






"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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




class 	__UpperCamelCase    (    UpperCamelCase	):
					def __init__(       self       :  Optional[Any]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Tuple      )  ->					List[Any]:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase    ,   scheduler=UpperCAmelCase      )
					@torch.no_grad()
					def __call__(       self       :  List[Any]    ,   UpperCAmelCase       :  int = 1    ,   UpperCAmelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   UpperCAmelCase       :  int = 1000    ,   UpperCAmelCase       :  Optional[str] = "pil"    ,   UpperCAmelCase       :  bool = True    ,   )  ->					Union[ImagePipelineOutput, Tuple]:
											# Sample gaussian noise to begin loop
											if isinstance(self.unet.config.sample_size    ,   UpperCAmelCase      ):
																	lowerCAmelCase      :Optional[int]				  = (
																	    batch_size,
																	    self.unet.config.in_channels,
																	    self.unet.config.sample_size,
																	    self.unet.config.sample_size,
																	)
											else:
																	lowerCAmelCase      :str				  = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

											if self.device.type == "mps":
																	# randn does not work reproducibly on mps
																	lowerCAmelCase      :str				  = randn_tensor(UpperCAmelCase    ,   generator=UpperCAmelCase      )
																	lowerCAmelCase      :Tuple				  = image.to(self.device      )
											else:
																	lowerCAmelCase      :str				  = randn_tensor(UpperCAmelCase    ,   generator=UpperCAmelCase    ,   device=self.device      )

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

											for t in self.progress_bar(self.scheduler.timesteps      ):
																	# 1. predict noise model_output
																	lowerCAmelCase      :int				  = self.unet(UpperCAmelCase    ,   UpperCAmelCase      ).sample

																	# 2. compute previous image: x_t -> x_t-1
																	lowerCAmelCase      :str				  = self.scheduler.step(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   generator=UpperCAmelCase      ).prev_sample

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

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase      )







"""simple docstring"""


import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir




@dataclass
class 	__UpperCamelCase    :
					lowercase_						:							str




class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[str]				  = get_tests_dir('fixtures/test_sentencepiece.model'      )
											lowerCAmelCase      :Tuple				  = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model'      )

											lowerCAmelCase      :List[str]				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :Dict				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   0      )

											lowerCAmelCase      :int				  = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

											with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
																	lowerCAmelCase      :List[str]				  = SpmConverter(UpperCAmelCase      )
											self.assertEqual(len(UpperCAmelCase      )    ,   1      )

											self.assertIn(
											    'The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'
											    ' which is not implemented in the fast tokenizers.'    ,   str(w[0].message      )    ,   )