






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, ChunkPipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		import torch

		from transformers.modeling_outputs import BaseModelOutput

		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_a      	=  logging.get_logger(__name__)





@add_end_docstrings(snake_case__)
class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase    )

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

							requires_backends(self       ,    'vision'    )
							self.check_model_type(UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if "text_queries" in kwargs:
													_UpperCAmelCase									=			kwargs.pop('text_queries'    )

							if isinstance(UpperCAmelCase       ,    (str, Image.Image)    ):
													_UpperCAmelCase									=			{'image': image, 'candidate_labels': candidate_labels}
							else:
													_UpperCAmelCase									=			image
							_UpperCAmelCase									=			super().__call__(UpperCAmelCase       ,    **UpperCAmelCase    )
							return results

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{}
							if "threshold" in kwargs:
													_UpperCAmelCase									=			kwargs['threshold']
							if "top_k" in kwargs:
													_UpperCAmelCase									=			kwargs['top_k']
							return {}, {}, postprocess_params

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			load_image(inputs['image']    )
							_UpperCAmelCase									=			inputs['candidate_labels']
							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			candidate_labels.split(','    )

							_UpperCAmelCase									=			torch.tensor([[image.height, image.width]]       ,    dtype=torch.intaa    )
							for i, candidate_label in enumerate(UpperCAmelCase    ):
													_UpperCAmelCase									=			self.tokenizer(UpperCAmelCase       ,    return_tensors=self.framework    )
													_UpperCAmelCase									=			self.image_processor(UpperCAmelCase       ,    return_tensors=self.framework    )
													yield {
													    "is_last": i == len(UpperCAmelCase    ) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			model_inputs.pop('target_size'    )
							_UpperCAmelCase									=			model_inputs.pop('candidate_label'    )
							_UpperCAmelCase									=			model_inputs.pop('is_last'    )

							_UpperCAmelCase									=			self.model(**UpperCAmelCase    )

							_UpperCAmelCase									=			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
							return model_outputs

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			[]
							for model_output in model_outputs:
													_UpperCAmelCase									=			model_output['candidate_label']
													_UpperCAmelCase									=			BaseModelOutput(UpperCAmelCase    )
													_UpperCAmelCase									=			self.image_processor.post_process_object_detection(
													    outputs=UpperCAmelCase       ,    threshold=UpperCAmelCase       ,    target_sizes=model_output['target_size']    )[0]

													for index in outputs["scores"].nonzero():
																			_UpperCAmelCase									=			outputs['scores'][index].item()
																			_UpperCAmelCase									=			self._get_bounding_box(outputs['boxes'][index][0]    )

																			_UpperCAmelCase									=			{'score': score, 'label': label, 'box': box}
																			results.append(UpperCAmelCase    )

							_UpperCAmelCase									=			sorted(UpperCAmelCase       ,    key=lambda UpperCAmelCase    : x["score"]       ,    reverse=UpperCAmelCase    )
							if top_k:
													_UpperCAmelCase									=			results[:top_k]

							return results

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if self.framework != "pt":
													raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'    )
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			box.int().tolist()
							_UpperCAmelCase									=			{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox








from __future__ import annotations

import collections
import pprint
from pathlib import Path


def __A    (      __lowerCAmelCase	)->  str:





						"""simple docstring"""



						return "".join(sorted(__lowerCAmelCase	)	)


def __A    (      __lowerCAmelCase	)->  list[str]:





						"""simple docstring"""



						return word_by_signature[signature(__lowerCAmelCase	)]


_a      	=  Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_a      	=  sorted({word.strip().lower() for word in data.splitlines()})

_a      	=  collections.defaultdict(list)
for word in word_list:
		word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
		_a      	=  {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

		with open('''anagrams.txt''', '''w''') as file:
				file.write('''all_anagrams = \n ''')
				file.write(pprint.pformat(all_anagrams))
