
import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english




@require_tokenizers
class 					_lowerCamelCase			(   lowerCamelCase__						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	BertTokenizer
     SCREAMING_SNAKE_CASE_		 =	BertTokenizerFast
     SCREAMING_SNAKE_CASE_		 =	True
     SCREAMING_SNAKE_CASE_		 =	True
     SCREAMING_SNAKE_CASE_		 =	filter_non_english






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict:




           """simple docstring"""







           super().setUp()

           UpperCamelCase__     : str											=					[
               '''[UNK]''',
               '''[CLS]''',
               '''[SEP]''',
               '''[PAD]''',
               '''[MASK]''',
               '''want''',
               '''##want''',
               '''##ed''',
               '''wa''',
               '''un''',
               '''runn''',
               '''##ing''',
               ''',''',
               '''low''',
               '''lowest''',
           ]
           UpperCamelCase__     : str											=					os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES['''vocab_file''']	)
           with open(self.vocab_file    ,    '''w'''    ,    encoding='''utf-8'''	) as vocab_writer:
                 vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					'''UNwant\u00E9d,running'''
           UpperCamelCase__     : List[str]											=					'''unwanted, running'''
           return input_text, output_text






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.tokenizer_class(self.vocab_file	)

           UpperCamelCase__     : str											=					tokenizer.tokenize('''UNwant\u00E9d,running'''	)
           self.assertListEqual(a__    ,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']	)
           self.assertListEqual(tokenizer.convert_tokens_to_ids(a__	)    ,    [9, 6, 7, 1_2, 1_0, 1_1]	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           if not self.test_rust_tokenizer:
                 return

           UpperCamelCase__     : str											=					self.get_tokenizer()
           UpperCamelCase__     : List[Any]											=					self.get_rust_tokenizer()

           UpperCamelCase__     : Optional[int]											=					'''UNwant\u00E9d,running'''

           UpperCamelCase__     : Any											=					tokenizer.tokenize(a__	)
           UpperCamelCase__     : int											=					rust_tokenizer.tokenize(a__	)
           self.assertListEqual(a__    ,    a__	)

           UpperCamelCase__     : int											=					tokenizer.encode(a__    ,    add_special_tokens=a__	)
           UpperCamelCase__     : Tuple											=					rust_tokenizer.encode(a__    ,    add_special_tokens=a__	)
           self.assertListEqual(a__    ,    a__	)

           UpperCamelCase__     : str											=					self.get_rust_tokenizer()
           UpperCamelCase__     : str											=					tokenizer.encode(a__	)
           UpperCamelCase__     : int											=					rust_tokenizer.encode(a__	)
           self.assertListEqual(a__    ,    a__	)

           # With lower casing
           UpperCamelCase__     : Union[str, Any]											=					self.get_tokenizer(do_lower_case=a__	)
           UpperCamelCase__     : Optional[int]											=					self.get_rust_tokenizer(do_lower_case=a__	)

           UpperCamelCase__     : Any											=					'''UNwant\u00E9d,running'''

           UpperCamelCase__     : Any											=					tokenizer.tokenize(a__	)
           UpperCamelCase__     : Optional[int]											=					rust_tokenizer.tokenize(a__	)
           self.assertListEqual(a__    ,    a__	)

           UpperCamelCase__     : Dict											=					tokenizer.encode(a__    ,    add_special_tokens=a__	)
           UpperCamelCase__     : int											=					rust_tokenizer.encode(a__    ,    add_special_tokens=a__	)
           self.assertListEqual(a__    ,    a__	)

           UpperCamelCase__     : Optional[int]											=					self.get_rust_tokenizer()
           UpperCamelCase__     : Dict											=					tokenizer.encode(a__	)
           UpperCamelCase__     : List[str]											=					rust_tokenizer.encode(a__	)
           self.assertListEqual(a__    ,    a__	)






