


"""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

UpperCamelCase__				:List[str]			   =	logging.get_logger(__name__)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Optional[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_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  str:
			_UpperCamelCase					:List[str]					     =	to_pil_image(snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase					:str					     =	pil_image.size
			_UpperCamelCase					:Any					     =	pytesseract.image_to_data(snake_case__       ,					lang=snake_case__       ,					output_type='''dict'''       ,					config=snake_case__		)
			_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase					:List[Any]					     =	data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

			# finally, normalize the bounding boxes
			_UpperCamelCase					:Tuple					     =	[]
			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      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["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__ = 1 / 2_55					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = True					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = ""					,				**SCREAMING_SNAKE_CASE__					,				)      ->	None:

					"""simple docstring"""



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

					_UpperCamelCase					:Tuple					     =	do_resize
					_UpperCamelCase					:List[Any]					     =	size
					_UpperCamelCase					:Tuple					     =	resample
					_UpperCamelCase					:Union[str, Any]					     =	do_rescale
					_UpperCamelCase					:Union[str, Any]					     =	rescale_value
					_UpperCamelCase					:Union[str, Any]					     =	do_normalize
					_UpperCamelCase					:Union[str, Any]					     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_UpperCamelCase					:str					     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
					_UpperCamelCase					:List[str]					     =	apply_ocr
					_UpperCamelCase					:Optional[Any]					     =	ocr_lang
					_UpperCamelCase					:Union[str, Any]					     =	tesseract_config
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	np.ndarray:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	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()}"   )
					_UpperCamelCase					:Dict					     =	(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   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	np.ndarray:

					"""simple docstring"""



					return rescale(SCREAMING_SNAKE_CASE__					,				scale=SCREAMING_SNAKE_CASE__					,				data_format=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	np.ndarray:

					"""simple docstring"""



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





		def   _UpperCamelCase( 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__ = 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"""



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

					_UpperCamelCase					:List[Any]					     =	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.'''   )

					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.
					_UpperCamelCase					:List[str]					     =	[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

					# Tesseract OCR to get words + normalized bounding boxes
					if apply_ocr:
								requires_backends(self					,				'''pytesseract'''   )
								_UpperCamelCase					:List[str]					     =	[]
								_UpperCamelCase					:List[str]					     =	[]
								for image in images:
											_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	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:
								_UpperCamelCase					:Any					     =	[self.resize(image=SCREAMING_SNAKE_CASE__					,				size=SCREAMING_SNAKE_CASE__					,				resample=SCREAMING_SNAKE_CASE__   ) for image in images]

					if do_rescale:
								_UpperCamelCase					:str					     =	[self.rescale(image=SCREAMING_SNAKE_CASE__					,				scale=SCREAMING_SNAKE_CASE__   ) for image in images]

					if do_normalize:
								_UpperCamelCase					:Optional[int]					     =	[self.normalize(image=SCREAMING_SNAKE_CASE__					,				mean=SCREAMING_SNAKE_CASE__					,				std=SCREAMING_SNAKE_CASE__   ) for image in images]

					_UpperCamelCase					:int					     =	[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) for image in images]

					_UpperCamelCase					:str					     =	BatchFeature(data={'''pixel_values''': images}					,				tensor_type=SCREAMING_SNAKE_CASE__   )

					if apply_ocr:
								_UpperCamelCase					:Optional[Any]					     =	words_batch
								_UpperCamelCase					:Optional[Any]					     =	boxes_batch
					return data





"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


UpperCamelCase__				:List[Any]			   =	logging.get_logger(__name__)


UpperCamelCase__				:Tuple			   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__				:List[str]			   =	{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase__				:List[Any]			   =	{
    """RUCAIBox/mvp""": 1_024,
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							VOCAB_FILES_NAMES
		A    =							PRETRAINED_VOCAB_FILES_MAP
		A    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A    =							["input_ids", "attention_mask"]
		A    =							MvpTokenizer
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__="replace"					,				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__=False					,				SCREAMING_SNAKE_CASE__=True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	List[str]:

					"""simple docstring"""



					super().__init__(
					    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				tokenizer_file=SCREAMING_SNAKE_CASE__					,				errors=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__					,				add_prefix_space=SCREAMING_SNAKE_CASE__					,				trim_offsets=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:int					     =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
					if pre_tok_state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
								_UpperCamelCase					:int					     =	getattr(SCREAMING_SNAKE_CASE__					,				pre_tok_state.pop('''type'''   )   )
								_UpperCamelCase					:Optional[int]					     =	add_prefix_space
								_UpperCamelCase					:List[Any]					     =	pre_tok_class(**SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[int]					     =	add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					_UpperCamelCase					:Any					     =	'''post_processor'''
					_UpperCamelCase					:Optional[Any]					     =	getattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					if tokenizer_component_instance:
								_UpperCamelCase					:Optional[Any]					     =	json.loads(tokenizer_component_instance.__getstate__()   )

								# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
								if "sep" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''sep''']   )
								if "cls" in state:
											_UpperCamelCase					:Optional[Any]					     =	tuple(state['''cls''']   )

								_UpperCamelCase					:str					     =	False

								if state.get('''add_prefix_space'''					,				SCREAMING_SNAKE_CASE__   ) != add_prefix_space:
											_UpperCamelCase					:Optional[int]					     =	add_prefix_space
											_UpperCamelCase					:Union[str, Any]					     =	True

								if state.get('''trim_offsets'''					,				SCREAMING_SNAKE_CASE__   ) != trim_offsets:
											_UpperCamelCase					:Union[str, Any]					     =	trim_offsets
											_UpperCamelCase					:Optional[Any]					     =	True

								if changes_to_apply:
											_UpperCamelCase					:Union[str, Any]					     =	getattr(SCREAMING_SNAKE_CASE__					,				state.pop('''type'''   )   )
											_UpperCamelCase					:Optional[Any]					     =	component_class(**SCREAMING_SNAKE_CASE__   )
											setattr(self.backend_tokenizer					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					if self._mask_token is None:
								if self.verbose:
											logger.error('''Using mask_token, but it is not set yet.'''   )
								return None
					return str(self._mask_token   )
		@mask_token.setter
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	AddedToken(SCREAMING_SNAKE_CASE__					,				lstrip=SCREAMING_SNAKE_CASE__					,				rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) else value
					_UpperCamelCase					:int					     =	value
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	BatchEncoding:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	kwargs.get('''is_split_into_words'''					,				SCREAMING_SNAKE_CASE__   )

					if is_split_into_words and not self.add_prefix_space:
								raise ValueError(
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    '''to use it with pretokenized inputs.'''   )

					return super()._encode_plus(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__ = None   )      ->	Tuple[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					,				name=SCREAMING_SNAKE_CASE__   )
					return tuple(SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=None   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
								return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





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

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	[self.sep_token_id]
					_UpperCamelCase					:List[str]					     =	[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]

