"""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


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Dict	   =       {'vocab_file': 'spiece.model'}

_a				:		Any	   =       {
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):






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

       _lowerCAmelCase						:	Optional[int]        =					{} if sp_model_kwargs is None else sp_model_kwargs

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

       _lowerCAmelCase						:	Union[str, Any]        =					3

       _lowerCAmelCase						:	Union[str, Any]        =					do_lower_case
       _lowerCAmelCase						:	str        =					remove_space
       _lowerCAmelCase						:	Any        =					keep_accents
       _lowerCAmelCase						:	List[Any]        =					vocab_file

       _lowerCAmelCase						:	List[Any]        =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(a__  )

       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(a__  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab






     def __getstate__(		self  ):
       _lowerCAmelCase						:	Any        =					self.__dict__.copy()
       _lowerCAmelCase						:	Optional[Any]        =					None
       return state






     def __setstate__(		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					d

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

       _lowerCAmelCase						:	str        =					spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )






     def    __A  (		self    ,					a__  ):
       if self.remove_space:
         _lowerCAmelCase						:	List[Any]        =					""" """.join(inputs.strip().split()  )
       else:
         _lowerCAmelCase						:	str        =					inputs
       _lowerCAmelCase						:	Tuple        =					outputs.replace("""``"""    ,					"""\""""  ).replace("""''"""    ,					"""\""""  )

       if not self.keep_accents:
         _lowerCAmelCase						:	List[Any]        =					unicodedata.normalize("""NFKD"""    ,					a__  )
         _lowerCAmelCase						:	Optional[Any]        =					"""""".join([c for c in outputs if not unicodedata.combining(a__  )]  )
       if self.do_lower_case:
         _lowerCAmelCase						:	Union[str, Any]        =					outputs.lower()

       return outputs






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					self.preprocess_text(a__  )
       _lowerCAmelCase						:	Any        =					self.sp_model.encode(a__    ,					out_type=a__  )
       _lowerCAmelCase						:	Optional[Any]        =					[]
       for piece in pieces:
         if len(a__  ) > 1 and piece[-1] == str(""","""  ) and piece[-2].isdigit():
           _lowerCAmelCase						:	List[Any]        =					self.sp_model.EncodeAsPieces(piece[:-1].replace(a__    ,					""""""  )  )
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]  ) == 1:
               _lowerCAmelCase						:	Dict        =					cur_pieces[1:]
             else:
               _lowerCAmelCase						:	int        =					cur_pieces[0][1:]
           cur_pieces.append(piece[-1]  )
           new_pieces.extend(a__  )
         else:
           new_pieces.append(a__  )

       return new_pieces






     def    __A  (		self    ,					a__  ):
       return self.sp_model.PieceToId(a__  )






     def    __A  (		self    ,					a__  ):
       return self.sp_model.IdToPiece(a__  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	List[str]        =					"""""".join(a__  ).replace(a__    ,					""" """  ).strip()
       return out_string






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       _lowerCAmelCase						:	Any        =					[self.sep_token_id]
       _lowerCAmelCase						:	List[Any]        =					[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    ,					a__    ,					a__ = None    ,					a__ = False  ):

       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 ([0] * len(a__  )) + [1] + ([0] * len(a__  )) + [1, 1]
       return ([0] * len(a__  )) + [1, 1]






     def    __A  (		self    ,					a__    ,					a__ = None  ):
       _lowerCAmelCase						:	List[str]        =					[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    ,					a__    ,					a__ = None  ):
       if not os.path.isdir(a__  ):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
         return
       _lowerCAmelCase						:	Dict        =					os.path.join(
           a__    ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

       return (out_vocab_file,)






     def    __A  (		self    ,					*a__    ,					**a__  ):
       _lowerCAmelCase						:	Optional[int]        =					super()._decode(*a__    ,					**a__  )
       _lowerCAmelCase						:	int        =					text.replace(""" """    ,					""""""  ).replace("""\u2582"""    ,					""" """  ).replace("""\u2583"""    ,					"""\n"""  )
       return text





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	Dict        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	str        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




