






"""simple docstring"""




from typing import List, Optional, Tuple, Union

import torch

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




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         UpperCamelCase_: Union[str, Any]   				=    DDIMScheduler.from_config(scheduler.config							)

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





       @torch.no_grad()
       def __call__( self  ,      _lowerCamelCase = 1  ,      _lowerCamelCase = None  ,      _lowerCamelCase = 0.0  ,      _lowerCamelCase = 5_0  ,      _lowerCamelCase = None  ,      _lowerCamelCase = "pil"  ,      _lowerCamelCase = True  ,      ):

         # Sample gaussian noise to begin loop
         if isinstance(self.unet.config.sample_size  ,      _lowerCamelCase							):
           UpperCamelCase_: Optional[int]   				=    (
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
         else:
           UpperCamelCase_: Tuple   				=    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

         UpperCamelCase_: Optional[int]   				=    randn_tensor(_lowerCamelCase  ,      generator=_lowerCamelCase  ,      device=self.device  ,      dtype=self.unet.dtype							)

         # set step values
         self.scheduler.set_timesteps(_lowerCamelCase							)

         for t in self.progress_bar(self.scheduler.timesteps							):
           # 1. predict noise model_output
           UpperCamelCase_: Tuple   				=    self.unet(_lowerCamelCase  ,      _lowerCamelCase							).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
           UpperCamelCase_: Union[str, Any]   				=    self.scheduler.step(
               _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      eta=_lowerCamelCase  ,      use_clipped_model_output=_lowerCamelCase  ,      generator=_lowerCamelCase							).prev_sample

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

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCamelCase							)







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class     _lowerCAmelCase(	UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Optional[int]									=RoFormerTokenizer
       a    :		int									=RoFormerTokenizerFast
       a    :		int									=True
       a    :		Optional[int]									=True





       def       _a   ( self							):
         super().setUp()





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      **_lowerCamelCase							):
         return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'  ,      **_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    '永和服装饰品有限公司,今天天气非常好'
         UpperCamelCase_: Any   				=    '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
         return input_text, output_text





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.get_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: int   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Tuple   				=    tokenizer.tokenize(_lowerCamelCase							)

         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)

         UpperCamelCase_: Dict   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Dict   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.get_rust_tokenizer()
         UpperCamelCase_				,UpperCamelCase_: Tuple   				=    self.get_chinese_input_output_texts()
         UpperCamelCase_: Optional[Any]   				=    tokenizer.tokenize(_lowerCamelCase							)
         self.assertListEqual(_lowerCamelCase  ,      output_text.split()							)
         UpperCamelCase_: str   				=    tokens + [tokenizer.unk_token]
         UpperCamelCase_: Optional[Any]   				=    [2_2_9_4_3, 2_1_3_3_2, 3_4_4_3_1, 4_5_9_0_4, 1_1_7, 3_0_6, 1_2_3_1, 1_2_3_1, 2_6_5_3, 3_3_9_9_4, 1_2_6_6, 1_0_0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase							)  ,      _lowerCamelCase							)





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass