






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from ..image_utils import load_image


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

_A			:    Any					  =logging.get_logger(__name__)


_A			:    Tuple					  =Dict[str, Any]
_A			:    str					  =List[Prediction]

@add_end_docstrings(_lowercase	)
class _lowercase			(       _lowercase	):




							def __init__(							self:       str    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       int  ):
														super().__init__(*UpperCamelCase__    ,							**UpperCamelCase__  )

														if self.framework == "tf":
																					raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''  )

														requires_backends(self    ,							"""vision"""  )
														self.check_model_type(
														    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()  )  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							**UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			Any 						= {}
														if "threshold" in kwargs:
																					lowerCamelCase__      :			Dict 						= kwargs["""threshold"""]
														return {}, {}, postprocess_kwargs




							def __call__(							self:       int    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       int  ):
														return super().__call__(*UpperCamelCase__    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]  ):
														lowerCamelCase__      :			List[str] 						= load_image(UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= torch.IntTensor([[image.height, image.width]]  )
														lowerCamelCase__      :			Tuple 						= self.image_processor(images=[image]    ,							return_tensors="""pt"""  )
														if self.tokenizer is not None:
																					lowerCamelCase__      :			Any 						= self.tokenizer(text=inputs["""words"""]    ,							boxes=inputs["""boxes"""]    ,							return_tensors="""pt"""  )
														lowerCamelCase__      :			int 						= target_size
														return inputs




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			Optional[int] 						= model_inputs.pop("""target_size"""  )
														lowerCamelCase__      :			Optional[int] 						= self.model(**UpperCamelCase__  )
														lowerCamelCase__      :			Optional[int] 						= outputs.__class__({"""target_size""": target_size, **outputs}  )
														if self.tokenizer is not None:
																					lowerCamelCase__      :			Optional[Any] 						= model_inputs["""bbox"""]
														return model_outputs




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Optional[int]=0.9  ):
														lowerCamelCase__      :			Optional[int] 						= model_outputs["""target_size"""]
														if self.tokenizer is not None:
																					# This is a LayoutLMForTokenClassification variant.
																					# The OCR got the boxes and the model classified the words.
																					lowerCamelCase__      :			Any 						= target_size[0].tolist()

																					def unnormalize(UpperCamelCase__:       Tuple  ):
																												return self._get_bounding_box(
																												    torch.Tensor(
																												        [
																												            (width * bbox[0] / 1_000),
																												            (height * bbox[1] / 1_000),
																												            (width * bbox[2] / 1_000),
																												            (height * bbox[3] / 1_000),
																												        ]  )  )

																					lowerCamelCase__      :			Any 						= model_outputs["""logits"""].squeeze(0  ).softmax(dim=-1  ).max(dim=-1  )
																					lowerCamelCase__      :			Dict 						= [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
																					lowerCamelCase__      :			List[str] 						= [unnormalize(UpperCamelCase__  ) for bbox in model_outputs["""bbox"""].squeeze(0  )]
																					lowerCamelCase__      :			Union[str, Any] 						= ["""score""", """label""", """box"""]
																					lowerCamelCase__      :			Dict 						= [dict(zip(UpperCamelCase__    ,							UpperCamelCase__  )  ) for vals in zip(scores.tolist()    ,							UpperCamelCase__    ,							UpperCamelCase__  ) if vals[0] > threshold]
														else:
																					# This is a regular ForObjectDetectionModel
																					lowerCamelCase__      :			str 						= self.image_processor.post_process_object_detection(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			Tuple 						= raw_annotations[0]
																					lowerCamelCase__      :			str 						= raw_annotation["""scores"""]
																					lowerCamelCase__      :			List[Any] 						= raw_annotation["""labels"""]
																					lowerCamelCase__      :			List[str] 						= raw_annotation["""boxes"""]

																					lowerCamelCase__      :			Any 						= scores.tolist()
																					lowerCamelCase__      :			Tuple 						= [self.model.config.idalabel[label.item()] for label in labels]
																					lowerCamelCase__      :			List[str] 						= [self._get_bounding_box(UpperCamelCase__  ) for box in boxes]

																					# {"scores": [...], ...} --> [{"score":x, ...}, ...]
																					lowerCamelCase__      :			Dict 						= ["""score""", """label""", """box"""]
																					lowerCamelCase__      :			Optional[int] 						= [
																					    dict(zip(UpperCamelCase__    ,							UpperCamelCase__  )  )
																					    for vals in zip(raw_annotation["""scores"""]    ,							raw_annotation["""labels"""]    ,							raw_annotation["""boxes"""]  )
																					]

														return annotation




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       "torch.Tensor"  ):
														if self.framework != "pt":
																					raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""  )
														lowerCamelCase__      :			Dict 						= box.int().tolist()
														lowerCamelCase__      :			Optional[Any] 						= {
														    """xmin""": xmin,
														    """ymin""": ymin,
														    """xmax""": xmax,
														    """ymax""": ymax,
														}
														return bbox









'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			:    str					  =logging.get_logger(__name__)

_A			:    int					  ={
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """roc_bert"""




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any=30_522    ,							UpperCamelCase__:       Optional[Any]=768    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       Tuple=12    ,							UpperCamelCase__:       Tuple=3_072    ,							UpperCamelCase__:       str="gelu"    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       List[str]=0.1    ,							UpperCamelCase__:       Dict=512    ,							UpperCamelCase__:       str=2    ,							UpperCamelCase__:       str=0.02    ,							UpperCamelCase__:       Tuple=1e-12    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Union[str, Any]=0    ,							UpperCamelCase__:       List[Any]="absolute"    ,							UpperCamelCase__:       Any=None    ,							UpperCamelCase__:       Any=True    ,							UpperCamelCase__:       Optional[int]=True    ,							UpperCamelCase__:       Union[str, Any]=768    ,							UpperCamelCase__:       int=910    ,							UpperCamelCase__:       Tuple=512    ,							UpperCamelCase__:       int=24_858    ,							UpperCamelCase__:       Optional[Any]=True    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Optional[Any] 						= vocab_size
														lowerCamelCase__      :			Tuple 						= max_position_embeddings
														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			int 						= num_hidden_layers
														lowerCamelCase__      :			Tuple 						= num_attention_heads
														lowerCamelCase__      :			Any 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			str 						= hidden_dropout_prob
														lowerCamelCase__      :			Dict 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			Tuple 						= type_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= layer_norm_eps
														lowerCamelCase__      :			List[Any] 						= use_cache
														lowerCamelCase__      :			Tuple 						= enable_pronunciation
														lowerCamelCase__      :			Union[str, Any] 						= enable_shape
														lowerCamelCase__      :			Union[str, Any] 						= pronunciation_embed_dim
														lowerCamelCase__      :			Any 						= pronunciation_vocab_size
														lowerCamelCase__      :			int 						= shape_embed_dim
														lowerCamelCase__      :			Tuple 						= shape_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= concat_input
														lowerCamelCase__      :			str 						= position_embedding_type
														lowerCamelCase__      :			Dict 						= classifier_dropout
														super().__init__(pad_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

