


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__(    snake_case__						):






    '''simple docstring'''





    def __init__(	self				:   str       ,   __snake_case				:   int       ,   __snake_case				:   List[str]	):





       '''simple docstring'''
       super().__init__()

       # make sure scheduler can always be converted to DDIM
       UpperCAmelCase_							:      str						=   DDIMScheduler.from_config(scheduler.config	)

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





    @torch.no_grad()
    def __call__(	self				:   Union[str, Any]       ,   __snake_case				:   int = 1       ,   __snake_case				:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,   __snake_case				:   float = 0.0       ,   __snake_case				:   int = 50       ,   __snake_case				:   Optional[bool] = None       ,   __snake_case				:   Optional[str] = "pil"       ,   __snake_case				:   bool = True       ,   ):





       '''simple docstring'''

       # Sample gaussian noise to begin loop
       if isinstance(self.unet.config.sample_size       ,   __snake_case	):
          UpperCAmelCase_							:      Dict						=   (
              batch_size,
              self.unet.config.in_channels,
              self.unet.config.sample_size,
              self.unet.config.sample_size,
          )
       else:
          UpperCAmelCase_							:      List[str]						=   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

       UpperCAmelCase_							:      Dict						=   randn_tensor(__snake_case       ,   generator=__snake_case       ,   device=self.device       ,   dtype=self.unet.dtype	)

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

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

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

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=__snake_case	)




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase				:  List[Any]      					=							{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  Optional[Any]      					=							['VivitImageProcessor']


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase				:  List[Any]      					=							[
           'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'VivitModel',
           'VivitPreTrainedModel',
           'VivitForVideoClassification',
       ]


if TYPE_CHECKING:
       from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vivit import VivitImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vivit import (
                  VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VivitForVideoClassification,
                  VivitModel,
                  VivitPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase				:  str      					=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)