






import copy
import re

class 		_snake_case  :
			_lowercase		:    Tuple			     =							'''hp'''
			_lowercase		:    Optional[int]			     =							{}
			_lowercase		:    Union[str, Any]			     =							None






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls		,					a		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= prefix
						SCREAMING_SNAKE_CASE						= defaults
						cls.build_naming_info()






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a		,					a)      ->       Union[str, Any]:
						if len(a) == 0:
									return ""
						SCREAMING_SNAKE_CASE						= None
						if any(char.isdigit() for char in word):
									raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number''')
						if word in info["short_word"]:
									return info["short_word"][word]
						for prefix_len in range(1		,					len(a) + 1):
									SCREAMING_SNAKE_CASE						= word[:prefix_len]
									if prefix in info["reverse_short_word"]:
												continue
									else:
												SCREAMING_SNAKE_CASE						= prefix
												break

						if short_word is None:
									# Paranoid fallback
									def int_to_alphabetic(a):
												SCREAMING_SNAKE_CASE						= ''
												while integer != 0:
															SCREAMING_SNAKE_CASE						= chr(ord('A') + integer % 10) + s
															integer //= 10
												return s

									SCREAMING_SNAKE_CASE						= 0
									while True:
												SCREAMING_SNAKE_CASE						= word + '#' + int_to_alphabetic(a)
												if sword in info["reverse_short_word"]:
															continue
												else:
															SCREAMING_SNAKE_CASE						= sword
															break

						SCREAMING_SNAKE_CASE						= short_word
						SCREAMING_SNAKE_CASE						= word
						return short_word






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a		,					a)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= param_name.split('_')

						SCREAMING_SNAKE_CASE						= [TrialShortNamer.shortname_for_word(a		,					a) for word in words]

						# We try to create a separatorless short name, but if there is a collision we have to fallback
						# to a separated short name
						SCREAMING_SNAKE_CASE						= ['', '_']

						for separator in separators:
									SCREAMING_SNAKE_CASE						= separator.join(a)
									if shortname not in info["reverse_short_param"]:
												SCREAMING_SNAKE_CASE						= shortname
												SCREAMING_SNAKE_CASE						= param_name
												return shortname

						return param_name






			@staticmethod
			def    SCREAMING_SNAKE_CASE__    (       a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= TrialShortNamer.shortname_for_key(a		,					a)
						SCREAMING_SNAKE_CASE						= short_name
						SCREAMING_SNAKE_CASE						= param_name






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls)      ->       List[str]:
						if cls.NAMING_INFO is not None:
									return

						SCREAMING_SNAKE_CASE						= {
						    'short_word': {},
						    'reverse_short_word': {},
						    'short_param': {},
						    'reverse_short_param': {},
						}

						SCREAMING_SNAKE_CASE						= list(cls.DEFAULTS.keys())

						for k in field_keys:
									cls.add_new_param_name(a		,					a)

						SCREAMING_SNAKE_CASE						= info






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls		,					a)      ->       Optional[int]:
						cls.build_naming_info()
						assert cls.PREFIX is not None
						SCREAMING_SNAKE_CASE						= [copy.copy(cls.PREFIX)]

						for k, v in params.items():
									if k not in cls.DEFAULTS:
												raise Exception(f'''You should provide a default value for the param name {k} with value {v}''')
									if v == cls.DEFAULTS[k]:
												# The default value is not added to the name
												continue

									SCREAMING_SNAKE_CASE						= cls.NAMING_INFO['short_param'][k]

									if isinstance(a		,					a):
												SCREAMING_SNAKE_CASE						= 1 if v else 0

									SCREAMING_SNAKE_CASE						= '' if isinstance(a		,					(int, float)) else '-'
									SCREAMING_SNAKE_CASE						= f'''{key}{sep}{v}'''
									name.append(a)

						return "_".join(a)







			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= repr[len(cls.PREFIX) + 1 :]
						if repr == "":
									SCREAMING_SNAKE_CASE						= []
						else:
									SCREAMING_SNAKE_CASE						= repr.split('_')

						SCREAMING_SNAKE_CASE						= {}

						for value in values:
									if "-" in value:
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= value.split('-')
									else:
												SCREAMING_SNAKE_CASE						= re.sub('[0-9.]'		,					''		,					a)
												SCREAMING_SNAKE_CASE						= float(re.sub('[^0-9.]'		,					''		,					a))

									SCREAMING_SNAKE_CASE						= cls.NAMING_INFO['reverse_short_param'][p_k]

									SCREAMING_SNAKE_CASE						= p_v

						for k in cls.DEFAULTS:
									if k not in parameters:
												SCREAMING_SNAKE_CASE						= cls.DEFAULTS[k]

						return parameters












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_		:						str             =    logging.get_logger(__name__)


a_		:						List[Any]             =    Dict[str, Any]
a_		:						Optional[int]             =    List[Prediction]

@add_end_docstrings(A__					)
class 		_snake_case  (  A__					):






			def __init__(       self		,					*a		,					**a)      ->       int:
						super().__init__(*a		,					**a)

						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    SCREAMING_SNAKE_CASE__    (       self		,					**a)      ->       int:
						SCREAMING_SNAKE_CASE						= {}
						if "threshold" in kwargs:
									SCREAMING_SNAKE_CASE						= kwargs['threshold']
						return {}, {}, postprocess_kwargs






			def __call__(       self		,					*a		,					**a)      ->       Union[Predictions, List[Prediction]]:
						return super().__call__(*a		,					**a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= load_image(a)
						SCREAMING_SNAKE_CASE						= torch.IntTensor([[image.height, image.width]])
						SCREAMING_SNAKE_CASE						= self.image_processor(images=[image]		,					return_tensors='pt')
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= self.tokenizer(text=inputs['words']		,					boxes=inputs['boxes']		,					return_tensors='pt')
						SCREAMING_SNAKE_CASE						= target_size
						return inputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= model_inputs.pop('target_size')
						SCREAMING_SNAKE_CASE						= self.model(**a)
						SCREAMING_SNAKE_CASE						= outputs.__class__({'target_size': target_size, **outputs})
						if self.tokenizer is not None:
									SCREAMING_SNAKE_CASE						= model_inputs['bbox']
						return model_outputs






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=0.9)      ->       Dict:
						SCREAMING_SNAKE_CASE						= 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.
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= target_size[0].tolist()

									def unnormalize(a):
												return self._get_bounding_box(
												    torch.Tensor(
												        [
												            (width * bbox[0] / 1000),
												            (height * bbox[1] / 1000),
												            (width * bbox[2] / 1000),
												            (height * bbox[3] / 1000),
												        ]))

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

									SCREAMING_SNAKE_CASE						= scores.tolist()
									SCREAMING_SNAKE_CASE						= [self.model.config.idalabel[label.item()] for label in labels]
									SCREAMING_SNAKE_CASE						= [self._get_bounding_box(a) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									SCREAMING_SNAKE_CASE						= ['score', 'label', 'box']
									SCREAMING_SNAKE_CASE						= [
									    dict(zip(a		,					a))
									    for vals in zip(raw_annotation['scores']		,					raw_annotation['labels']		,					raw_annotation['boxes'])
									]

						return annotation







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict[str, int]:
						if self.framework != "pt":
									raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= box.int().tolist()
						SCREAMING_SNAKE_CASE						= {
						    'xmin': xmin,
						    'ymin': ymin,
						    'xmax': xmax,
						    'ymax': ymax,
						}
						return bbox




