




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


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

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

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

lowerCamelCase				:		List[Any]     =   {
    "openbmb/cpm-ant-10b": 1_024,
}

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       int     ):


      '''simple docstring'''



      lowerCamelCase_		     =    collections.OrderedDict()
      with open(lowercase	,							'r'	,							encoding='utf-8'     ) as reader:
            lowerCamelCase_		     =    reader.readlines()
      for index, token in enumerate(lowercase     ):
            lowerCamelCase_		     =    token.rstrip('\n'     )
            lowerCamelCase_		     =    index
      return vocab







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  List[Any]						,							A_					:  List[Any]						,							A_					:  List[str]="<unk>"						,							A_					:  Optional[int]=200  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    vocab
       lowerCamelCase_		     =    unk_token
       lowerCamelCase_		     =    max_input_chars_per_word





 def 				a__ (				self					:  Tuple						,							A_					:  Any  )   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    list(A_  )
       if len(A_  ) > self.max_input_chars_per_word:
             return [self.unk_token]

       lowerCamelCase_		     =    0
       lowerCamelCase_		     =    []
       while start < len(A_  ):
             lowerCamelCase_		     =    len(A_  )
             lowerCamelCase_		     =    None
             while start < end:
                   lowerCamelCase_		     =    ''.join(chars[start:end]  )
                   if substr in self.vocab:
                         lowerCamelCase_		     =    substr
                         break
                   end -= 1
             if cur_substr is None:
                   sub_tokens.append(self.unk_token  )
                   start += 1
             else:
                   sub_tokens.append(A_  )
                   lowerCamelCase_		     =    end

       return sub_tokens







class   A(						UpperCamelCase   ):



 '''simple docstring'''


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

 def __init__(				self					:  List[Any]						,							A_					:  int						,							A_					:  str="<d>"						,							A_					:  str="</d>"						,							A_					:  Any="<s>"						,							A_					:  Dict="</s>"						,							A_					:  Union[str, Any]="<pad>"						,							A_					:  int="<unk>"						,							A_					:  Union[str, Any]="</n>"						,							A_					:  str="</_>"						,							A_					:  Union[str, Any]="left"						,							**A_					:  str						,							)   ->	int:

       """simple docstring"""






       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_		     =    bod_token
       lowerCamelCase_		     =    eod_token
       lowerCamelCase_		     =    load_vocab(A_  )
       lowerCamelCase_		     =    self.encoder[space_token]
       lowerCamelCase_		     =    self.encoder[line_token]

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

       lowerCamelCase_		     =    collections.OrderedDict(sorted(self.encoder.items()						,							key=lambda A_  : x[1]  )  )
       lowerCamelCase_		     =    {v: k for k, v in self.encoder.items()}

       lowerCamelCase_		     =    WordpieceTokenizer(vocab=self.encoder						,							unk_token=self.unk_token  )

 @property
 def 				a__ (				self					:  Tuple  )   ->	int:

       """simple docstring"""






       return self.encoder[self.bod_token]

 @property
 def 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






       return self.encoder[self.eod_token]

 @property
 def 				a__ (				self					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






       return self.encoder["\n"]

 @property
 def 				a__ (				self					:  Optional[Any]  )   ->	int:

       """simple docstring"""






       return len(self.encoder  )

 def 				a__ (				self					:  Optional[int]  )   ->	Optional[int]:

       """simple docstring"""






       return dict(self.encoder						,							**self.added_tokens_encoder  )

 def 				a__ (				self					:  str						,							A_					:  Optional[Any]  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    []
       for x in jieba.cut(A_						,							cut_all=A_  ):
             output_tokens.extend(self.wordpiece_tokenizer.tokenize(A_  )  )
       return output_tokens

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  Any						,							**A_					:  List[str]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [i for i in token_ids if i >= 0]
       lowerCamelCase_		     =    [
           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 				a__ (				self					:  Optional[Any]						,							A_					:  List[str]  )   ->	int:

       """simple docstring"""






       return token in self.encoder

 def 				a__ (				self					:  Tuple						,							A_					:  List[str]  )   ->	str:

       """simple docstring"""






       return "".join(A_  )

 def 				a__ (				self					:  List[Any]						,							A_					:  Union[str, Any]  )   ->	Union[str, Any]:

       """simple docstring"""






       return self.encoder.get(A_						,							self.encoder.get(self.unk_token  )  )

 def 				a__ (				self					:  Tuple						,							A_					:  Optional[Any]  )   ->	Any:

       """simple docstring"""






       return self.decoder.get(A_						,							self.unk_token  )

 def 				a__ (				self					:  Tuple						,							A_					:  str						,							A_					:  Optional[str] = None  )   ->	Tuple[str]:

       """simple docstring"""






       if os.path.isdir(A_  ):
             lowerCamelCase_		     =    os.path.join(
                 A_						,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
       else:
             lowerCamelCase_		     =    (filename_prefix + '-' if filename_prefix else '') + save_directory
       lowerCamelCase_		     =    0
       if " " in self.encoder:
             lowerCamelCase_		     =    self.encoder[' ']
             del self.encoder[" "]
       if "\n" in self.encoder:
             lowerCamelCase_		     =    self.encoder['\n']
             del self.encoder["\n"]
       lowerCamelCase_		     =    collections.OrderedDict(sorted(self.encoder.items()						,							key=lambda A_  : 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_		     =    token_index
                   writer.write(token + '\n'  )
                   index += 1
       return (vocab_file,)

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

       """simple docstring"""






       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 				a__ (				self					:  str						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None						,							A_					:  bool = False  )   ->	List[int]:

       """simple docstring"""






       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, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)


if is_vision_available():
 import PIL







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''pixel_values''']

 def __init__(				self					:  List[Any]						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = PILImageResampling.BICUBIC						,							A_					:  bool = True						,							A_					:  Dict[str, int] = None						,							A_					:  bool = True						,							A_					:  Union[int, float] = 1 / 255						,							A_					:  bool = True						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = True						,							**A_					:  Dict						,							)   ->	None:

       """simple docstring"""






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

       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    resample
       lowerCamelCase_		     =    do_center_crop
       lowerCamelCase_		     =    crop_size
       lowerCamelCase_		     =    do_rescale
       lowerCamelCase_		     =    rescale_factor
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowerCamelCase_		     =    image_std if image_std is not None else OPENAI_CLIP_STD
       lowerCamelCase_		     =    do_convert_rgb

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

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_						,							default_to_square=A_  )
       if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
       lowerCamelCase_		     =    get_resize_output_image_size(A_						,							size=size['shortest_edge']						,							default_to_square=A_  )
       return resize(A_						,							size=A_						,							resample=A_						,							data_format=A_						,							**A_  )

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

       """simple docstring"""






       lowerCamelCase_		     =    get_size_dict(A_  )
       if "height" not in size or "width" not in size:
             raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""  )
       return center_crop(A_						,							size=(size['height'], size['width'])						,							data_format=A_						,							**A_  )

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

       """simple docstring"""






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

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

       """simple docstring"""






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





 def 				a__ (				self					:  Any						,							A_					:  ImageInput						,							A_					:  bool = None						,							A_					:  Dict[str, int] = None						,							A_					:  PILImageResampling = None						,							A_					:  bool = None						,							A_					:  int = None						,							A_					:  bool = None						,							A_					:  float = None						,							A_					:  bool = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  Optional[Union[float, List[float]]] = None						,							A_					:  bool = None						,							A_					:  Optional[Union[str, TensorType]] = None						,							A_					:  Optional[ChannelDimension] = ChannelDimension.FIRST						,							**A_					:  Dict						,							)   ->	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_						,							param_name='size'						,							default_to_square=A_  )
       lowerCamelCase_		     =    resample if resample is not None else self.resample
       lowerCamelCase_		     =    do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase_		     =    crop_size if crop_size is not None else self.crop_size
       lowerCamelCase_		     =    get_size_dict(A_						,							param_name='crop_size'						,							default_to_square=A_  )
       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_		     =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       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_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop 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('Image mean and std must be specified if do_normalize is True.'  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
             lowerCamelCase_		     =    [convert_to_rgb(A_  ) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase_		     =    [to_numpy_array(A_  ) for image in images]

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

       if do_center_crop:
             lowerCamelCase_		     =    [self.center_crop(image=A_						,							size=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_		     =    {'pixel_values': images}
       return BatchFeature(data=A_						,							tensor_type=A_  )



