
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

SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[Any]:







				"""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 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	List[str]:







				"""simple docstring"""



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

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

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

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

				return words, normalized_boxes





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["pixel_values"]






	def __init__(       self					: Union[str, Any]							,A					: bool = True							,A					: Dict[str, int] = None							,A					: PILImageResampling = PILImageResampling.BILINEAR							,A					: bool = True							,A					: float = 1 / 2_55							,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					: Optional[Any]							,):
					super().__init__(**A )
					__A   			= size if size is not None else {"height": 2_24, "width": 2_24}
					__A   			= get_size_dict(A )

					__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					: Any							,A					: np.ndarray							,A					: Dict[str, int]							,A					: PILImageResampling = PILImageResampling.BILINEAR							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: List[str]							,):
					__A   			= 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()}''' )
					__A   			= (size["height"], size["width"])
					return resize(A							,size=A							,resample=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Tuple							,A					: np.ndarray							,A					: Union[int, float]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: List[Any]							,):
					return rescale(A							,scale=A							,data_format=A							,**A )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: np.ndarray							,A					: Union[float, Iterable[float]]							,A					: Union[float, Iterable[float]]							,A					: Optional[Union[str, ChannelDimension]] = None							,**A					: List[Any]							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A )







	def 				UpperCamelCase_  (       self					: Any							,A					: ImageInput							,A					: bool = None							,A					: Dict[str, int] = None							,A					: List[str]=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					: int							,):
					__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(A )
					__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(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.
					__A   			= [to_numpy_array(A ) 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(A							,A							,A )
													words_batch.append(A )
													boxes_batch.append(A )

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

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

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

					__A   			= [to_channel_dimension_format(A							,A ) for image in images]

					__A   			= BatchFeature(data={"pixel_values": images}							,tensor_type=A )

					if apply_ocr:
									__A   			= words_batch
									__A   			= boxes_batch
					return data


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							:Optional[int]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[int]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






	def __init__(       self					: Optional[Any]							,A					: List[str]							,A					: List[Any]="<s>"							,A					: Tuple="</s>"							,A					: Any="</s>"							,A					: Optional[Any]="<s>"							,A					: Tuple="<unk>"							,A					: str="<pad>"							,A					: int="<mask>"							,A					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]							,A					: Optional[Dict[str, Any]] = None							,**A					: Optional[Any]							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,mask_token=A							,additional_special_tokens=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )
					__A   			= vocab_file
					# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
					# sentencepiece vocabulary (this is the case for <s> and </s>
					__A   			= {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
					__A   			= len(self.fairseq_tokens_to_ids )
					__A   			= len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

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






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

					if token_ids_a is None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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]






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset )






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: Dict ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__A   			= d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs" ):
									__A   			= {}

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)
