


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


UpperCAmelCase   :    Dict     =		logging.get_logger(__name__)

UpperCAmelCase   :    Any     =		{
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Any			       =		"cvt"







		def __init__( self		,      A=3		,      A=[7, 3, 3]		,      A=[4, 2, 2]		,      A=[2, 1, 1]		,      A=[64, 1_92, 3_84]		,      A=[1, 3, 6]		,      A=[1, 2, 10]		,      A=[4.0, 4.0, 4.0]		,      A=[0.0, 0.0, 0.0]		,      A=[0.0, 0.0, 0.0]		,      A=[0.0, 0.0, 0.1]		,      A=[True, True, True]		,      A=[False, False, True]		,      A=["dw_bn", "dw_bn", "dw_bn"]		,      A=[3, 3, 3]		,      A=[1, 1, 1]		,      A=[2, 2, 2]		,      A=[1, 1, 1]		,      A=[1, 1, 1]		,      A=0.02		,      A=1e-1_2		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(**A	)
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_sizes
					lowerCamelCase    		=  patch_stride
					lowerCamelCase    		=  patch_padding
					lowerCamelCase    		=  embed_dim
					lowerCamelCase    		=  num_heads
					lowerCamelCase    		=  depth
					lowerCamelCase    		=  mlp_ratio
					lowerCamelCase    		=  attention_drop_rate
					lowerCamelCase    		=  drop_rate
					lowerCamelCase    		=  drop_path_rate
					lowerCamelCase    		=  qkv_bias
					lowerCamelCase    		=  cls_token
					lowerCamelCase    		=  qkv_projection_method
					lowerCamelCase    		=  kernel_qkv
					lowerCamelCase    		=  padding_kv
					lowerCamelCase    		=  stride_kv
					lowerCamelCase    		=  padding_q
					lowerCamelCase    		=  stride_q
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

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

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  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    		=  self.scheduler.step(A		,      A		,      A	).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(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

