




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

UpperCamelCase__    			=			logging.get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int     ):
					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      (						SCREAMING_SNAKE_CASE_							:				np.ndarray      ,      SCREAMING_SNAKE_CASE_							:				Optional[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[str]     ):
					__lowerCAmelCase				   =       to_pil_image(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       pil_image.size
					__lowerCAmelCase				   =       pytesseract.image_to_data(SCREAMING_SNAKE_CASE_      ,      lang=SCREAMING_SNAKE_CASE_      ,      output_type="dict"      ,      config=SCREAMING_SNAKE_CASE_     )
					__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(SCREAMING_SNAKE_CASE_     ) if not word.strip()]
					__lowerCAmelCase				   =       [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_     ) if idx not in irrelevant_indices]
					__lowerCAmelCase				   =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_     ) if idx not in irrelevant_indices]
					__lowerCAmelCase				   =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_     ) if idx not in irrelevant_indices]
					__lowerCAmelCase				   =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_     ) if idx not in irrelevant_indices]
					__lowerCAmelCase				   =       [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
					__lowerCAmelCase				   =       []
					for x, y, w, h in zip(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
										__lowerCAmelCase				   =       [x, y, x + w, y + h]
										actual_boxes.append(SCREAMING_SNAKE_CASE_     )

					# finally, normalize the bounding boxes
					__lowerCAmelCase				   =       []
					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__      ):
						_a							:    Dict					=    ["""pixel_values"""]






						def __init__(   self		,			_A = True		,			_A = None		,			_A = PILImageResampling.BILINEAR		,			_A = True		,			_A = 1 / 2_5_5		,			_A = True		,			_A = None		,			_A = None		,			_A = True		,			_A = None		,			_A = ""		,			**_A		,			):




											"""simple docstring"""



											super().__init__(**_A			)
											__lowerCAmelCase				   =       size if size is not None else {"height": 2_2_4, "width": 2_2_4}
											__lowerCAmelCase				   =       get_size_dict(_A			)

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = PILImageResampling.BILINEAR		,			_A = None		,			**_A		,			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A		,			_A = None		,			**_A		,			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A = None		,			_A = None		,			_A=None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = None		,			_A = ChannelDimension.FIRST		,			**_A		,			):




											"""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(_A			)
											__lowerCAmelCase				   =       resample if resample is not None else self.resample
											__lowerCAmelCase				   =       do_rescale if do_rescale is not None else self.do_rescale
											__lowerCAmelCase				   =       rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCAmelCase				   =       do_normalize if do_normalize is not None else self.do_normalize
											__lowerCAmelCase				   =       image_mean if image_mean is not None else self.image_mean
											__lowerCAmelCase				   =       image_std if image_std is not None else self.image_std
											__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(_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.
											__lowerCAmelCase				   =       [to_numpy_array(_A			) for image in images]

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

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

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

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

											__lowerCAmelCase				   =       [to_channel_dimension_format(_A		,			_A			) for image in images]

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

											if apply_ocr:
																__lowerCAmelCase				   =       words_batch
																__lowerCAmelCase				   =       boxes_batch
											return data










import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__    			=			get_tests_dir("""fixtures/test_sentencepiece.model""")
UpperCamelCase__    			=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

UpperCamelCase__    			=			"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class 			a__    (					snake_case__ ,				unittest.TestCase      ):
						_a							:    int					=    CamembertTokenizer
						_a							:    Dict					=    CamembertTokenizerFast
						_a							:    Tuple					=    True
						_a							:    List[Any]					=    True






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase				   =       CamembertTokenizer(_A			)
											tokenizer.save_pretrained(self.tmpdirname			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       "<pad>"
											__lowerCAmelCase				   =       1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A			)		,			_A			)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A			)		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       list(self.get_tokenizer().get_vocab().keys()			)

											self.assertEqual(vocab_keys[0]		,			"<s>NOTUSED"			)
											self.assertEqual(vocab_keys[1]		,			"<pad>"			)
											self.assertEqual(vocab_keys[-1]		,			"<mask>"			)
											self.assertEqual(len(_A			)		,			1_0_0_4			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.assertEqual(self.get_tokenizer().vocab_size		,			1_0_0_5			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       CamembertTokenizer(_A			)
											tokenizer.save_pretrained(self.tmpdirname			)
											__lowerCAmelCase				   =       CamembertTokenizerFast.from_pretrained(self.tmpdirname			)

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											# <unk> tokens are not the same for `rust` than for `slow`.
											# Because spm gives back raw token instead of `unk` in EncodeAsPieces
											# tokens = tokenizer.tokenize(sequence)
											__lowerCAmelCase				   =       tokenizer.convert_ids_to_tokens(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if not self.test_rust_tokenizer:
																return

											__lowerCAmelCase				   =       self.get_tokenizer()
											__lowerCAmelCase				   =       self.get_rust_tokenizer()

											__lowerCAmelCase				   =       "I was born in 92000, and this is falsé."

											__lowerCAmelCase				   =       tokenizer.tokenize(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.tokenize(_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       tokenizer.encode(_A		,			add_special_tokens=_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A		,			add_special_tokens=_A			)
											self.assertListEqual(_A		,			_A			)

											__lowerCAmelCase				   =       self.get_rust_tokenizer()
											__lowerCAmelCase				   =       tokenizer.encode(_A			)
											__lowerCAmelCase				   =       rust_tokenizer.encode(_A			)
											self.assertListEqual(_A		,			_A			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       {"input_ids": [[5, 5_4, 7_1_9_6, 2_9_7, 3_0, 2_3, 7_7_6, 1_8, 1_1, 3_2_1_5, 3_7_0_5, 8_2_5_2, 2_2, 3_1_6_4, 1_1_8_1, 2_1_1_6, 2_9, 1_6, 8_1_3, 2_5, 7_9_1, 3_3_1_4, 2_0, 3_4_4_6, 3_8, 2_7_5_7_5, 1_2_0, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_6_8, 1_7, 1_1, 9_0_8_8, 2_0, 1_5_1_7, 8, 2_2_8_0_4, 1_8_8_1_8, 1_0, 3_8, 6_2_9, 6_0_7, 6_0_7, 1_4_2, 1_9, 7_1_9_6, 8_6_7, 5_6, 1_0_3_2_6, 2_4, 2_2_6_7, 2_0, 4_1_6, 5_0_7_2, 1_5_6_1_2, 2_3_3, 7_3_4, 7, 2_3_9_9, 2_7, 1_6, 3_0_1_5, 1_6_4_9, 7, 2_4, 2_0, 4_3_3_8, 2_3_9_9, 2_7, 1_3, 3_4_0_0, 1_4, 1_3, 6_1_8_9, 8, 9_3_0, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# camembert is a french model. So we also use french texts.
											__lowerCAmelCase				   =       [
											    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
											    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
											    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
											    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
											    "telles que la traduction et la synthèse de texte.",
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=_A		,			model_name="camembert-base"		,			revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"		,			sequences=_A		,			)




