



'''simple docstring'''

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

a							   =				logging.get_logger(__name__)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			return [
			    int(10_00 * (box[0] / width)					),
			    int(10_00 * (box[1] / height)					),
			    int(10_00 * (box[2] / width)					),
			    int(10_00 * (box[3] / height)					),
			]







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    List[Any]      							=to_pil_image(__UpperCAmelCase					)
			snake_case							,  snake_case:    List[Any]      							=pil_image.size
			snake_case:    Dict      							=pytesseract.image_to_data(__UpperCAmelCase				,							lang=__UpperCAmelCase				,							output_type='dict'				,							config=__UpperCAmelCase					)
			snake_case							,  snake_case							,  snake_case							,  snake_case							,  snake_case:    Any      							=data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

			return words, normalized_boxes







class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Tuple        =				["""pixel_values"""]


							def __init__(				self   :						Tuple      ,						a_   :						bool = True      ,						a_   :						Dict[str, int] = None      ,						a_   :						PILImageResampling = PILImageResampling.BILINEAR      ,						a_   :						bool = True      ,						a_   :						float = 1 / 2_5_5      ,						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_   :						Union[str, Any]      ,						)			->					None:
										super().__init__(**a_			)
										snake_case:    Optional[int]      							=size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
										snake_case:    str      							=get_size_dict(a_			)

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


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						np.ndarray      ,						a_   :						Dict[str, int]      ,						a_   :						PILImageResampling = PILImageResampling.BILINEAR      ,						a_   :						Optional[Union[str, ChannelDimension]] = None      ,						**a_   :						Union[str, Any]      ,						)			->					np.ndarray:
										snake_case:    Tuple      							=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()}'''			)
										snake_case:    str      							=(size['height'], size['width'])
										return resize(a_      ,						size=a_      ,						resample=a_      ,						data_format=a_      ,						**a_			)


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


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




							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						ImageInput      ,						a_   :						bool = None      ,						a_   :						Dict[str, int] = None      ,						a_   :						Dict=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_   :						List[Any]      ,						)			->					PIL.Image.Image:
										snake_case:    Union[str, Any]      							=do_resize if do_resize is not None else self.do_resize
										snake_case:    int      							=size if size is not None else self.size
										snake_case:    Dict      							=get_size_dict(a_			)
										snake_case:    Union[str, Any]      							=resample if resample is not None else self.resample
										snake_case:    int      							=do_rescale if do_rescale is not None else self.do_rescale
										snake_case:    int      							=rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case:    Tuple      							=do_normalize if do_normalize is not None else self.do_normalize
										snake_case:    Union[str, Any]      							=image_mean if image_mean is not None else self.image_mean
										snake_case:    Optional[int]      							=image_std if image_std is not None else self.image_std
										snake_case:    int      							=apply_ocr if apply_ocr is not None else self.apply_ocr
										snake_case:    List[Any]      							=ocr_lang if ocr_lang is not None else self.ocr_lang
										snake_case:    str      							=tesseract_config if tesseract_config is not None else self.tesseract_config

										snake_case:    Optional[int]      							=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.
										snake_case:    Tuple      							=[to_numpy_array(a_			) for image in images]

										# Tesseract OCR to get words + normalized bounding boxes
										if apply_ocr:
													requires_backends(self      ,						'pytesseract'			)
													snake_case:    List[Any]      							=[]
													snake_case:    Any      							=[]
													for image in images:
																snake_case							,  snake_case:    Optional[int]      							=apply_tesseract(a_      ,						a_      ,						a_			)
																words_batch.append(a_			)
																boxes_batch.append(a_			)

										if do_resize:
													snake_case:    Any      							=[self.resize(image=a_      ,						size=a_      ,						resample=a_			) for image in images]

										if do_rescale:
													snake_case:    Any      							=[self.rescale(image=a_      ,						scale=a_			) for image in images]

										if do_normalize:
													snake_case:    Dict      							=[self.normalize(image=a_      ,						mean=a_      ,						std=a_			) for image in images]

										snake_case:    Optional[int]      							=[to_channel_dimension_format(a_      ,						a_			) for image in images]

										snake_case:    Tuple      							=BatchFeature(data={'pixel_values': images}      ,						tensor_type=a_			)

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









'''simple docstring'''

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				MgpstrTokenizer
							UpperCAmelCase			:		List[Any]        =				False
							UpperCAmelCase			:		Optional[int]        =				{}
							UpperCAmelCase			:		Any        =				False


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										super().setUp()

										# fmt: off
										snake_case:    List[Any]      							=['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
										# fmt: on
										snake_case:    Union[str, Any]      							=dict(zip(a_      ,						range(len(a_			)			)			)			)

										snake_case:    List[Any]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write(json.dumps(a_			) + '\n'			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						**a_   :						List[Any]			)			->					str:
										return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Dict			)			->					int:
										snake_case:    Any      							='tester'
										snake_case:    List[str]      							='tester'
										return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.'			)
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										pass


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										snake_case:    int      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case:    Optional[Any]      							='[SPECIAL_TOKEN]'

																tokenizer.add_special_tokens({'cls_token': special_token}			)
																snake_case:    List[str]      							=tokenizer.encode([special_token]      ,						add_special_tokens=a_			)
																self.assertEqual(len(a_			)      ,						1			)

																snake_case:    Union[str, Any]      							=tokenizer.decode(a_      ,						skip_special_tokens=a_			)
																self.assertTrue(special_token not in decoded			)


							def    UpperCamelCase     (				self   :						int			)			->					str:
										snake_case:    int      							=self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case							,  snake_case:    Dict      							=self.get_input_output_texts(a_			)

																snake_case:    Dict      							=tokenizer.tokenize(a_			)
																snake_case:    str      							=tokenizer.convert_tokens_to_ids(a_			)
																snake_case:    List[Any]      							=tokenizer.encode(a_      ,						add_special_tokens=a_			)
																self.assertListEqual(a_      ,						a_			)

																snake_case:    str      							=tokenizer.convert_ids_to_tokens(a_			)
																self.assertNotEqual(len(a_			)      ,						0			)
																snake_case:    Tuple      							=tokenizer.decode(a_			)
																self.assertIsInstance(a_      ,						a_			)

																self.assertEqual(text_a.replace(' '      ,						''			)      ,						a_			)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.'			)
							def    UpperCamelCase     (				self   :						Dict			)			->					str:
										pass




							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'			)
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										pass




