
"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_camembert import CamembertTokenizer
else:
					UpperCAmelCase__      :    Union[str, Any]    							= None


UpperCAmelCase__      :    Tuple    							= logging.get_logger(__name__)

UpperCAmelCase__      :    Optional[int]    							= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__      :    Union[str, Any]    							= {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase__      :    Any    							= {
    'camembert-base': 5_1_2,
}

UpperCAmelCase__      :    int    							= '▁'



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[Any]					       =   VOCAB_FILES_NAMES
				__UpperCamelCase      :						Optional[int]					       =   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase      :						int					       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase      :						Any					       =   ['''input_ids''', '''attention_mask''']
				__UpperCamelCase      :						int					       =   CamembertTokenizer



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__="<s>"   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="</s>"   ,       SCREAMING_SNAKE_CASE__="<s>"   ,       SCREAMING_SNAKE_CASE__="<unk>"   ,       SCREAMING_SNAKE_CASE__="<pad>"   ,       SCREAMING_SNAKE_CASE__="<mask>"   ,       SCREAMING_SNAKE_CASE__=["<s>NOTUSED", "</s>NOTUSED"]   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   AddedToken(SCREAMING_SNAKE_CASE__   ,       lstrip=SCREAMING_SNAKE_CASE__   ,       rstrip=SCREAMING_SNAKE_CASE__    ) if isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) else mask_token

											super().__init__(
											    SCREAMING_SNAKE_CASE__   ,       tokenizer_file=SCREAMING_SNAKE_CASE__   ,       bos_token=SCREAMING_SNAKE_CASE__   ,       eos_token=SCREAMING_SNAKE_CASE__   ,       sep_token=SCREAMING_SNAKE_CASE__   ,       cls_token=SCREAMING_SNAKE_CASE__   ,       unk_token=SCREAMING_SNAKE_CASE__   ,       pad_token=SCREAMING_SNAKE_CASE__   ,       mask_token=SCREAMING_SNAKE_CASE__   ,       additional_special_tokens=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   vocab_file
											SCREAMING_SNAKE_CASE__						:       Dict           =   False if not self.vocab_file else True



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		List[int]:



											"""simple docstring"""




											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [self.cls_token_id]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		List[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep    ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		Tuple[str]:



											"""simple docstring"""




											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																		    """tokenizer."""    )

											if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
																		return
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   os.path.join(
											    SCREAMING_SNAKE_CASE__   ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

											if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE__    ):
																		copyfile(self.vocab_file   ,       SCREAMING_SNAKE_CASE__    )

											return (out_vocab_file,)






"""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

UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							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 lowercase_  (   _snake_case   ,_snake_case   ,_snake_case = None						):
							SCREAMING_SNAKE_CASE__						:       Dict           =   tesseract_config if tesseract_config is not None else """"""

							# apply OCR
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   to_pil_image(_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   pil_image.size
							SCREAMING_SNAKE_CASE__						:       Tuple           =   pytesseract.image_to_data(_snake_case   ,lang=_snake_case   ,output_type="""dict"""   ,config=_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

							# filter empty words and corresponding coordinates
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [idx for idx, word in enumerate(_snake_case						) if not word.strip()]
							SCREAMING_SNAKE_CASE__						:       Dict           =   [word for idx, word in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       int           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   []
							for x, y, w, h in zip(_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [x, y, x + w, y + h]
														actual_boxes.append(_snake_case						)

							# finally, normalize the bounding boxes
							SCREAMING_SNAKE_CASE__						:       List[str]           =   []
							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 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   ['''pixel_values''']



				def __init__(self   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = ""   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		None:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   size if size is not None else {"""height""": 2_24, """width""": 2_24}
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   get_size_dict(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_resize
											SCREAMING_SNAKE_CASE__						:       Any           =   size
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   resample
											SCREAMING_SNAKE_CASE__						:       Dict           =   apply_ocr
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ocr_lang
											SCREAMING_SNAKE_CASE__						:       Tuple           =   tesseract_config



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR   ,       SCREAMING_SNAKE_CASE__ = None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		np.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   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()}'''    )
											SCREAMING_SNAKE_CASE__						:       Any           =   (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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		PIL.Image.Image:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   size if size is not None else self.size
											SCREAMING_SNAKE_CASE__						:       Dict           =   get_size_dict(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   apply_ocr if apply_ocr is not None else self.apply_ocr
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ocr_lang if ocr_lang is not None else self.ocr_lang
											SCREAMING_SNAKE_CASE__						:       Dict           =   tesseract_config if tesseract_config is not None else self.tesseract_config

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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.
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [to_numpy_array(SCREAMING_SNAKE_CASE__    ) for image in images]

											if apply_ocr:
																		requires_backends(self   ,       """pytesseract"""    )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
																		SCREAMING_SNAKE_CASE__						:       Dict           =   []
																		for image in images:
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   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:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [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)
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [flip_channel_order(SCREAMING_SNAKE_CASE__    ) for image in images]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) for image in images]

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   BatchFeature(data={"""pixel_values""": images}   ,       tensor_type=SCREAMING_SNAKE_CASE__    )

											if apply_ocr:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   words_batch
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   boxes_batch
											return data




