



"""simple docstring"""






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					@cached_property
					def 						snake_case			(			self				:    int	)->   Optional[int]:
								return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''	)



					@cached_property
					def 						snake_case			(			self				:    int	)->   str:
								return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''	)



					def 						snake_case			(			self				:    List[Any]	)->   int:
								lowerCamelCase__						:						Optional[int]						      =self.tokenizer_ab
								lowerCamelCase__						:						Tuple						      =''' I am a small frog.'''
								lowerCamelCase__						:						Optional[int]						      =tok([src_text],    padding=lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
								lowerCamelCase__						:						str						      =tok.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase,    clean_up_tokenization_spaces=lowerCamelCase	)[0]
								assert src_text == decoded



					def 						snake_case			(			self				:    Union[str, Any]	)->   Union[str, Any]:
								lowerCamelCase__						:						Any						      =self.rust_tokenizer_ab
								lowerCamelCase__						:						str						      =''' I am a small frog.'''
								lowerCamelCase__						:						Optional[int]						      =tok([src_text],    padding=lowerCamelCase,    truncation=lowerCamelCase	)['''input_ids''']
								lowerCamelCase__						:						Optional[Any]						      =tok.batch_decode(lowerCamelCase,    skip_special_tokens=lowerCamelCase,    clean_up_tokenization_spaces=lowerCamelCase	)[0]
								assert src_text == decoded



					def 						snake_case			(			self				:    List[str]	)->   Union[str, Any]:
								assert self.tokenizer_ab.add_prefix_space
								assert self.tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[5502, 2], [5502, 2]]




					def 						snake_case			(			self				:    Optional[Any]	)->   str:
								assert self.rust_tokenizer_ab.add_prefix_space
								assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']	).input_ids == [[5502, 2], [5502, 2]]






'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

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


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			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
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

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

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )