

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

lowerCAmelCase__	   = logging.get_logger(__name__)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      Tuple:




				'''simple docstring'''





				return [
				    int(1_0_0_0 * (box[0] / width)      ),
				    int(1_0_0_0 * (box[1] / height)      ),
				    int(1_0_0_0 * (box[2] / width)      ),
				    int(1_0_0_0 * (box[3] / height)      ),
				]



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							np.ndarray	,       SCREAMING_SNAKE_CASE_:							Optional[str]	,       SCREAMING_SNAKE_CASE_:							Optional[str]      )			->      str:




				'''simple docstring'''





				A__        =			to_pil_image(SCREAMING_SNAKE_CASE_      )
				A__    ,				A__        =			pil_image.size
				A__        =			pytesseract.image_to_data(SCREAMING_SNAKE_CASE_	,       lang=SCREAMING_SNAKE_CASE_	,       output_type="dict"	,       config=SCREAMING_SNAKE_CASE_      )
				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(SCREAMING_SNAKE_CASE_      ) if not word.strip()]
				A__        =			[word for idx, word in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]
				A__        =			[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_      ) if idx not in irrelevant_indices]

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

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

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

				return words, normalized_boxes





class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       ['pixel_values']


			def __init__(    self      ,      lowercase = True      ,      lowercase = None      ,      lowercase = PILImageResampling.BILINEAR      ,      lowercase = True      ,      lowercase = 1 / 255      ,      lowercase = True      ,      lowercase = None      ,      lowercase = None      ,      lowercase = True      ,      lowercase = None      ,      lowercase = ""      ,      **lowercase      ,      )  ->						None:
							'''simple docstring'''






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

							A__        =			do_resize
							A__        =			size
							A__        =			resample
							A__        =			do_rescale
							A__        =			rescale_value
							A__        =			do_normalize
							A__        =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A__        =			image_std if image_std is not None else IMAGENET_STANDARD_STD
							A__        =			apply_ocr
							A__        =			ocr_lang
							A__        =			tesseract_config


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = PILImageResampling.BILINEAR      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							A__        =			get_size_dict(lowercase       )
							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(lowercase      ,      size=lowercase      ,      resample=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							return rescale(lowercase      ,      scale=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = None      ,      **lowercase      ,      )  ->						np.ndarray:
							'''simple docstring'''






							return normalize(lowercase      ,      mean=lowercase      ,      std=lowercase      ,      data_format=lowercase      ,      **lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase = None      ,      lowercase = None      ,      lowercase=None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = None      ,      lowercase = ChannelDimension.FIRST      ,      **lowercase      ,      )  ->						PIL.Image.Image:
							'''simple docstring'''






							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(lowercase       )
							A__        =			resample if resample is not None else self.resample
							A__        =			do_rescale if do_rescale is not None else self.do_rescale
							A__        =			rescale_factor if rescale_factor is not None else self.rescale_factor
							A__        =			do_normalize if do_normalize is not None else self.do_normalize
							A__        =			image_mean if image_mean is not None else self.image_mean
							A__        =			image_std if image_std is not None else self.image_std
							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(lowercase       )

							if not valid_images(lowercase       ):
											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.
							A__        =			[to_numpy_array(lowercase       ) for image in images]

							# Tesseract OCR to get words + normalized bounding boxes
							if apply_ocr:
											requires_backends(self      ,      "pytesseract"       )
											A__        =			[]
											A__        =			[]
											for image in images:
															A__    ,				A__        =			apply_tesseract(lowercase      ,      lowercase      ,      lowercase       )
															words_batch.append(lowercase       )
															boxes_batch.append(lowercase       )

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

							if do_rescale:
											A__        =			[self.rescale(image=lowercase      ,      scale=lowercase       ) for image in images]

							if do_normalize:
											A__        =			[self.normalize(image=lowercase      ,      mean=lowercase      ,      std=lowercase       ) for image in images]

							A__        =			[to_channel_dimension_format(lowercase      ,      lowercase       ) for image in images]

							A__        =			BatchFeature(data={"pixel_values": images}      ,      tensor_type=lowercase       )

							if apply_ocr:
											A__        =			words_batch
											A__        =			boxes_batch
							return data




import datasets

from .evaluate import evaluate


lowerCAmelCase__	   = """\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
"""

lowerCAmelCase__	   = """
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

lowerCAmelCase__	   = """
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""

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







			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							return datasets.MetricInfo(
							    description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(
							        {
							            "predictions": {"id": datasets.Value("string"       ), "prediction_text": datasets.Value("string"       )},
							            "references": {
							                "id": datasets.Value("string"       ),
							                "answers": datasets.features.Sequence(
							                    {
							                        "text": datasets.Value("string"       ),
							                        "answer_start": datasets.Value("int32"       ),
							                    }       ),
							            },
							        }       )      ,      codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]      ,      reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			{prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							A__        =			[
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							A__        =			evaluate(dataset=lowercase      ,      predictions=lowercase       )
							return score

