






'''simple docstring'''







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__						,	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__						,	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__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= box.int().tolist()
														lowerCamelCase__      :			Optional[Any] 						= {
														    """xmin""": xmin,
														    """ymin""": ymin,
														    """xmax""": xmax,
														    """ymax""": ymax,
														}
														return bbox









'''simple docstring'''







import argparse
import os
import re

import packaging.version


_A			:    List[str]					  ='''examples/'''
_A			:    Any					  ={
    '''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(r'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(r'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), r'''\1version="VERSION",'''),
    '''doc''': (re.compile(r'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
_A			:    int					  ={
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
_A			:    int					  ='''README.md'''





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> str:
							with open(UpperCamelCase       ,    """r"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														lowerCamelCase__      :			List[str] 						= f.read()
							lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= REPLACE_PATTERNS[pattern]
							lowerCamelCase__      :			Dict 						= replace.replace("""VERSION"""       ,    UpperCamelCase   )
							lowerCamelCase__      :			str 						= re_pattern.sub(UpperCamelCase       ,    UpperCamelCase   )
							with open(UpperCamelCase       ,    """w"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														f.write(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							for folder, directories, fnames in os.walk(UpperCamelCase   ):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove("""research_projects"""   )
														if "legacy" in directories:
																					directories.remove("""legacy"""   )
														for fname in fnames:
																					if fname.endswith(""".py"""   ):
																												update_version_in_file(os.path.join(UpperCamelCase       ,    UpperCamelCase   )       ,    UpperCamelCase       ,    pattern="""examples"""   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> List[Any]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
							if not patch:
														update_version_in_examples(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[Any]:
							lowerCamelCase__      :			Dict 						= """🤗 Transformers currently provides the following architectures"""
							lowerCamelCase__      :			Dict 						= """1. Want to contribute a new model?"""
							with open(UpperCamelCase       ,    """r"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														lowerCamelCase__      :			int 						= f.readlines()

							# Find the start of the list.
							lowerCamelCase__      :			Optional[int] 						= 0
							while not lines[start_index].startswith(_start_prompt   ):
														start_index += 1
							start_index += 1

							lowerCamelCase__      :			Optional[Any] 						= start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt   ):
														if lines[index].startswith("""1."""   ):
																					lowerCamelCase__      :			List[Any] 						= lines[index].replace(
																					    """https://huggingface.co/docs/transformers/main/model_doc"""       ,    """https://huggingface.co/docs/transformers/model_doc"""       ,    )
														index += 1

							with open(UpperCamelCase       ,    """w"""       ,    encoding="""utf-8"""       ,    newline="""\n"""   ) as f:
														f.writelines(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[Any]:
							with open(REPLACE_FILES["""init"""]       ,    """r"""   ) as f:
														lowerCamelCase__      :			int 						= f.read()
							lowerCamelCase__      :			Optional[Any] 						= REPLACE_PATTERNS["""init"""][0].search(UpperCamelCase   ).groups()[0]
							return packaging.version.parse(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase=False   )   -> List[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= get_version()
							if patch and default_version.is_devrelease:
														raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""   )
							if default_version.is_devrelease:
														lowerCamelCase__      :			List[str] 						= default_version.base_version
							elif patch:
														lowerCamelCase__      :			Any 						= f'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
							else:
														lowerCamelCase__      :			List[Any] 						= f'''{default_version.major}.{default_version.minor + 1}.0'''

							# Now let's ask nicely if that's the right one.
							lowerCamelCase__      :			Any 						= input(f'''Which version are you releasing? [{default_version}]'''   )
							if len(UpperCamelCase   ) == 0:
														lowerCamelCase__      :			Optional[int] 						= default_version

							print(f'''Updating version to {version}.'''   )
							global_version_update(UpperCamelCase       ,    patch=UpperCamelCase   )
							if not patch:
														print("""Cleaning main README, don't forget to run `make fix-copies`."""   )
														clean_main_ref_in_model_list()





def 				SCREAMING_SNAKE_CASE_ ()   -> List[str]:
							lowerCamelCase__      :			Optional[int] 						= get_version()
							lowerCamelCase__      :			Any 						= f'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
							lowerCamelCase__      :			Any 						= current_version.base_version

							# Check with the user we got that right.
							lowerCamelCase__      :			List[Any] 						= input(f'''Which version are we developing now? [{dev_version}]'''   )
							if len(UpperCamelCase   ) == 0:
														lowerCamelCase__      :			Dict 						= dev_version

							print(f'''Updating version to {version}.'''   )
							global_version_update(UpperCamelCase   )
							print("""Cleaning main README, don't forget to run `make fix-copies`."""   )
							clean_main_ref_in_model_list()


if __name__ == "__main__":
			_A			:    List[Any]					  =argparse.ArgumentParser()
			parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
			parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
			_A			:    List[str]					  =parser.parse_args()
			if not args.post_release:
						pre_release_work(patch=args.patch)
			elif args.patch:
						print('''Nothing to do after a patch :-)''')
			else:
						post_release_work()

