






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


lowerCamelCase					    =	logging.get_logger(__name__)

lowerCamelCase					    =	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase					    =	{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}

class        snake_case_				(							lowerCamelCase__   ):







   """simple docstring"""



   def __init__(     self:						Dict ,							snake_case__:						Tuple=None ,							snake_case__:						Tuple=None ,							snake_case__:						int=False ,							snake_case__:						int=True ,							snake_case__:						Any=False ,							snake_case__:						List[str]="<s>" ,							snake_case__:						Optional[int]="</s>" ,							snake_case__:						Dict="<unk>" ,							snake_case__:						Union[str, Any]="<sep>" ,							snake_case__:						List[Any]="<pad>" ,							snake_case__:						int="<cls>" ,							snake_case__:						Union[str, Any]="<mask>" ,							snake_case__:						Optional[int]=["<eop>", "<eod>"] ,							**snake_case__:						Any ,							)	->		int:


       """simple docstring"""



       _lowercase     			=						AddedToken(_UpperCAmelCase ,							lstrip=_UpperCAmelCase ,							rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase ,							_UpperCAmelCase						) else mask_token

       super().__init__(
           vocab_file=_UpperCAmelCase ,							tokenizer_file=_UpperCAmelCase ,							do_lower_case=_UpperCAmelCase ,							remove_space=_UpperCAmelCase ,							keep_accents=_UpperCAmelCase ,							bos_token=_UpperCAmelCase ,							eos_token=_UpperCAmelCase ,							unk_token=_UpperCAmelCase ,							sep_token=_UpperCAmelCase ,							pad_token=_UpperCAmelCase ,							cls_token=_UpperCAmelCase ,							mask_token=_UpperCAmelCase ,							additional_special_tokens=_UpperCAmelCase ,							**_UpperCAmelCase ,							)

       _lowercase     			=						3
       _lowercase     			=						do_lower_case
       _lowercase     			=						remove_space
       _lowercase     			=						keep_accents
       _lowercase     			=						vocab_file
       _lowercase     			=						False if not self.vocab_file else True

       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.'						)
       _lowercase     			=						jieba
       _lowercase     			=						str.maketrans(' \n' ,							'\u2582\u2583'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						List[str] ,							snake_case__:						Optional[Any] = None						)	->		Any:


       """simple docstring"""



       _lowercase     			=						[self.sep_token_id]
       _lowercase     			=						[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       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						str ,							snake_case__:						str = None						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						[self.sep_token_id]
       _lowercase     			=						[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       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						int ,							snake_case__:						Dict = None						)	->		Tuple:


       """simple docstring"""



       if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'						)

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

       if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
           copyfile(self.vocab_file ,							_UpperCAmelCase						)

       return (out_vocab_file,)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						str ,							*snake_case__:						List[str] ,							**snake_case__:						Any						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						[
           ' '.join([x.translate(self.translator						) for x in self.jieba.cut(_UpperCAmelCase ,							cut_all=_UpperCAmelCase						)]						)
           for text in batch_text_or_text_pairs
       ]
       return super()._batch_encode_plus(_UpperCAmelCase ,							*_UpperCAmelCase ,							**_UpperCAmelCase						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							*snake_case__:						List[str] ,							**snake_case__:						Any						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						super()._decode(*_UpperCAmelCase ,							**_UpperCAmelCase						)
       _lowercase     			=						text.replace(' ' ,							''						).replace('\u2582' ,							' '						).replace('\u2583' ,							'\n'						)
       return text




import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

       snake_case_			= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )