


'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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





class 					lowercase__						(		_snake_case      ):





							'''simple docstring'''


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






							@torch.no_grad()
							def __call__(					self					,      __snake_case = 1					,      __snake_case = None					,      __snake_case = 1000					,      __snake_case = "pil"					,      __snake_case = True					,      ):
											# Sample gaussian noise to begin loop
											if isinstance(self.unet.config.sample_size					,      __snake_case						):
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   (
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
											else:
															_SCREAMING_SNAKE_CASE				: Dict  	=   (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				: Union[str, Any]  	=   randn_tensor(__snake_case					,      generator=__snake_case						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   image.to(self.device						)
											else:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   randn_tensor(__snake_case					,      generator=__snake_case					,      device=self.device						)

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

											for t in self.progress_bar(self.scheduler.timesteps						):
															# 1. predict noise model_output
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.unet(__snake_case					,      __snake_case						).sample

															# 2. compute previous image: x_t -> x_t-1
															_SCREAMING_SNAKE_CASE				: List[Any]  	=   self.scheduler.step(__snake_case					,      __snake_case					,      __snake_case					,      generator=__snake_case						).prev_sample

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   (image / 2 + 0.5).clamp(0					,      1						)
											_SCREAMING_SNAKE_CASE				: Dict  	=   image.cpu().permute(0					,      2					,      3					,      1						).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.numpy_to_pil(__snake_case						)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=__snake_case						)








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_	:     Union[str, Any]							     =	{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Dict							     =	['PegasusTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	['PegasusTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	[
				    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'PegasusForCausalLM',
				    'PegasusForConditionalGeneration',
				    'PegasusModel',
				    'PegasusPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[Any]							     =	[
				    'TFPegasusForConditionalGeneration',
				    'TFPegasusModel',
				    'TFPegasusPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'FlaxPegasusForConditionalGeneration',
				    'FlaxPegasusModel',
				    'FlaxPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus import PegasusTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus_fast import PegasusTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_pegasus import (
								    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PegasusForCausalLM,
								    PegasusForConditionalGeneration,
								    PegasusModel,
								    PegasusPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_pegasus import (
								    FlaxPegasusForConditionalGeneration,
								    FlaxPegasusModel,
								    FlaxPegasusPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     List[str]							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




