




"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""vocab_file""": """spiece.model"""}

lowercase__     						=						{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=False  ,      lowercase=True  ,      lowercase=False  ,      lowercase="<s>"  ,      lowercase="</s>"  ,      lowercase="<unk>"  ,      lowercase="<sep>"  ,      lowercase="<pad>"  ,      lowercase="<cls>"  ,      lowercase="<mask>"  ,      lowercase=["<eop>", "<eod>"]  ,      lowercase = None  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Optional[Any]    =    AddedToken(lowercase  ,      lstrip=lowercase  ,      rstrip=lowercase  ) if isinstance(lowercase  ,      lowercase  ) else mask_token

							_lowerCamelCase     :			Dict    =    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=lowercase  ,      remove_space=lowercase  ,      keep_accents=lowercase  ,      bos_token=lowercase  ,      eos_token=lowercase  ,      unk_token=lowercase  ,      sep_token=lowercase  ,      pad_token=lowercase  ,      cls_token=lowercase  ,      mask_token=lowercase  ,      additional_special_tokens=lowercase  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **lowercase  ,      )

							_lowerCamelCase     :			List[Any]    =    3

							_lowerCamelCase     :			Dict    =    do_lower_case
							_lowerCamelCase     :			Optional[Any]    =    remove_space
							_lowerCamelCase     :			Union[str, Any]    =    keep_accents
							_lowerCamelCase     :			int    =    vocab_file

							_lowerCamelCase     :			str    =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(lowercase  )

							try:
											import jieba
							except ModuleNotFoundError as error:
											raise error.__class__(
											    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
											    'See https://pypi.org/project/jieba/ for installation.'  )
							_lowerCamelCase     :			Optional[Any]    =    jieba
							_lowerCamelCase     :			List[Any]    =    str.maketrans(' \n'  ,      '\u2582\u2583'  )
			@property
			# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
			def 					A_    (  self  ):
							return len(self.sp_model  )
			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    {self.convert_ids_to_tokens(lowercase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab
			def __getstate__(  self  ):
							_lowerCamelCase     :			Optional[Any]    =    self.__dict__.copy()
							_lowerCamelCase     :			Optional[int]    =    None
							return state
			def __setstate__(  self  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    d

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

							_lowerCamelCase     :			List[Any]    =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )
			def 					A_    (  self  ,      lowercase  ):
							if self.remove_space:
											_lowerCamelCase     :			List[str]    =    ' '.join(inputs.strip().split()  )
							else:
											_lowerCamelCase     :			Tuple    =    inputs
							_lowerCamelCase     :			Optional[int]    =    outputs.replace('``'  ,      '"'  ).replace('\'\''  ,      '"'  )

							if not self.keep_accents:
											_lowerCamelCase     :			Any    =    unicodedata.normalize('NFKD'  ,      lowercase  )
											_lowerCamelCase     :			Dict    =    ''.join([c for c in outputs if not unicodedata.combining(lowercase  )]  )
							if self.do_lower_case:
											_lowerCamelCase     :			Dict    =    outputs.lower()

							return outputs
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			Tuple    =    self.preprocess_text(lowercase  )
							_lowerCamelCase     :			List[Any]    =    self.sp_model.encode(lowercase  ,      out_type=lowercase  )
							_lowerCamelCase     :			int    =    []
							for piece in pieces:
											if len(lowercase  ) > 1 and piece[-1] == str(','  ) and piece[-2].isdigit():
															_lowerCamelCase     :			Optional[int]    =    self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase  ,      ''  )  )
															if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																			if len(cur_pieces[0]  ) == 1:
																							_lowerCamelCase     :			Any    =    cur_pieces[1:]
																			else:
																							_lowerCamelCase     :			str    =    cur_pieces[0][1:]
															cur_pieces.append(piece[-1]  )
															new_pieces.extend(lowercase  )
											else:
															new_pieces.append(lowercase  )

							return new_pieces
			def 					A_    (  self  ,      lowercase  ):
							return self.sp_model.PieceToId(lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							return self.sp_model.IdToPiece(lowercase  )
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			List[str]    =    ''.join(lowercase  ).replace(lowercase  ,      ' '  ).strip()
							return out_string
			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							_lowerCamelCase     :			Optional[int]    =    [self.sep_token_id]
							_lowerCamelCase     :			int    =    [self.cls_token_id]
							if token_ids_a is None:
											return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls
			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ,      lowercase = False  ):