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




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BasicTokenizer()

           self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''	)    ,    ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BasicTokenizer(do_lower_case=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''	)    ,    ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']	)
           self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''	)    ,    ['''hello''']	)






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					BasicTokenizer(do_lower_case=a__    ,    strip_accents=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''	)    ,    ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']	)
           self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''	)    ,    ['''h\u00E9llo''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					BasicTokenizer(do_lower_case=a__    ,    strip_accents=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''	)    ,    ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']	)
           self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''	)    ,    ['''hello''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[Any]:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					BasicTokenizer(do_lower_case=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''	)    ,    ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']	)
           self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''	)    ,    ['''hello''']	)






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




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					BasicTokenizer(do_lower_case=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''	)    ,    ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']	)






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




           """simple docstring"""







           UpperCamelCase__     : Any											=					BasicTokenizer(do_lower_case=a__    ,    strip_accents=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''	)    ,    ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : int											=					BasicTokenizer(do_lower_case=a__    ,    strip_accents=a__	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''	)    ,    ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					BasicTokenizer(do_lower_case=a__    ,    never_split=['''[UNK]''']	)

           self.assertListEqual(
               tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''	)    ,    ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					BasicTokenizer()
           UpperCamelCase__     : Optional[int]											=					'''a\n\'ll !!to?\'d of, can\'t.'''
           UpperCamelCase__     : Optional[Any]											=					['''a''', '''\'''', '''ll''', '''!''', '''!''', '''to''', '''?''', '''\'''', '''d''', '''of''', ''',''', '''can''', '''\'''', '''t''', '''.''']
           self.assertListEqual(tokenizer.tokenize(a__	)    ,    a__	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

           UpperCamelCase__     : Union[str, Any]											=					{}
           for i, token in enumerate(a__	):
                 UpperCamelCase__     : Tuple											=					i
           UpperCamelCase__     : str											=					WordpieceTokenizer(vocab=a__    ,    unk_token='''[UNK]'''	)

           self.assertListEqual(tokenizer.tokenize(''''''	)    ,    []	)

           self.assertListEqual(tokenizer.tokenize('''unwanted running'''	)    ,    ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']	)

           self.assertListEqual(tokenizer.tokenize('''unwantedX running'''	)    ,    ['''[UNK]''', '''runn''', '''##ing''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           self.assertTrue(_is_whitespace(''' '''	)	)
           self.assertTrue(_is_whitespace('''\t'''	)	)
           self.assertTrue(_is_whitespace('''\r'''	)	)
           self.assertTrue(_is_whitespace('''\n'''	)	)
           self.assertTrue(_is_whitespace('''\u00A0'''	)	)

           self.assertFalse(_is_whitespace('''A'''	)	)
           self.assertFalse(_is_whitespace('''-'''	)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           self.assertTrue(_is_control('''\u0005'''	)	)

           self.assertFalse(_is_control('''A'''	)	)
           self.assertFalse(_is_control(''' '''	)	)
           self.assertFalse(_is_control('''\t'''	)	)
           self.assertFalse(_is_control('''\r'''	)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           self.assertTrue(_is_punctuation('''-'''	)	)
           self.assertTrue(_is_punctuation('''$'''	)	)
           self.assertTrue(_is_punctuation('''`'''	)	)
           self.assertTrue(_is_punctuation('''.'''	)	)

           self.assertFalse(_is_punctuation('''A'''	)	)
           self.assertFalse(_is_punctuation(''' '''	)	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_tokenizer()
           UpperCamelCase__     : str											=					self.get_rust_tokenizer()

           # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
           self.assertListEqual([tokenizer.tokenize(a__	) for t in ['''Test''', '''\xad''', '''test''']]    ,    [['''[UNK]'''], [], ['''[UNK]''']]	)

           self.assertListEqual(
               [rust_tokenizer.tokenize(a__	) for t in ['''Test''', '''\xad''', '''test''']]    ,    [['''[UNK]'''], [], ['''[UNK]''']]	)






