


"""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

__snake_case         =					logging.get_logger(__name__)


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







@add_end_docstrings(__lowerCamelCase			)
class _lowerCAmelCase       (   __lowerCamelCase			):







			def __init__(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	Union[str, Any]:

							'''simple docstring'''




							super().__init__(*SCREAMING_SNAKE_CASE_		,			**SCREAMING_SNAKE_CASE_		)

							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		,			**UpperCamelCase__		)			->	int:

							'''simple docstring'''




							snake_case					:   int         =					{}
							if "threshold" in kwargs:
											snake_case					:   Dict         =					kwargs["threshold"]
							return {}, {}, postprocess_kwargs







			def __call__(			self		,			*UpperCamelCase__		,			**UpperCamelCase__		)			->	str:

							'''simple docstring'''




							return super().__call__(*SCREAMING_SNAKE_CASE_		,			**SCREAMING_SNAKE_CASE_		)







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[int]:

							'''simple docstring'''




							snake_case					:   Optional[int]         =					load_image(SCREAMING_SNAKE_CASE_		)
							snake_case					:   Dict         =					torch.IntTensor([[image.height, image.width]]		)
							snake_case					:   Any         =					self.image_processor(images=[image]		,			return_tensors="pt"		)
							if self.tokenizer is not None:
											snake_case					:   Any         =					self.tokenizer(text=inputs["words"]		,			boxes=inputs["boxes"]		,			return_tensors="pt"		)
							snake_case					:   Optional[Any]         =					target_size
							return inputs







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Union[str, Any]:

							'''simple docstring'''




							snake_case					:   Union[str, Any]         =					model_inputs.pop("target_size"		)
							snake_case					:   Any         =					self.model(**SCREAMING_SNAKE_CASE_		)
							snake_case					:   Tuple         =					outputs.__class__({"target_size": target_size, **outputs}		)
							if self.tokenizer is not None:
											snake_case					:   List[str]         =					model_inputs["bbox"]
							return model_outputs







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__=0.9		)			->	Any:

							'''simple docstring'''




							snake_case					:   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.
											snake_case      ,snake_case					:   List[str]         =					target_size[0].tolist()

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

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

											snake_case					:   Optional[int]         =					scores.tolist()
											snake_case					:   int         =					[self.model.config.idalabel[label.item()] for label in labels]
											snake_case					:   int         =					[self._get_bounding_box(SCREAMING_SNAKE_CASE_		) for box in boxes]

											# {"scores": [...], ...} --> [{"score":x, ...}, ...]
											snake_case					:   List[Any]         =					["score", "label", "box"]
											snake_case					:   int         =					[
											    dict(zip(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		)		)
											    for vals in zip(raw_annotation["scores"]		,			raw_annotation["labels"]		,			raw_annotation["boxes"]		)
											]

							return annotation







			def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Dict:

							'''simple docstring'''




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









"""simple docstring"""



# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__snake_case         =					subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__snake_case         =					(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

__snake_case         =					"""|""".join(sys.argv[1:])
__snake_case         =					re.compile(RF'''^({joined_dirs}).*?\.py$''')

__snake_case         =					[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





