




import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase		:				List[Any]       			=		'\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

__lowerCAmelCase		:				List[Any]       			=		'\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

__lowerCAmelCase		:				Optional[Any]       			=		'\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,       _KWARGS_DESCRIPTION   )
class     snake_case__							(datasets.Metric   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Tuple:
							return datasets.MetricInfo(
							    description=_DESCRIPTION					,      citation=_CITATION					,      inputs_description=_KWARGS_DESCRIPTION					,      features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"					,      id="sequence"							),
							            "references": datasets.Value("string"					,      id="sequence"							),
							        }							)					,      codebase_urls=["https://github.com/google-research/google-research/tree/master/rouge"]					,      reference_urls=[
							        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
							        "https://github.com/google-research/google-research/tree/master/rouge",
							    ]					,      )





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     int=None					,      __lowerCamelCase				:     Tuple=True					,      __lowerCamelCase				:     Optional[int]=False							)					->     List[str]:
							if rouge_types is None:
										a    =			["rouge1", "rouge2", "rougeL", "rougeLsum"]

							a    =			rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase					,      use_stemmer=__lowerCamelCase							)
							if use_aggregator:
										a    =			scoring.BootstrapAggregator()
							else:
										a    =			[]

							for ref, pred in zip(__lowerCamelCase					,      __lowerCamelCase							):
										a    =			scorer.score(__lowerCamelCase					,      __lowerCamelCase							)
										if use_aggregator:
													aggregator.add_scores(__lowerCamelCase							)
										else:
													scores.append(__lowerCamelCase							)

							if use_aggregator:
										a    =			aggregator.aggregate()
							else:
										a    =			{}
										for key in scores[0]:
													a    =			[score[key] for score in scores]

							return result












from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
		import PIL

# soft dependency
if is_pytesseract_available():
		import pytesseract

__lowerCAmelCase		:				List[str]       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Dict,  A		:					int,  A		:					Optional[int]   ):






			'''simple docstring'''







			return [
			    int(1000 * (box[0] / width)   ),
			    int(1000 * (box[1] / height)   ),
			    int(1000 * (box[2] / width)   ),
			    int(1000 * (box[3] / height)   ),
			]




def 			__magic_name__				( A		:					np.ndarray,  A		:					Optional[str],  A		:					Optional[str] = None   ):






			'''simple docstring'''







			a    =			tesseract_config if tesseract_config is not None else ""

			# apply OCR
			a    =			to_pil_image(A   )
			a    ,		a    =			pil_image.size
			a    =			pytesseract.image_to_data(A,  lang=A,  output_type="dict",  config=A   )
			a    ,		a    ,		a    ,		a    ,		a    =			data["text"], data["left"], data["top"], data["width"], data["height"]

			# filter empty words and corresponding coordinates
			a    =			[idx for idx, word in enumerate(A   ) if not word.strip()]
			a    =			[word for idx, word in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]
			a    =			[coord for idx, coord in enumerate(A   ) if idx not in irrelevant_indices]

			# turn coordinates into (left, top, left+width, top+height) format
			a    =			[]
			for x, y, w, h in zip(A,  A,  A,  A   ):
						a    =			[x, y, x + w, y + h]
						actual_boxes.append(A   )

			# finally, normalize the bounding boxes
			a    =			[]
			for box in actual_boxes:
						normalized_boxes.append(normalize_box(A,  A,  A   )   )

			assert len(A   ) == len(A   ), "Not as many words as there are bounding boxes"

			return words, normalized_boxes






class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					["""pixel_values"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     bool = True					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = ""					,      **__lowerCamelCase				:     Tuple					,      )					->     None:
							super().__init__(**__lowerCamelCase							)
							a    =			size if size is not None else {"height": 2_24, "width": 2_24}
							a    =			get_size_dict(__lowerCamelCase							)

							a    =			do_resize
							a    =			size
							a    =			resample
							a    =			apply_ocr
							a    =			ocr_lang
							a    =			tesseract_config





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     np.ndarray					,      __lowerCamelCase				:     Dict[str, int]					,      __lowerCamelCase				:     PILImageResampling = PILImageResampling.BILINEAR					,      __lowerCamelCase				:     Optional[Union[str, ChannelDimension]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     np.ndarray:
							a    =			get_size_dict(__lowerCamelCase							)
							if "height" not in size or "width" not in size:
										raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
							a    =			(size["height"], size["width"])
							return resize(__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase					,      data_format=__lowerCamelCase					,      **__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     ImageInput					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Dict[str, int] = None					,      __lowerCamelCase				:     PILImageResampling = None					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[str] = None					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase				:     ChannelDimension = ChannelDimension.FIRST					,      **__lowerCamelCase				:     Optional[Any]					,      )					->     PIL.Image.Image:
							a    =			do_resize if do_resize is not None else self.do_resize
							a    =			size if size is not None else self.size
							a    =			get_size_dict(__lowerCamelCase							)
							a    =			resample if resample is not None else self.resample
							a    =			apply_ocr if apply_ocr is not None else self.apply_ocr
							a    =			ocr_lang if ocr_lang is not None else self.ocr_lang
							a    =			tesseract_config if tesseract_config is not None else self.tesseract_config

							a    =			make_list_of_images(__lowerCamelCase							)

							if not valid_images(__lowerCamelCase							):
										raise ValueError(
										    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
										    "torch.Tensor, tf.Tensor or jax.ndarray."							)

							if do_resize and size is None:
										raise ValueError("Size must be specified if do_resize is True."							)

							# All transformations expect numpy arrays.
							a    =			[to_numpy_array(__lowerCamelCase							) for image in images]

							if apply_ocr:
										requires_backends(self					,      "pytesseract"							)
										a    =			[]
										a    =			[]
										for image in images:
													a    ,		a    =			apply_tesseract(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase							)
													words_batch.append(__lowerCamelCase							)
													boxes_batch.append(__lowerCamelCase							)

							if do_resize:
										a    =			[self.resize(image=__lowerCamelCase					,      size=__lowerCamelCase					,      resample=__lowerCamelCase							) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							a    =			[flip_channel_order(__lowerCamelCase							) for image in images]
							a    =			[to_channel_dimension_format(__lowerCamelCase					,      __lowerCamelCase							) for image in images]

							a    =			BatchFeature(data={"pixel_values": images}					,      tensor_type=__lowerCamelCase							)

							if apply_ocr:
										a    =			words_batch
										a    =			boxes_batch
							return data