     @slow
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.tokenizer_class.from_pretrained('''bert-base-uncased'''	)

           UpperCamelCase__     : Tuple											=					tokenizer.encode('''sequence builders'''    ,    add_special_tokens=a__	)
           UpperCamelCase__     : Any											=					tokenizer.encode('''multi-sequence build'''    ,    add_special_tokens=a__	)

           UpperCamelCase__     : Optional[Any]											=					tokenizer.build_inputs_with_special_tokens(a__	)
           UpperCamelCase__     : Union[str, Any]											=					tokenizer.build_inputs_with_special_tokens(a__    ,    a__	)

           assert encoded_sentence == [1_0_1] + text + [1_0_2]
           assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                       UpperCamelCase__     : List[str]											=					self.rust_tokenizer_class.from_pretrained(a__    ,    **a__	)

                       UpperCamelCase__     : Optional[int]											=					F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
                       UpperCamelCase__     : List[str]											=					tokenizer_r.encode_plus(
                           a__    ,    return_attention_mask=a__    ,    return_token_type_ids=a__    ,    return_offsets_mapping=a__    ,    add_special_tokens=a__    ,    )

                       UpperCamelCase__     : List[Any]											=					tokenizer_r.do_lower_case if hasattr(a__    ,    '''do_lower_case'''	) else False
                       UpperCamelCase__     : Union[str, Any]											=					(
                           [
                               ((0, 0), tokenizer_r.cls_token),
                               ((0, 1), '''A'''),
                               ((1, 2), ''','''),
                               ((3, 5), '''na'''),
                               ((5, 6), '''##ï'''),
                               ((6, 8), '''##ve'''),
                               ((9, 1_5), tokenizer_r.mask_token),
                               ((1_6, 2_1), '''Allen'''),
                               ((2_1, 2_3), '''##NL'''),
                               ((2_3, 2_4), '''##P'''),
                               ((2_5, 3_3), '''sentence'''),
                               ((3_3, 3_4), '''.'''),
                               ((0, 0), tokenizer_r.sep_token),
                           ]
                           if not do_lower_case
                           else [
                               ((0, 0), tokenizer_r.cls_token),
                               ((0, 1), '''a'''),
                               ((1, 2), ''','''),
                               ((3, 8), '''naive'''),
                               ((9, 1_5), tokenizer_r.mask_token),
                               ((1_6, 2_1), '''allen'''),
                               ((2_1, 2_3), '''##nl'''),
                               ((2_3, 2_4), '''##p'''),
                               ((2_5, 3_3), '''sentence'''),
                               ((3_3, 3_4), '''.'''),
                               ((0, 0), tokenizer_r.sep_token),
                           ]
                       )

                       self.assertEqual(
                           [e[1] for e in expected_results]    ,    tokenizer_r.convert_ids_to_tokens(tokens['''input_ids''']	)	)
                       self.assertEqual([e[0] for e in expected_results]    ,    tokens['''offset_mapping''']	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Optional[int]											=					['''的''', '''人''', '''有''']
           UpperCamelCase__     : str											=					''''''.join(a__	)
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                       UpperCamelCase__     : Dict											=					True
                       UpperCamelCase__     : Optional[int]											=					self.tokenizer_class.from_pretrained(a__    ,    **a__	)
                       UpperCamelCase__     : int											=					self.rust_tokenizer_class.from_pretrained(a__    ,    **a__	)

                       UpperCamelCase__     : Dict											=					tokenizer_p.encode(a__    ,    add_special_tokens=a__	)
                       UpperCamelCase__     : Optional[int]											=					tokenizer_r.encode(a__    ,    add_special_tokens=a__	)

                       UpperCamelCase__     : List[str]											=					tokenizer_r.convert_ids_to_tokens(a__	)
                       UpperCamelCase__     : Any											=					tokenizer_p.convert_ids_to_tokens(a__	)

