"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = 42
       SCREAMING_SNAKE_CASE_						      = 42







       def __init__(					self,				lowerCAmelCase__,				lowerCAmelCase__) -> int:
            super().__init__()

            snake_case_   			=   PNDMScheduler.from_config(scheduler.config)

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







       @torch.no_grad()
       def __call__(					self,				lowerCAmelCase__ = 1,				lowerCAmelCase__ = 50,				lowerCAmelCase__ = None,				lowerCAmelCase__ = "pil",				lowerCAmelCase__ = True,				**lowerCAmelCase__,				) -> Union[ImagePipelineOutput, Tuple]:
            snake_case_   			=   randn_tensor(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),				generator=lowerCAmelCase__,				device=self.device,				)

            self.scheduler.set_timesteps(lowerCAmelCase__)
            for t in self.progress_bar(self.scheduler.timesteps):
                 snake_case_   			=   self.unet(lowerCAmelCase__,				lowerCAmelCase__).sample

                 snake_case_   			=   self.scheduler.step(lowerCAmelCase__,				lowerCAmelCase__,				lowerCAmelCase__).prev_sample

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

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__)


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "nystromformer"







       def __init__(					self,				lowerCAmelCase__=3_0000,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu_new",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=510,				lowerCAmelCase__=2,				lowerCAmelCase__=64,				lowerCAmelCase__=64,				lowerCAmelCase__=65,				lowerCAmelCase__=False,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-5,				lowerCAmelCase__=1,				lowerCAmelCase__=0,				lowerCAmelCase__=2,				**lowerCAmelCase__,				) -> Optional[Any]:
            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   segment_means_seq_len
            snake_case_   			=   num_landmarks
            snake_case_   			=   conv_kernel_size
            snake_case_   			=   inv_coeff_init_option
            snake_case_   			=   layer_norm_eps
            super().__init__(pad_token_id=lowerCAmelCase__,				bos_token_id=lowerCAmelCase__,				eos_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

