
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

__a         =   logging.get_logger(__name__)




def 							a		(				snake_case__:   List[str]		,  snake_case__:   int		,  snake_case__:   str   ):


				'''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 							a		(				snake_case__:   np.ndarray		,  snake_case__:   Optional[str]		,  snake_case__:   Optional[str] = None   ):


				'''simple docstring'''
				lowercase_	    =   tesseract_config if tesseract_config is not None else ''''''

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

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

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

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

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

				return words, normalized_boxes


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      ['pixel_values']
	def __init__(			self    :     int			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = ""			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   resample
					lowercase_	    =   apply_ocr
					lowercase_	    =   ocr_lang
					lowercase_	    =   tesseract_config
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     int			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			)
					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()}'''			)
					lowercase_	    =   (size['''height'''], size['''width'''])
					return resize(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)




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

					lowercase_	    =   make_list_of_images(SCREAMING_SNAKE_CASE_			)

					if not valid_images(SCREAMING_SNAKE_CASE_			):
									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.
					lowercase_	    =   [to_numpy_array(SCREAMING_SNAKE_CASE_			) for image in images]

					if apply_ocr:
									requires_backends(self			,				'''pytesseract'''			)
									lowercase_	    =   []
									lowercase_	    =   []
									for image in images:
													lowercase_    ,      lowercase_	    =   apply_tesseract(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
													words_batch.append(SCREAMING_SNAKE_CASE_			)
													boxes_batch.append(SCREAMING_SNAKE_CASE_			)

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

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

					lowercase_	    =   BatchFeature(data={'''pixel_values''': images}			,				tensor_type=SCREAMING_SNAKE_CASE_			)

					if apply_ocr:
									lowercase_	    =   words_batch
									lowercase_	    =   boxes_batch
					return data







'''simple docstring'''



import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_UpperCamelCase						:  Optional[Any]							       =				"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_UpperCamelCase						:  Any							       =				"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_UpperCamelCase						:  Dict							       =				"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"



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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      if version.parse(scb.__version__	) < version.parse('1.4.12'	):
          raise ImportWarning(
              'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
              'You can install it with `pip install "sacrebleu>=1.4.12"`.'	)
      return datasets.MetricInfo(
          description=_DESCRIPTION      , citation=_CITATION      , homepage='http://www.cs.umd.edu/~snover/tercom/'      , inputs_description=_KWARGS_DESCRIPTION      , features=datasets.Features(
              {
                  'predictions': datasets.Value('string'      , id='sequence'	),
                  'references': datasets.Sequence(datasets.Value('string'      , id='sequence'	)      , id='references'	),
              }	)      , codebase_urls=['https://github.com/mjpost/sacreBLEU#ter']      , reference_urls=[
              'https://github.com/jhclark/tercom',
          ]      , )


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE = False      , _SCREAMING_SNAKE_CASE = False      , _SCREAMING_SNAKE_CASE = False      , _SCREAMING_SNAKE_CASE = False      , ):





      '''simple docstring'''

      lowerCAmelCase 						=							len(references[0]	)
      if any(len(_SCREAMING_SNAKE_CASE	) != references_per_prediction for refs in references	):
          raise ValueError('Sacrebleu requires the same number of references for each prediction'	)
      lowerCAmelCase 						=							[[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE	)]

      lowerCAmelCase 						=							TER(
          normalized=_SCREAMING_SNAKE_CASE      , no_punct=_SCREAMING_SNAKE_CASE      , asian_support=_SCREAMING_SNAKE_CASE      , case_sensitive=_SCREAMING_SNAKE_CASE      , )
      lowerCAmelCase 						=							sb_ter.corpus_score(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

      return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





