


'''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	=logging.get_logger(__name__)


__A	=Dict[str, Any]
__A	=List[Prediction]


@add_end_docstrings(a__	)
class 		_snake_case					(     a__	):




					def __init__(      self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(*_lowerCamelCase							,							**_lowerCamelCase)

								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 				snake_case__						(      self							,							**_lowerCamelCase):
								UpperCAmelCase__  :					int        =     {}
								if "threshold" in kwargs:
											UpperCAmelCase__  :					List[str]        =     kwargs["""threshold"""]
								return {}, {}, postprocess_kwargs




					def __call__(      self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return super().__call__(*_lowerCamelCase							,							**_lowerCamelCase)




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




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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=0.9):
								UpperCAmelCase__  :					Optional[Any]        =     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.
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     target_size[0].tolist()

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

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

											UpperCAmelCase__  :					Tuple        =     scores.tolist()
											UpperCAmelCase__  :					int        =     [self.model.config.idalabel[label.item()] for label in labels]
											UpperCAmelCase__  :					int        =     [self._get_bounding_box(_lowerCamelCase) for box in boxes]

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

								return annotation




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.framework != "pt":
											raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""")
								UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__   ,       UpperCAmelCase__  :					Union[str, Any]        =     box.int().tolist()
								UpperCAmelCase__  :					Dict        =     {
								    """xmin""": xmin,
								    """ymin""": ymin,
								    """xmax""": xmax,
								    """ymax""": ymax,
								}
								return bbox



'''simple docstring'''



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class 		_snake_case					(     yaml.SafeLoader	):




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     [self.constructed_objects[key_node] for key_node, _ in node.value]
								UpperCAmelCase__  :					List[str]        =     [tuple(_lowerCamelCase) if isinstance(_lowerCamelCase							,							_lowerCamelCase) else key for key in keys]
								UpperCAmelCase__  :					List[str]        =     Counter(_lowerCamelCase)
								UpperCAmelCase__  :					Any        =     [key for key in counter if counter[key] > 1]
								if duplicate_keys:
											raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}''')




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase=False):
								UpperCAmelCase__  :					List[str]        =     super().construct_mapping(_lowerCamelCase							,							deep=_lowerCamelCase)
								self._check_no_duplicates_on_constructed_node(_lowerCamelCase)
								return mapping







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Tuple        =     list(readme_content.splitlines()					)
			if full_content and full_content[0] == "---" and "---" in full_content[1:]:
						UpperCAmelCase__  :					Dict        =     full_content[1:].index("""---"""					) + 1
						UpperCAmelCase__  :					List[Any]        =     """\n""".join(full_content[1:sep_idx]					)
						return yamlblock, "\n".join(full_content[sep_idx + 1 :]					)

			return None, "\n".join(UpperCamelCase__					)


class 		_snake_case					(     a__	):
					# class attributes
					lowerCAmelCase      :str   =					{'''train_eval_index'''}  # train-eval-index in the YAML metadata




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase):
								with open(_lowerCamelCase							,							encoding="""utf-8""") as readme_file:
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     _split_yaml_from_readme(readme_file.read())
								if yaml_string is not None:
											return cls.from_yaml_string(_lowerCamelCase)
								else:
											return cls()




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if path.exists():
											with open(_lowerCamelCase							,							encoding="""utf-8""") as readme_file:
														UpperCAmelCase__  :					int        =     readme_file.read()
								else:
											UpperCAmelCase__  :					List[str]        =     None
								UpperCAmelCase__  :					Union[str, Any]        =     self._to_readme(_lowerCamelCase)
								with open(_lowerCamelCase							,							"""w"""							,							encoding="""utf-8""") as readme_file:
											readme_file.write(_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase = None):
								if readme_content is not None:
											UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     _split_yaml_from_readme(_lowerCamelCase)
											UpperCAmelCase__  :					str        =     """---\n""" + self.to_yaml_string() + """---\n""" + content
								else:
											UpperCAmelCase__  :					Optional[int]        =     """---\n""" + self.to_yaml_string() + """---\n"""
								return full_content




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     yaml.load(_lowerCamelCase							,							Loader=_NoDuplicateSafeLoader) or {}

								# Convert the YAML keys to DatasetMetadata fields
								UpperCAmelCase__  :					List[str]        =     {
								    (key.replace("""-"""							,							"""_""") if key.replace("""-"""							,							"""_""") in cls._FIELDS_WITH_DASHES else key): value
								    for key, value in metadata_dict.items()
								}
								return cls(**_lowerCamelCase)




					def 				snake_case__						(      self):
								return yaml.safe_dump(
								    {
								        (key.replace("""_"""							,							"""-""") if key in self._FIELDS_WITH_DASHES else key): value
								        for key, value in self.items()
								    }							,							sort_keys=_lowerCamelCase							,							allow_unicode=_lowerCamelCase							,							encoding="""utf-8"""							,							).decode("""utf-8""")

__A	={
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		__A	=ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
		ap.add_argument('readme_filepath')
		__A	=ap.parse_args()

		__A	=Path(args.readme_filepath)
		__A	=DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)