


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   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     int      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     List[str]    ):
            super().__init__()

            # make sure scheduler can always be converted to DDIM
            a_    :     Union[str, Any]										=    DDIMScheduler.from_config(scheduler.config    )

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

       @torch.no_grad()
       def __call__(   self      :     str      ,							lowerCamelCase_      :     int = 1      ,							lowerCamelCase_      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     int = 5_0      ,							lowerCamelCase_      :     Optional[bool] = None      ,							lowerCamelCase_      :     Optional[str] = "pil"      ,							lowerCamelCase_      :     bool = True      ,							):

            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size      ,							lowerCamelCase_    ):
                 a_    :     List[Any]										=    (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
            else:
                 a_    :     List[Any]										=    (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

            a_    :     Tuple										=    randn_tensor(lowerCamelCase_      ,							generator=lowerCamelCase_      ,							device=self.device      ,							dtype=self.unet.dtype    )

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

            for t in self.progress_bar(self.scheduler.timesteps    ):
                 # 1. predict noise model_output
                 a_    :     Union[str, Any]										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    ).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
                 a_    :     Tuple										=    self.scheduler.step(
                     lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_      ,							eta=lowerCamelCase_      ,							use_clipped_model_output=lowerCamelCase_      ,							generator=lowerCamelCase_    ).prev_sample

            a_    :     int										=    (image / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    image.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            if output_type == "pil":
                 a_    :     Union[str, Any]										=    self.numpy_to_pil(lowerCamelCase_    )

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=lowerCamelCase_    )







from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase        =     logging.get_logger(__name__)

__lowerCamelCase        =     {
    '''facebook/convnextv2-tiny-1k-224''': '''https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json''',
}


class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):
       lowerCamelCase__:     str							    =     """convnextv2"""

       def __init__(   self      :     Tuple      ,							lowerCamelCase_      :     str=3      ,							lowerCamelCase_      :     Tuple=4      ,							lowerCamelCase_      :     Any=4      ,							lowerCamelCase_      :     Optional[int]=None      ,							lowerCamelCase_      :     Optional[int]=None      ,							lowerCamelCase_      :     Optional[int]="gelu"      ,							lowerCamelCase_      :     Optional[Any]=0.0_2      ,							lowerCamelCase_      :     str=1E-12      ,							lowerCamelCase_      :     Dict=0.0      ,							lowerCamelCase_      :     Union[str, Any]=2_2_4      ,							lowerCamelCase_      :     Dict=None      ,							lowerCamelCase_      :     Optional[Any]=None      ,							**lowerCamelCase_      :     List[Any]      ,							):
            super().__init__(**lowerCamelCase_    )

            a_    :     Dict										=    num_channels
            a_    :     Optional[int]										=    patch_size
            a_    :     int										=    num_stages
            a_    :     Any										=    [9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
            a_    :     Any										=    [3, 3, 9, 3] if depths is None else depths
            a_    :     Any										=    hidden_act
            a_    :     Dict										=    initializer_range
            a_    :     Optional[Any]										=    layer_norm_eps
            a_    :     int										=    drop_path_rate
            a_    :     Optional[Any]										=    image_size
            a_    :     Dict										=    ["""stem"""] + [F'''stage{idx}''' for idx in range(1      ,							len(self.depths    ) + 1    )]
            a_			,			a_    :     int										=    get_aligned_output_features_output_indices(
                out_features=lowerCamelCase_      ,							out_indices=lowerCamelCase_      ,							stage_names=self.stage_names    )



