





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

__lowercase       :str       =  logging.get_logger(__name__)







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  Any     ):





						'''simple docstring'''


						return [
						    int(1_000 * (box[0] / width)     ),
						    int(1_000 * (box[1] / height)     ),
						    int(1_000 * (box[2] / width)     ),
						    int(1_000 * (box[3] / height)     ),
						]







def       UpperCAmelCase							(     _lowerCamelCase      :  np.ndarray			,   _lowerCamelCase      :  Optional[str]			,   _lowerCamelCase      :  Optional[str]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				int   =					to_pil_image(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pil_image.size
						SCREAMING_SNAKE_CASE__     :				Tuple   =					pytesseract.image_to_data(_lowerCamelCase			,   lang=_lowerCamelCase			,   output_type="dict"			,   config=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Dict   =					data["text"], data["left"], data["top"], data["width"], data["height"]

						# filter empty words and corresponding coordinates
						SCREAMING_SNAKE_CASE__     :				List[str]   =					[idx for idx, word in enumerate(_lowerCamelCase     ) if not word.strip()]
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[word for idx, word in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				int   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				Dict   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]
						SCREAMING_SNAKE_CASE__     :				int   =					[coord for idx, coord in enumerate(_lowerCamelCase     ) if idx not in irrelevant_indices]

						# turn coordinates into (left, top, left+width, top+height) format
						SCREAMING_SNAKE_CASE__     :				Tuple   =					[]
						for x, y, w, h in zip(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     ):
												SCREAMING_SNAKE_CASE__     :				List[str]   =					[x, y, x + w, y + h]
												actual_boxes.append(_lowerCamelCase     )

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

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

						return words, normalized_boxes



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["pixel_values"]

							def __init__(      self				:     int							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     bool = True							,	a				:     float = 1 / 2_55							,	a				:     bool = True							,	a				:     Union[float, Iterable[float]] = None							,	a				:     Union[float, Iterable[float]] = None							,	a				:     bool = True							,	a				:     Optional[str] = None							,	a				:     Optional[str] = ""							,	**a				:     Optional[int]							,	)	->None:
													super().__init__(**a    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					size if size is not None else {"height": 2_24, "width": 2_24}
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					get_size_dict(a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_resize
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					size
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					resample
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					rescale_value
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				int   =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_std if image_std is not None else IMAGENET_STANDARD_STD
													SCREAMING_SNAKE_CASE__     :				str   =					apply_ocr
													SCREAMING_SNAKE_CASE__     :				int   =					ocr_lang
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tesseract_config

							def      A_  (      self				:     Any							,	a				:     np.ndarray							,	a				:     Dict[str, int]							,	a				:     PILImageResampling = PILImageResampling.BILINEAR							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[int]							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					get_size_dict(a    )
													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()}"""    )
													SCREAMING_SNAKE_CASE__     :				Any   =					(size["height"], size["width"])
													return resize(a							,	size=a							,	resample=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Tuple							,	a				:     np.ndarray							,	a				:     Union[int, float]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Any							,	)	->np.ndarray:
													return rescale(a							,	scale=a							,	data_format=a							,	**a    )

							def      A_  (      self				:     Optional[int]							,	a				:     np.ndarray							,	a				:     Union[float, Iterable[float]]							,	a				:     Union[float, Iterable[float]]							,	a				:     Optional[Union[str, ChannelDimension]] = None							,	**a				:     Optional[Any]							,	)	->np.ndarray:
													return normalize(a							,	mean=a							,	std=a							,	data_format=a							,	**a    )






							def      A_  (      self				:     Any							,	a				:     ImageInput							,	a				:     bool = None							,	a				:     Dict[str, int] = None							,	a				:     List[str]=None							,	a				:     bool = None							,	a				:     float = None							,	a				:     bool = None							,	a				:     Union[float, Iterable[float]] = None							,	a				:     Union[float, Iterable[float]] = None							,	a				:     bool = None							,	a				:     Optional[str] = None							,	a				:     Optional[str] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     ChannelDimension = ChannelDimension.FIRST							,	**a				:     int							,	)	->PIL.Image.Image:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_resize if do_resize is not None else self.do_resize
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size if size is not None else self.size
													SCREAMING_SNAKE_CASE__     :				Any   =					get_size_dict(a    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					resample if resample is not None else self.resample
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_rescale if do_rescale is not None else self.do_rescale
													SCREAMING_SNAKE_CASE__     :				int   =					rescale_factor if rescale_factor is not None else self.rescale_factor
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_normalize if do_normalize is not None else self.do_normalize
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_mean if image_mean is not None else self.image_mean
													SCREAMING_SNAKE_CASE__     :				Any   =					image_std if image_std is not None else self.image_std
													SCREAMING_SNAKE_CASE__     :				str   =					apply_ocr if apply_ocr is not None else self.apply_ocr
													SCREAMING_SNAKE_CASE__     :				List[str]   =					ocr_lang if ocr_lang is not None else self.ocr_lang
													SCREAMING_SNAKE_CASE__     :				List[str]   =					tesseract_config if tesseract_config is not None else self.tesseract_config

													SCREAMING_SNAKE_CASE__     :				str   =					make_list_of_images(a    )

													if not valid_images(a    ):
																			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."    )

													if do_rescale and rescale_factor is None:
																			raise ValueError("Rescale factor must be specified if do_rescale is True."    )

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("If do_normalize is True, image_mean and image_std must be specified."    )

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

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																			requires_backends(self							,	"pytesseract"    )
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[]
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in images:
																									SCREAMING_SNAKE_CASE__     :				Dict   =					apply_tesseract(a							,	a							,	a    )
																									words_batch.append(a    )
																									boxes_batch.append(a    )

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

													if do_rescale:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[self.rescale(image=a							,	scale=a    ) for image in images]

													if do_normalize:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					[self.normalize(image=a							,	mean=a							,	std=a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				Any   =					[to_channel_dimension_format(a							,	a    ) for image in images]

													SCREAMING_SNAKE_CASE__     :				List[str]   =					BatchFeature(data={"pixel_values": images}							,	tensor_type=a    )

													if apply_ocr:
																			SCREAMING_SNAKE_CASE__     :				Any   =					words_batch
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					boxes_batch
													return data






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__lowercase       :Tuple       =  "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__lowercase       :str       =  "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__lowercase       :List[Any]       =  "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,							_KWARGS_DESCRIPTION							)
class    _a     (				datasets.Metric							):






							"""simple docstring"""

							def      A_  (      self				:     List[Any]    )	->MetricInfo:
													return datasets.MetricInfo(
													    description=_DESCRIPTION							,	citation=_CITATION							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
													        {
													            "predictions": datasets.Sequence(datasets.Value("string"							,	id="token"    )							,	id="sequence"    ),
													            "references": datasets.Sequence(
													                datasets.Sequence(datasets.Value("string"							,	id="token"    )							,	id="sequence"    )							,	id="references"    ),
													        }    )							,	)






							def      A_  (      self				:     str							,	a				:     List[List[List[str]]]							,	a				:     List[List[str]]							,	a				:     int = 1							,	a				:     int = 4							,	)	->Dict[str, float]:
													return {
													    "google_bleu": gleu_score.corpus_gleu(
													        list_of_references=a							,	hypotheses=a							,	min_len=a							,	max_len=a    )
													}