



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


lowercase_										=  logging.get_logger(__name__)

lowercase_										=  {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class 					_snake_case      (       lowercase__):
				UpperCamelCase__					:       List[Any]  		="""layoutlmv3"""




				def __init__(    self       :    Tuple,       __lowercase       :    Dict=5_0265,       __lowercase       :    Optional[Any]=768,       __lowercase       :    Any=12,       __lowercase       :    str=12,       __lowercase       :    List[Any]=3072,       __lowercase       :    int="gelu",       __lowercase       :    int=0.1,       __lowercase       :    str=0.1,       __lowercase       :    Dict=512,       __lowercase       :    Optional[Any]=2,       __lowercase       :    Optional[int]=0.02,       __lowercase       :    Optional[int]=1e-5,       __lowercase       :    Union[str, Any]=1,       __lowercase       :    str=0,       __lowercase       :    Optional[Any]=2,       __lowercase       :    Dict=1024,       __lowercase       :    List[str]=128,       __lowercase       :    int=128,       __lowercase       :    Union[str, Any]=True,       __lowercase       :    List[Any]=32,       __lowercase       :    str=128,       __lowercase       :    Optional[Any]=64,       __lowercase       :    Union[str, Any]=256,       __lowercase       :    Any=True,       __lowercase       :    int=True,       __lowercase       :    int=True,       __lowercase       :    List[str]=224,       __lowercase       :    str=3,       __lowercase       :    Optional[Any]=16,       __lowercase       :    Union[str, Any]=None,       **__lowercase       :    Optional[Any],       ):
							super().__init__(
							    vocab_size=a_,       hidden_size=a_,       num_hidden_layers=a_,       num_attention_heads=a_,       intermediate_size=a_,       hidden_act=a_,       hidden_dropout_prob=a_,       attention_probs_dropout_prob=a_,       max_position_embeddings=a_,       type_vocab_size=a_,       initializer_range=a_,       layer_norm_eps=a_,       pad_token_id=a_,       bos_token_id=a_,       eos_token_id=a_,       **a_,       )
							lowercase__       	=					max_ad_position_embeddings
							lowercase__       	=					coordinate_size
							lowercase__       	=					shape_size
							lowercase__       	=					has_relative_attention_bias
							lowercase__       	=					rel_pos_bins
							lowercase__       	=					max_rel_pos
							lowercase__       	=					has_spatial_attention_bias
							lowercase__       	=					rel_ad_pos_bins
							lowercase__       	=					max_rel_ad_pos
							lowercase__       	=					text_embed
							lowercase__       	=					visual_embed
							lowercase__       	=					input_size
							lowercase__       	=					num_channels
							lowercase__       	=					patch_size
							lowercase__       	=					classifier_dropout
class 					_snake_case      (       lowercase__):
				UpperCamelCase__					:       Dict  		=version.parse("""1.12""")




				@property
				def 				A__   (    self       :    List[Any]    ):
							# 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       :    Dict    ):
							return 1e-5




				@property
				def 				A__   (    self       :    Any    ):
							return 12




				def 				A__   (    self       :    str,       __lowercase       :    List[Any],       __lowercase       :    Any = -1,       __lowercase       :    Optional[Any] = -1,       __lowercase       :    int = False,       __lowercase       :    int = None,       __lowercase       :    Any = 3,       __lowercase       :    Dict = 40,       __lowercase       :    Any = 40,       ):
							setattr(processor.image_processor,       "apply_ocr",       a_    )

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

							# Generate dummy bounding boxes
							lowercase__       	=					[[[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)
							lowercase__       	=					self._generate_dummy_images(a_,       a_,       a_,       a_    )

							lowercase__       	=					dict(
							    processor(
							        a_,       text=a_,       boxes=a_,       return_tensors=a_,       )    )

							return inputs









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
