




"""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__			(  __SCREAMING_SNAKE_CASE		):






			'''simple docstring'''



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

							# make sure scheduler can always be converted to DDIM
							_lowerCamelCase  :    Optional[int]					=					DDIMScheduler.from_config(scheduler.config						)

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






			@torch.no_grad()
			def __call__(						self						,   lowercase = 1						,   lowercase = None						,   lowercase = 0.0						,   lowercase = 50						,   lowercase = None						,   lowercase = "pil"						,   lowercase = True						,   ):

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,   _a						):
											_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 isinstance(_a						,   _a						) and len(_a						) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(_a						)}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

							_lowerCamelCase  :    Dict					=					randn_tensor(_a						,   generator=_a						,   device=self.device						,   dtype=self.unet.dtype						)

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

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

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

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=_a						)





"""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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """deit"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.0						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=224						,   lowercase=16						,   lowercase=3						,   lowercase=True						,   lowercase=16						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    List[str]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					num_attention_heads
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    str					=					attention_probs_dropout_prob
							_lowerCamelCase  :    str					=					initializer_range
							_lowerCamelCase  :    Union[str, Any]					=					layer_norm_eps
							_lowerCamelCase  :    Optional[Any]					=					image_size
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    Dict					=					num_channels
							_lowerCamelCase  :    Dict					=					qkv_bias
							_lowerCamelCase  :    Dict					=					encoder_stride






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    version.parse("""1.11"""		)
			@property
			def       A_					(						self						):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]						)






			@property
			def       A_					(						self						):
							return 1E-4