

"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a__			:						Optional[int]							=			logging.get_logger(__name__)

a__			:						str							=			{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}


class       _snake_case  (    SCREAMING_SNAKE_CASE_							):
						__snake_case			      =  'qdqbert'







						def __init__(self  ,				UpperCAmelCase_=3_0_5_2_2  ,				UpperCAmelCase_=7_6_8  ,				UpperCAmelCase_=1_2  ,				UpperCAmelCase_=1_2  ,				UpperCAmelCase_=3_0_7_2  ,				UpperCAmelCase_="gelu"  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=0.1  ,				UpperCAmelCase_=5_1_2  ,				UpperCAmelCase_=2  ,				UpperCAmelCase_=0.0_2  ,				UpperCAmelCase_=1e-12  ,				UpperCAmelCase_=True  ,				UpperCAmelCase_=1  ,				UpperCAmelCase_=0  ,				UpperCAmelCase_=2  ,				**UpperCAmelCase_  ,				)      ->    int:
											super().__init__(pad_token_id=lowercase_  ,				bos_token_id=lowercase_  ,				eos_token_id=lowercase_  ,				**lowercase_				)

											__lowercase        =      vocab_size
											__lowercase        =      max_position_embeddings
											__lowercase        =      hidden_size
											__lowercase        =      num_hidden_layers
											__lowercase        =      num_attention_heads
											__lowercase        =      intermediate_size
											__lowercase        =      hidden_act
											__lowercase        =      hidden_dropout_prob
											__lowercase        =      attention_probs_dropout_prob
											__lowercase        =      initializer_range
											__lowercase        =      type_vocab_size
											__lowercase        =      layer_norm_eps
											__lowercase        =      use_cache







"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

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







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

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

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