                       # it is expected that each Chinese character is not preceded by "##"
                       self.assertListEqual(a__    ,    a__	)
                       self.assertListEqual(a__    ,    a__	)

                       UpperCamelCase__     : int											=					False
                       UpperCamelCase__     : List[Any]											=					self.rust_tokenizer_class.from_pretrained(a__    ,    **a__	)
                       UpperCamelCase__     : int											=					self.tokenizer_class.from_pretrained(a__    ,    **a__	)

                       UpperCamelCase__     : List[str]											=					tokenizer_r.encode(a__    ,    add_special_tokens=a__	)
                       UpperCamelCase__     : List[str]											=					tokenizer_p.encode(a__    ,    add_special_tokens=a__	)

                       UpperCamelCase__     : Union[str, Any]											=					tokenizer_r.convert_ids_to_tokens(a__	)
                       UpperCamelCase__     : str											=					tokenizer_p.convert_ids_to_tokens(a__	)

                       # it is expected that only the first Chinese character is not preceded by "##".
                       UpperCamelCase__     : Optional[Any]											=					[
                           F'''##{token}''' if idx != 0 else token for idx, token in enumerate(a__	)
                       ]
                       self.assertListEqual(a__    ,    a__	)
                       self.assertListEqual(a__    ,    a__	)









from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase      							=   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

       _lowerCamelCase				:  Optional[int]						=		[trans(img.convert('''RGB'''   )   ) for img in image]
       _lowerCamelCase				:  Dict						=		torch.stack(lowercase_   )
       return image







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase				:  Optional[Any]						=		DDIMScheduler.from_config(scheduler.config)

             self.register_modules(unet=a__   ,				scheduler=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             if strength < 0 or strength > 1:
                    raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}""")


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

             _lowerCamelCase				:  Tuple						=		max(num_inference_steps - init_timestep   ,				0)
             _lowerCamelCase				:  int						=		self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





             if not isinstance(a__   ,				(torch.Tensor, PIL.Image.Image, list)):
                    raise ValueError(
                        F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__)}""")

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

             if isinstance(a__   ,				a__) and len(a__) != batch_size:
                    raise ValueError(
                        F"""You have passed a list of generators of length {len(a__)}, but requested an effective batch"""
                        F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

             # get latents
             print('''add noise to latents at timestep'''   ,				a__)
             _lowerCamelCase				:  List[Any]						=		self.scheduler.add_noise(a__   ,				a__   ,				a__)
             _lowerCamelCase				:  List[Any]						=		init_latents

             return latents



      @torch.no_grad()
      def __call__(		self   ,				a__ = None   ,				a__ = 0.8   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = None   ,				a__ = "pil"   ,				a__ = True   ,				):



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

             # 3. set timesteps
             self.scheduler.set_timesteps(a__   ,				device=self.device)
             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.get_timesteps(a__   ,				a__   ,				self.device)
             _lowerCamelCase				:  Union[str, Any]						=		timesteps[:1].repeat(a__)

             # 4. Prepare latent variables
             _lowerCamelCase				:  int						=		self.prepare_latents(a__   ,				a__   ,				a__   ,				self.unet.dtype   ,				self.device   ,				a__)
             _lowerCamelCase				:  int						=		latents

             # 5. Denoising loop
             for t in self.progress_bar(a__):
                    # 1. predict noise model_output
                    _lowerCamelCase				:  Dict						=		self.unet(a__   ,				a__).sample

                    # 2. predict previous mean of image x_t-1 and add variance depending on eta
                    # eta corresponds to η in paper and should be between [0, 1]
                    # do x_t -> x_t-1
                    _lowerCamelCase				:  Tuple						=		self.scheduler.step(
                        a__   ,				a__   ,				a__   ,				eta=a__   ,				use_clipped_model_output=a__   ,				generator=a__   ,				).prev_sample

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image, latent_timestep.item())

             return ImagePipelineOutput(images=a__)

