



"""simple docstring"""






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

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


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

_lowercase : Tuple      						=						{"vocab_file": "vocab.txt"}

_lowercase : Optional[Any]      						=						{
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

_lowercase : List[Any]      						=						{
    "openbmb/cpm-ant-10b": 1_0_2_4,
}






def      snake_case__   (		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]			=collections.OrderedDict()
			with open(__lowerCamelCase							,		'''r'''							,		encoding='''utf-8'''	) as reader:
						lowerCamelCase__		:		List[str]			=reader.readlines()
			for index, token in enumerate(__lowerCamelCase	):
						lowerCamelCase__		:		Optional[Any]			=token.rstrip('''\n'''	)
						lowerCamelCase__		:		List[str]			=index
			return vocab

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]="<unk>",  lowerCamelCase							:  Optional[int]=200     )->					Dict:
								lowerCamelCase__		:		Optional[Any]			=vocab
								lowerCamelCase__		:		Any			=unk_token
								lowerCamelCase__		:		Optional[int]			=max_input_chars_per_word




					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Any     )->					List[str]:
								lowerCamelCase__		:		int			=list(A_     )
								if len(A_     ) > self.max_input_chars_per_word:
											return [self.unk_token]

								lowerCamelCase__		:		List[Any]			=0
								lowerCamelCase__		:		List[Any]			=[]
								while start < len(A_     ):
											lowerCamelCase__		:		Any			=len(A_     )
											lowerCamelCase__		:		Optional[Any]			=None
											while start < end:
														lowerCamelCase__		:		Union[str, Any]			=''''''.join(chars[start:end]     )
														if substr in self.vocab:
																	lowerCamelCase__		:		Optional[int]			=substr
																	break
														end -= 1
											if cur_substr is None:
														sub_tokens.append(self.unk_token     )
														start += 1
											else:
														sub_tokens.append(A_     )
														lowerCamelCase__		:		int			=end

								return sub_tokens





class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']
					_a   			=							False



					def __init__(       self							:  List[Any],  lowerCamelCase							:  int,  lowerCamelCase							:  str="<d>",  lowerCamelCase							:  str="</d>",  lowerCamelCase							:  Any="<s>",  lowerCamelCase							:  Dict="</s>",  lowerCamelCase							:  Union[str, Any]="<pad>",  lowerCamelCase							:  int="<unk>",  lowerCamelCase							:  Union[str, Any]="</n>",  lowerCamelCase							:  str="</_>",  lowerCamelCase							:  Union[str, Any]="left",  **lowerCamelCase							:  str,  )->					int:
								requires_backends(self,  ['''jieba''']     )
								super().__init__(
								    bod_token=A_,  eod_token=A_,  bos_token=A_,  eos_token=A_,  pad_token=A_,  unk_token=A_,  line_token=A_,  space_token=A_,  padding_side=A_,  **A_,  )
								lowerCamelCase__		:		Optional[Any]			=bod_token
								lowerCamelCase__		:		int			=eod_token
								lowerCamelCase__		:		Any			=load_vocab(A_     )
								lowerCamelCase__		:		Optional[Any]			=self.encoder[space_token]
								lowerCamelCase__		:		List[Any]			=self.encoder[line_token]

								del self.encoder[space_token]
								del self.encoder[line_token]

								lowerCamelCase__		:		Union[str, Any]			=collections.OrderedDict(sorted(self.encoder.items(),  key=lambda lowerCamelCase     : x[1]     )     )
								lowerCamelCase__		:		Dict			={v: k for k, v in self.encoder.items()}

								lowerCamelCase__		:		List[Any]			=WordpieceTokenizer(vocab=self.encoder,  unk_token=self.unk_token     )



					@property
					def 		snake_case       (       self							:  Tuple     )->					int:
								return self.encoder[self.bod_token]



					@property
					def 		snake_case       (       self							:  Dict     )->					str:
								return self.encoder[self.eod_token]



					@property
					def 		snake_case       (       self							:  Dict     )->					Optional[int]:
								return self.encoder["\n"]



					@property
					def 		snake_case       (       self							:  Optional[Any]     )->					int:
								return len(self.encoder     )



					def 		snake_case       (       self							:  Optional[int]     )->					Optional[int]:
								return dict(self.encoder,  **self.added_tokens_encoder     )



					def 		snake_case       (       self							:  str,  lowerCamelCase							:  Optional[Any]     )->					Optional[Any]:
								lowerCamelCase__		:		List[Any]			=[]
								for x in jieba.cut(A_,  cut_all=A_     ):
											output_tokens.extend(self.wordpiece_tokenizer.tokenize(A_     )     )
								return output_tokens



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Optional[int]:
								lowerCamelCase__		:		Optional[int]			=[i for i in token_ids if i >= 0]
								lowerCamelCase__		:		Any			=[
								    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
								]
								return super()._decode(A_,  **A_     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  List[str]     )->					int:
								return token in self.encoder



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  List[str]     )->					str:
								return "".join(A_     )



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  Union[str, Any]     )->					Union[str, Any]:
								return self.encoder.get(A_,  self.encoder.get(self.unk_token     )     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  Optional[Any]     )->					Any:
								return self.decoder.get(A_,  self.unk_token     )



					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None     )->					Tuple[str]:






								if os.path.isdir(A_     ):
											lowerCamelCase__		:		Optional[Any]			=os.path.join(
											    A_,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )
								else:
											lowerCamelCase__		:		List[str]			=(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
								lowerCamelCase__		:		Optional[Any]			=0
								if " " in self.encoder:
											lowerCamelCase__		:		str			=self.encoder[''' ''']
											del self.encoder[" "]
								if "\n" in self.encoder:
											lowerCamelCase__		:		Union[str, Any]			=self.encoder['''\n''']
											del self.encoder["\n"]
								lowerCamelCase__		:		Optional[int]			=collections.OrderedDict(sorted(self.encoder.items(),  key=lambda lowerCamelCase     : x[1]     )     )
								with open(A_,  '''w''',  encoding='''utf-8'''     ) as writer:
											for token, token_index in self.encoder.items():
														if index != token_index:
																	logger.warning(
																	    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																	    ''' Please check that the vocabulary is not corrupted!'''     )
																	lowerCamelCase__		:		List[str]			=token_index
														writer.write(token + '''\n'''     )
														index += 1
								return (vocab_file,)



					def 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  List[int],  lowerCamelCase							:  List[int] = None     )->					List[int]:






								if token_ids_a is None:
											return [self.bos_token_id] + token_ids_a
								return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




					def 		snake_case       (       self							:  str,  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None,  lowerCamelCase							:  bool = False     )->					List[int]:






								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 not None:
											return [1] + ([0] * len(A_     )) + [1] + ([0] * len(A_     ))
								return [1] + ([0] * len(A_     ))










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

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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Optional[int]	,							lowercase	:       Optional[int]     ):


      '''simple docstring'''



      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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       np.ndarray	,							lowercase	:       Optional[str]	,							lowercase	:       Optional[str]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    to_pil_image(lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_		     =    pil_image.size
      lowerCamelCase_		     =    pytesseract.image_to_data(lowercase	,							lang=lowercase	,							output_type='dict'	,							config=lowercase     )
      lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		     =    data['text'], data['left'], data['top'], data['width'], data['height']

      # filter empty words and corresponding coordinates
      lowerCamelCase_		     =    [idx for idx, word in enumerate(lowercase     ) if not word.strip()]
      lowerCamelCase_		     =    [word for idx, word in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]
      lowerCamelCase_		     =    [coord for idx, coord in enumerate(lowercase     ) if idx not in irrelevant_indices]

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

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

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

      return words, normalized_boxes







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  int						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  bool = True						,							A_					:  float = 1 / 255						,							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[int]						,							)   ->	None:

       """simple docstring"""






       super().__init__(**A_  )
       lowerCamelCase_		     =    size if size is not None else {'height': 224, 'width': 224}
       lowerCamelCase_		     =    get_size_dict(A_  )

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_value
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowerCamelCase_		     =    apply_ocr
       lowerCamelCase_		     =    ocr_lang
       lowerCamelCase_		     =    tesseract_config

 def 				a__ (				self					:  str						,							A_					:  np.ndarray						,							A_					:  Dict[str, int]						,							A_					:  PILImageResampling = PILImageResampling.BILINEAR						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  str						,							)   ->	np.ndarray:

       """simple docstring"""






       lowerCamelCase_		     =    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_		     =    (size['height'], size['width'])
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Any						,							A_					:  np.ndarray						,							A_					:  Union[int, float]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  Optional[Any]						,							)   ->	np.ndarray:

       """simple docstring"""






       return rescale(A_						,							scale=A_						,							data_format=A_						,							**A_  )

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  np.ndarray						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Union[float, Iterable[float]]						,							A_					:  Optional[Union[str, ChannelDimension]] = None						,							**A_					:  int						,							)   ->	np.ndarray:

       """simple docstring"""






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





 def 				a__ (				self					:  List[Any]						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  Dict=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_					:  Any						,							)   ->	PIL.Image.Image:

       """simple docstring"""






       lowerCamelCase_		     =    do_resize if do_resize is not None else self.do_resize
       lowerCamelCase_		     =    size if size is not None else self.size
       lowerCamelCase_		     =    get_size_dict(A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase_		     =    rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase_		     =    do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else self.image_mean
       lowerCamelCase_		     =    image_std if image_std is not None else self.image_std
       lowerCamelCase_		     =    apply_ocr if apply_ocr is not None else self.apply_ocr
       lowerCamelCase_		     =    ocr_lang if ocr_lang is not None else self.ocr_lang
       lowerCamelCase_		     =    tesseract_config if tesseract_config is not None else self.tesseract_config

       lowerCamelCase_		     =    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_		     =    [to_numpy_array(A_  ) for image in images]

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

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

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

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

       lowerCamelCase_		     =    [to_channel_dimension_format(A_						,							A_  ) for image in images]

       lowerCamelCase_		     =    BatchFeature(data={'pixel_values': images}						,							tensor_type=A_  )

       if apply_ocr:
             lowerCamelCase_		     =    words_batch
             lowerCamelCase_		     =    boxes_batch
       return data



