
'''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    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Optional[int]       ,							UpperCAmelCase__							:							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    UpperCAmelCase							(  UpperCAmelCase__							:							np.ndarray       ,							UpperCAmelCase__							:							Optional[str]       ,							UpperCAmelCase__							:							Optional[str]):
				lowerCamelCase					:			Union[str, Any]									= to_pil_image(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= pil_image.size
				lowerCamelCase					:			Union[str, Any]									= pytesseract.image_to_data(UpperCAmelCase__       ,							lang=UpperCAmelCase__       ,							output_type='dict'       ,							config=UpperCAmelCase__)
				lowerCamelCase					:			Union[str, Any]									= data['text'], data['left'], data['top'], data['width'], data['height']

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

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

				# finally, normalize the bounding boxes
				lowerCamelCase					:			List[str]									= []
				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     __snake_case		(   a__):
					_lowerCAmelCase			     =	['''pixel_values''']
					def __init__(     self,    A = True,    A = None,    A = PILImageResampling.BILINEAR,    A = True,    A = 1 / 255,    A = True,    A = None,    A = None,    A = True,    A = None,    A = "",    **A,    ):

									"""simple docstring"""


									super().__init__(**A      )
									lowerCamelCase					:			List[str]									= size if size is not None else {'height': 224, 'width': 224}
									lowerCamelCase					:			List[str]									= get_size_dict(A      )

									lowerCamelCase					:			int									= do_resize
									lowerCamelCase					:			Tuple									= size
									lowerCamelCase					:			Optional[Any]									= resample
									lowerCamelCase					:			List[Any]									= do_rescale
									lowerCamelCase					:			Dict									= rescale_value
									lowerCamelCase					:			Optional[int]									= do_normalize
									lowerCamelCase					:			List[str]									= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowerCamelCase					:			Optional[Any]									= image_std if image_std is not None else IMAGENET_STANDARD_STD
									lowerCamelCase					:			List[str]									= apply_ocr
									lowerCamelCase					:			Dict									= ocr_lang
									lowerCamelCase					:			List[Any]									= tesseract_config
					def     UpperCAmelCase_							(     self,    A,    A,    A = PILImageResampling.BILINEAR,    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= 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					:			str									= (size['height'], size['width'])
									return resize(A,    size=A,    resample=A,    data_format=A,    **A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A = None,    **A,    ):

									"""simple docstring"""


									return rescale(A,    scale=A,    data_format=A,    **A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A = None,    **A,    ):

									"""simple docstring"""


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




					def     UpperCAmelCase_							(     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					:			List[Any]									= do_resize if do_resize is not None else self.do_resize
									lowerCamelCase					:			Dict									= size if size is not None else self.size
									lowerCamelCase					:			Tuple									= get_size_dict(A      )
									lowerCamelCase					:			int									= resample if resample is not None else self.resample
									lowerCamelCase					:			List[Any]									= do_rescale if do_rescale is not None else self.do_rescale
									lowerCamelCase					:			Optional[Any]									= rescale_factor if rescale_factor is not None else self.rescale_factor
									lowerCamelCase					:			Union[str, Any]									= do_normalize if do_normalize is not None else self.do_normalize
									lowerCamelCase					:			Optional[Any]									= image_mean if image_mean is not None else self.image_mean
									lowerCamelCase					:			List[str]									= image_std if image_std is not None else self.image_std
									lowerCamelCase					:			List[Any]									= apply_ocr if apply_ocr is not None else self.apply_ocr
									lowerCamelCase					:			Optional[int]									= ocr_lang if ocr_lang is not None else self.ocr_lang
									lowerCamelCase					:			str									= tesseract_config if tesseract_config is not None else self.tesseract_config

									lowerCamelCase					:			str									= 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					:			Tuple									= [to_numpy_array(A      ) for image in images]

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

									if do_resize:
													lowerCamelCase					:			Union[str, Any]									= [self.resize(image=A,    size=A,    resample=A      ) for image in images]

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

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

									lowerCamelCase					:			Dict									= [to_channel_dimension_format(A,    A      ) for image in images]

									lowerCamelCase					:			List[str]									= BatchFeature(data={'pixel_values': images},    tensor_type=A      )

									if apply_ocr:
													lowerCamelCase					:			Optional[int]									= words_batch
													lowerCamelCase					:			Optional[int]									= boxes_batch
									return data


'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Optional[int]									= ''
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	out_string += self.sp_model.decode(A      ) + token
																	lowerCamelCase					:			Any									= []
													else:
																	current_sub_tokens.append(A      )
									out_string += self.sp_model.decode(A      )
									return out_string.strip()




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			str									= 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:
																	lowerCamelCase					:			Optional[Any]									= self.sp_model.serialized_model_proto()
																	fi.write(A      )

									return (out_vocab_file,)