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

							if token_ids_a is not None:
											return ([0] * len(lowercase  )) + [1] + ([0] * len(lowercase  )) + [1, 1]
							return ([0] * len(lowercase  )) + [1, 1]
			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							_lowerCamelCase     :			Optional[int]    =    [self.sep_token_id]
							_lowerCamelCase     :			List[Any]    =    [2]

							if token_ids_a is None:
											return len(token_ids_a + sep  ) * [0] + cls_segment_id
							return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id
			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							if not os.path.isdir(lowercase  ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
											return
							_lowerCamelCase     :			Optional[int]    =    os.path.join(
							    lowercase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase  ) and os.path.isfile(self.vocab_file  ):
											copyfile(self.vocab_file  ,      lowercase  )
							elif not os.path.isfile(self.vocab_file  ):
											with open(lowercase  ,      'wb'  ) as fi:
															_lowerCamelCase     :			Union[str, Any]    =    self.sp_model.serialized_model_proto()
															fi.write(lowercase  )

							return (out_vocab_file,)






			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Any    =    super()._decode(*lowercase  ,      **lowercase  )
							_lowerCamelCase     :			List[Any]    =    text.replace(' '  ,      ''  ).replace('\u2582'  ,      ' '  ).replace('\u2583'  ,      '\n'  )
							return text




import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
    import PIL.Image

    from .features import FeatureType


__UpperCAmelCase          =       None
__UpperCAmelCase          =       '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__UpperCAmelCase          =       [
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]



@dataclass
class 				lowerCAmelCase_		:
       UpperCAmelCase__				:					bool        =  True
       UpperCAmelCase__				:					Optional[str]        =  None
       # Automatically constructed
       UpperCAmelCase__				:					ClassVar[str]        =  "PIL.Image.Image"
       UpperCAmelCase__				:					ClassVar[Any]        =  pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
       UpperCAmelCase__				:					str        =  field(default="Image"       ,    init=a__       ,    repr=a__							)
       def __call__(			self       )     ->		Any:
          return self.pa_type
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		dict:
          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('To support encoding images, please install \'Pillow\'.'       )

          if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Optional[int]							=	np.array(SCREAMING_SNAKE_CASE_       )

          if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             return {"path": value, "bytes": None}
          elif isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             return {"path": None, "bytes": value}
          elif isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       ):
             # convert the image array to PNG/TIFF bytes
             return encode_np_array(SCREAMING_SNAKE_CASE_       )
          elif isinstance(SCREAMING_SNAKE_CASE_,			PIL.Image.Image       ):
             # convert the PIL image to bytes (default format is PNG/TIFF)
             return encode_pil_image(SCREAMING_SNAKE_CASE_       )
          elif value.get('path'       ) is not None and os.path.isfile(value['path']       ):
             # we set "bytes": None to not duplicate the data if they're already available locally
             return {"bytes": None, "path": value.get('path'       )}
          elif value.get('bytes'       ) is not None or value.get('path'       ) is not None:
             # store the image bytes, and path is used to infer the image format using the file extension
             return {"bytes": value.get('bytes'       ), "path": value.get('path'       )}
          else:
             raise ValueError(
                 F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		"PIL.Image.Image":
          if not self.decode:
             raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.'       )

          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('To support decoding images, please install \'Pillow\'.'       )

          if token_per_repo_id is None:
             UpperCamelCase    :  Any							=	{}

          UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	value['path'], value['bytes']
          if bytes_ is None:
             if path is None:
                raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}."""       )
             else:
                if is_local_path(SCREAMING_SNAKE_CASE_       ):
                   UpperCamelCase    :  int							=	PIL.Image.open(SCREAMING_SNAKE_CASE_       )
                else:
                   UpperCamelCase    :  int							=	path.split('::'       )[-1]
                   try:
                      UpperCamelCase    :  Optional[Any]							=	string_to_dict(SCREAMING_SNAKE_CASE_,			config.HUB_DATASETS_URL       )['repo_id']
                      UpperCamelCase    :  str							=	token_per_repo_id.get(SCREAMING_SNAKE_CASE_       )
                   except ValueError:
                      UpperCamelCase    :  Tuple							=	None
                   with xopen(SCREAMING_SNAKE_CASE_,			'rb',			use_auth_token=SCREAMING_SNAKE_CASE_       ) as f:
                      UpperCamelCase    :  Optional[int]							=	BytesIO(f.read()       )
                   UpperCamelCase    :  int							=	PIL.Image.open(bytes_       )
          else:
             UpperCamelCase    :  Optional[int]							=	PIL.Image.open(BytesIO(bytes_       )       )
          image.load()  # to avoid "Too many open files" errors
          return image
       def 					snake_case_       (			self       )     ->		Union["FeatureType", Dict[str, "FeatureType"]]:
          from .features import Value

