




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 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	return [
	    int(1000 * (box[0] / width)       ),
	    int(1000 * (box[1] / height)       ),
	    int(1000 * (box[2] / width)       ),
	    int(1000 * (box[3] / height)       ),
	]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase = None       ) ->			int:
	a									=				tesseract_config if tesseract_config is not None else """"""

	# apply OCR
	a									=				to_pil_image(__lowerCamelCase       )
	a							,					a									=				pil_image.size
	a									=				pytesseract.image_to_data(__lowerCamelCase				,     lang=__lowerCamelCase				,     output_type="""dict"""				,     config=__lowerCamelCase       )
	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(__lowerCamelCase       ) if not word.strip()]
	a									=				[word for idx, word in enumerate(__lowerCamelCase       ) if idx not in irrelevant_indices]
	a									=				[coord for idx, coord in enumerate(__lowerCamelCase       ) if idx not in irrelevant_indices]
	a									=				[coord for idx, coord in enumerate(__lowerCamelCase       ) if idx not in irrelevant_indices]
	a									=				[coord for idx, coord in enumerate(__lowerCamelCase       ) if idx not in irrelevant_indices]
	a									=				[coord for idx, coord in enumerate(__lowerCamelCase       ) if idx not in irrelevant_indices]

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

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

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

	return words, normalized_boxes



class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    ['''pixel_values''']
							def __init__(		self		:Union[str, Any]						, __magic_name__		:bool = True						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:PILImageResampling = PILImageResampling.BILINEAR						, __magic_name__		:bool = True						, __magic_name__		:Optional[str] = None						, __magic_name__		:Optional[str] = ""						, **__magic_name__		:List[str]						, ):







								'''simple docstring'''







								super().__init__(**__magic_name__							)
								a									=				size if size is not None else {"""height""": 224, """width""": 224}
								a									=				get_size_dict(__magic_name__							)

								a									=				do_resize
								a									=				size
								a									=				resample
								a									=				apply_ocr
								a									=				ocr_lang
								a									=				tesseract_config
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:np.ndarray						, __magic_name__		:Dict[str, int]						, __magic_name__		:PILImageResampling = PILImageResampling.BILINEAR						, __magic_name__		:Optional[Union[str, ChannelDimension]] = None						, **__magic_name__		:List[Any]						, ):







								'''simple docstring'''







								a									=				get_size_dict(__magic_name__							)
								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(__magic_name__						, size=__magic_name__						, resample=__magic_name__						, data_format=__magic_name__						, **__magic_name__							)





							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:ImageInput						, __magic_name__		:bool = None						, __magic_name__		:Dict[str, int] = None						, __magic_name__		:PILImageResampling = None						, __magic_name__		:bool = None						, __magic_name__		:Optional[str] = None						, __magic_name__		:Optional[str] = None						, __magic_name__		:Optional[Union[str, TensorType]] = None						, __magic_name__		:ChannelDimension = ChannelDimension.FIRST						, **__magic_name__		:int						, ):







								'''simple docstring'''







								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(__magic_name__							)
								a									=				resample if resample is not None else self.resample
								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(__magic_name__							)

								if not valid_images(__magic_name__							):
									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.
								a									=				[to_numpy_array(__magic_name__							) for image in images]

								if apply_ocr:
									requires_backends(self						, """pytesseract"""							)
									a									=				[]
									a									=				[]
									for image in images:
										a							,					a									=				apply_tesseract(__magic_name__						, __magic_name__						, __magic_name__							)
										words_batch.append(__magic_name__							)
										boxes_batch.append(__magic_name__							)

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

								# flip color channels from RGB to BGR (as Detectron2 requires this)
								a									=				[flip_channel_order(__magic_name__							) for image in images]
								a									=				[to_channel_dimension_format(__magic_name__						, __magic_name__							) for image in images]

								a									=				BatchFeature(data={"""pixel_values""": images}						, tensor_type=__magic_name__							)

								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 PreTrainedTokenizer
from ...utils import logging


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

__UpperCamelCase   :			Optional[Any]          = "▁"

__UpperCamelCase   :			Any          = {"vocab_file": "sentencepiece.bpe.model"}

__UpperCamelCase   :			int          = {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

__UpperCamelCase   :			Dict          = {
    "facebook/xglm-564M": 2_048,
}

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    VOCAB_FILES_NAMES
							UpperCamelCase__   	=    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__   	=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__   	=    ['''input_ids''', '''attention_mask''']
							def __init__(		self		:Optional[Any]						, __magic_name__		:List[str]						, __magic_name__		:Tuple="<s>"						, __magic_name__		:List[Any]="</s>"						, __magic_name__		:List[str]="</s>"						, __magic_name__		:Any="<s>"						, __magic_name__		:Tuple="<unk>"						, __magic_name__		:int="<pad>"						, __magic_name__		:Optional[Dict[str, Any]] = None						, **__magic_name__		:Optional[Any]						, ):







								'''simple docstring'''







								a									=				{} if sp_model_kwargs is None else sp_model_kwargs

								# Compatibility with the original tokenizer
								a									=				7
								a									=				[F'<madeupword{i}>' for i in range(self.num_madeup_words							)]

								a									=				kwargs.get("""additional_special_tokens"""						, []							)
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    bos_token=__magic_name__						, eos_token=__magic_name__						, unk_token=__magic_name__						, sep_token=__magic_name__						, cls_token=__magic_name__						, pad_token=__magic_name__						, sp_model_kwargs=self.sp_model_kwargs						, **__magic_name__						, )

								a									=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(str(__magic_name__							)							)
								a									=				vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								a									=				1

								# Mimic fairseq token-to-id alignment for the first 4 token
								a									=				{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

								a									=				len(self.sp_model							)
								a									=				{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words							)}
								self.fairseq_tokens_to_ids.update(__magic_name__							)

								a									=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
							def __getstate__(		self		:List[Any]							):







								'''simple docstring'''







								a									=				self.__dict__.copy()
								a									=				None
								a									=				self.sp_model.serialized_model_proto()
								return state
							def __setstate__(		self		:Any						, __magic_name__		:Any							):







								'''simple docstring'''







								a									=				d

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

								a									=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None							):







								'''simple docstring'''







								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								a									=				[self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None						, __magic_name__		:bool = False							):







								'''simple docstring'''







								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__magic_name__						, token_ids_a=__magic_name__						, already_has_special_tokens=__magic_name__							)

								if token_ids_a is None:
									return [1] + ([0] * len(__magic_name__							))
								return [1] + ([0] * len(__magic_name__							)) + [1, 1] + ([0] * len(__magic_name__							))
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[int]						, __magic_name__		:Optional[List[int]] = None							):







								'''simple docstring'''







								a									=				[self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a							) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]
							@property
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								return len(self.sp_model							) + self.fairseq_offset + self.num_madeup_words
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				{self.convert_ids_to_tokens(__magic_name__							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:str							):







								'''simple docstring'''







								return self.sp_model.encode(__magic_name__						, out_type=__magic_name__							)
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Any							):







								'''simple docstring'''







								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								a									=				self.sp_model.PieceToId(__magic_name__							)

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Dict							):







								'''simple docstring'''







								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  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[str]							):







								'''simple docstring'''







								a									=				"""""".join(__magic_name__							).replace(__magic_name__						, """ """							).strip()
								return out_string





							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:str						, __magic_name__		:Optional[str] = None							):







								'''simple docstring'''







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

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

								return (out_vocab_file,)

