".join([processor.tokenizer.unk_token]     ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						__lowerCAmelCase												=  [[[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)
						__lowerCAmelCase												=  self._generate_dummy_images(UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase			,				UpperCamelCase     )

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

						return inputs




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase	:		Optional[Any]				=   {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TrOCRForCausalLM''',
	    '''TrOCRPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
	from .processing_trocr import TrOCRProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
	import sys

	lowerCAmelCase	:		Optional[Any]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)