'''simple docstring'''




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

__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   int ,  _A:   str ,  _A:   int					)       ->      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 							snake_case__				(     _A:   int ,  _A:   str ,  _A:   Optional[Any] = None					)       ->      List[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    tesseract_config if tesseract_config is not None else """"""

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

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

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

		# finally, normalize the bounding boxes
		lowerCAmelCase     =    []
		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__(   A__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ""			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 224, """width""": 224}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)

					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    apply_ocr
					lowerCAmelCase     =    ocr_lang
					lowerCAmelCase     =    tesseract_config



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    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()}")
					lowerCAmelCase     =    (size["""height"""], size["""width"""])
					return resize(__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    apply_ocr if apply_ocr is not None else self.apply_ocr
					lowerCAmelCase     =    ocr_lang if ocr_lang is not None else self.ocr_lang
					lowerCAmelCase     =    tesseract_config if tesseract_config is not None else self.tesseract_config

					lowerCAmelCase     =    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.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

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

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

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

					lowerCAmelCase     =    BatchFeature(data={"""pixel_values""": images}			,       tensor_type=__lowerCAmelCase)

					if apply_ocr:
							lowerCAmelCase     =    words_batch
							lowerCAmelCase     =    boxes_batch
					return data












from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								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/jitsi/jiwer/'''],    reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




