


"""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

_lowerCamelCase     =  logging.get_logger(__name__)


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


@add_end_docstrings(lowercase   )
class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								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   lowerCAmelCase__		(						self       ,		**UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				{}
								if "threshold" in kwargs:
												SCREAMING_SNAKE_CASE__     							=				kwargs["threshold"]
								return {}, {}, postprocess_kwargs



				def __call__(						self       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								return super().__call__(*__a       ,		**__a     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=0.9     ):
								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(UpperCAmelCase__     ):
																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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								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




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				ArgumentParser("Accelerate CLI tool"						,					usage="accelerate <command> [<args>]"						,					allow_abbrev=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				parser.add_subparsers(help="accelerate command helpers"  )

				# Register commands
				get_config_parser(subparsers=lowerCamelCase_  )
				env_command_parser(subparsers=lowerCamelCase_  )
				launch_command_parser(subparsers=lowerCamelCase_  )
				tpu_command_parser(subparsers=lowerCamelCase_  )
				test_command_parser(subparsers=lowerCamelCase_  )

				# Let's go
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()

				if not hasattr(lowerCamelCase_						,					"func"  ):
								parser.print_help()
								exit(1  )

				# Run
				args.func(lowerCamelCase_  )


if __name__ == "__main__":
	main()
