
"""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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				def __init__(					self	:			int				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			List[str]  )		->  Dict:







											"""simple docstring"""




											super().__init__()

											# make sure scheduler can always be converted to DDIM
											_lowerCAmelCase  	=			DDIMScheduler.from_config(scheduler.config  )

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







				@torch.no_grad()
				def __call__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			int = 1				,     UpperCAmelCase_	:			Optional[Union[torch.Generator, List[torch.Generator]]] = None				,     UpperCAmelCase_	:			float = 0.0				,     UpperCAmelCase_	:			int = 50				,     UpperCAmelCase_	:			Optional[bool] = None				,     UpperCAmelCase_	:			Optional[str] = "pil"				,     UpperCAmelCase_	:			bool = True				,     )		->  Union[ImagePipelineOutput, Tuple]:







											"""simple docstring"""




											if isinstance(self.unet.config.sample_size				,     UpperCAmelCase_  ):
																		_lowerCAmelCase  	=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
											else:
																		_lowerCAmelCase  	=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

											_lowerCAmelCase  	=			randn_tensor(UpperCAmelCase_				,     generator=UpperCAmelCase_				,     device=self.device				,     dtype=self.unet.dtype  )

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

											for t in self.progress_bar(self.scheduler.timesteps  ):
																		# 1. predict noise model_output
																		_lowerCAmelCase  	=			self.unet(UpperCAmelCase_				,     UpperCAmelCase_  ).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  	=			self.scheduler.step(
																		    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     eta=UpperCAmelCase_				,     use_clipped_model_output=UpperCAmelCase_				,     generator=UpperCAmelCase_  ).prev_sample

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase_  )








"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"mobilenet_v1"


				def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			str=224				,     UpperCAmelCase_	:			Union[str, Any]=1.0				,     UpperCAmelCase_	:			List[str]=8				,     UpperCAmelCase_	:			Union[str, Any]="relu6"				,     UpperCAmelCase_	:			Optional[Any]=True				,     UpperCAmelCase_	:			Optional[int]=0.999				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Union[str, Any]=0.001				,     **UpperCAmelCase_	:			List[Any]				,     )		->  Dict:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )

											if depth_multiplier <= 0:
																		raise ValueError('depth_multiplier must be greater than zero.'  )

											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			depth_multiplier
											_lowerCAmelCase  	=			min_depth
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			tf_padding
											_lowerCAmelCase  	=			classifier_dropout_prob
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					version.parse("1.11"							)


				@property
				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											return OrderedDict([('pixel_values', {0: 'batch'})]  )


				@property
				def 				__lowerCamelCase	(					self	:			Dict  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											if self.task == "image-classification":
																		return OrderedDict([('logits', {0: 'batch'})]  )
											else:
																		return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]  )







				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  float:







											"""simple docstring"""




											return 1E-4