          return (
              self
              if self.decode
              else {
                  "bytes": Value('binary'       ),
                  "path": Value('string'       ),
              }
          )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		pa.StructArray:
          if pa.types.is_string(storage.type       ):
             UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.binary()       )
             UpperCamelCase    :  Dict							=	pa.StructArray.from_arrays([bytes_array, storage],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_binary(storage.type       ):
             UpperCamelCase    :  Optional[int]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  Union[str, Any]							=	pa.StructArray.from_arrays([storage, path_array],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_struct(storage.type       ):
             if storage.type.get_field_index('bytes'       ) >= 0:
                UpperCamelCase    :  List[str]							=	storage.field('bytes'       )
             else:
                UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.binary()       )
             if storage.type.get_field_index('path'       ) >= 0:
                UpperCamelCase    :  List[str]							=	storage.field('path'       )
             else:
                UpperCamelCase    :  Optional[Any]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  Optional[int]							=	pa.StructArray.from_arrays([bytes_array, path_array],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_list(storage.type       ):
             UpperCamelCase    :  Optional[Any]							=	pa.array(
                 [encode_np_array(np.array(SCREAMING_SNAKE_CASE_       )       )['bytes'] if arr is not None else None for arr in storage.to_pylist()],			type=pa.binary(),			)
             UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  int							=	pa.StructArray.from_arrays(
                 [bytes_array, path_array],			['bytes', 'path'],			mask=bytes_array.is_null()       )
          return array_cast(SCREAMING_SNAKE_CASE_,			self.pa_type       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		pa.StructArray:

          @no_op_if_value_is_null
          def path_to_bytes(SCREAMING_SNAKE_CASE_       ):
             with xopen(SCREAMING_SNAKE_CASE_,			'rb'       ) as f:
                UpperCamelCase    :  Optional[int]							=	f.read()
             return bytes_

          UpperCamelCase    :  Union[str, Any]							=	pa.array(
              [
                  (path_to_bytes(x['path']       ) if x['bytes'] is None else x['bytes']) if x is not None else None
                  for x in storage.to_pylist()
              ],			type=pa.binary(),			)
          UpperCamelCase    :  Any							=	pa.array(
              [os.path.basename(SCREAMING_SNAKE_CASE_       ) if path is not None else None for path in storage.field('path'       ).to_pylist()],			type=pa.string(),			)
          UpperCamelCase    :  int							=	pa.StructArray.from_arrays([bytes_array, path_array],			['bytes', 'path'],			mask=bytes_array.is_null()       )
          return array_cast(SCREAMING_SNAKE_CASE_,			self.pa_type       )






def 		UpperCamelCase	(   )	->     List[str]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      UpperCamelCase    :  Dict							=	list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
   return _IMAGE_COMPRESSION_FORMATS
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     bytes:
   UpperCamelCase    :  Any							=	BytesIO()
   if image.format in list_image_compression_formats():
      UpperCamelCase    :  Tuple							=	image.format
   else:
      UpperCamelCase    :  List[str]							=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
   image.save(snake_case__       ,     format=snake_case__	)
   return buffer.getvalue()
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     dict:
   if hasattr(snake_case__       ,     'filename'	) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(snake_case__	)}
def 		UpperCamelCase	(   snake_case__       :    np.ndarray	)	->     dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   UpperCamelCase    :  Union[str, Any]							=	array.dtype
   UpperCamelCase    :  List[Any]							=	dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
   UpperCamelCase    :  Optional[Any]							=	dtype.kind
   UpperCamelCase    :  Any							=	dtype.itemsize

   UpperCamelCase    :  int							=	None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      UpperCamelCase    :  Optional[Any]							=	np.dtype('|u1'	)
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""	)
      if dtype is not dest_dtype:
         warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""	)
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      UpperCamelCase    :  List[Any]							=	dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         UpperCamelCase    :  Dict							=	dtype_byteorder + dtype_kind + str(snake_case__	)
         UpperCamelCase    :  str							=	np.dtype(snake_case__	)
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""	)
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""	)
   UpperCamelCase    :  Union[str, Any]							=	PIL.Image.fromarray(array.astype(snake_case__	)	)
   return {"path": None, "bytes": image_to_bytes(snake_case__	)}


def 		UpperCamelCase	(   snake_case__       :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]	)	->     List[dict]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   if objs:
      UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	first_non_null_value(snake_case__	)
      if isinstance(snake_case__       ,     snake_case__	):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(snake_case__       ,     np.ndarray	):
         UpperCamelCase    :  List[Any]							=	no_op_if_value_is_null(snake_case__	)
         return [obj_to_image_dict_func(snake_case__	) for obj in objs]
      elif isinstance(snake_case__       ,     PIL.Image.Image	):
         UpperCamelCase    :  Optional[int]							=	no_op_if_value_is_null(snake_case__	)
         return [obj_to_image_dict_func(snake_case__	) for obj in objs]
      else:
         return objs
   else:
      return objs



