



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 				_snake_case			(					lowercase__):
    UpperCamelCase__				:					UNetaDModel
    UpperCamelCase__				:					KarrasVeScheduler




    def __init__(						self					:						Dict,					__lowercase					:						UNetaDModel,					__lowercase					:						KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=__lowercase,					scheduler=__lowercase						)




    @torch.no_grad()
    def __call__(						self					:						Optional[Any],					__lowercase					:						int = 1,					__lowercase					:						int = 50,					__lowercase					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__lowercase					:						Optional[str] = "pil",					__lowercase					:						bool = True,					**__lowercase					:						int,					):
       lowercase__					  =				self.unet.config.sample_size
       lowercase__					  =				(batch_size, 3, img_size, img_size)

       lowercase__					  =				self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       lowercase__					  =				randn_tensor(__lowercase,					generator=__lowercase,					device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(__lowercase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
          # here sigma_t == t_i from the paper
          lowercase__					  =				self.scheduler.schedule[t]
          lowercase__					  =				self.scheduler.schedule[t - 1] if t > 0 else 0

          # 1. Select temporarily increased noise level sigma_hat
          # 2. Add new noise to move from sample_i to sample_hat
          lowercase__  ,       lowercase__					  =				self.scheduler.add_noise_to_input(__lowercase,					__lowercase,					generator=__lowercase						)

          # 3. Predict the noise residual given the noise magnitude `sigma_hat`
          # The model inputs and output are adjusted by following eq. (213) in [1].
          lowercase__					  =				(sigma_hat / 2) * model((sample_hat + 1) / 2,					sigma_hat / 2						).sample

          # 4. Evaluate dx/dt at sigma_hat
          # 5. Take Euler step from sigma to sigma_prev
          lowercase__					  =				self.scheduler.step(__lowercase,					__lowercase,					__lowercase,					__lowercase						)

          if sigma_prev != 0:
             # 6. Apply 2nd order correction
             # The model inputs and output are adjusted by following eq. (213) in [1].
             lowercase__					  =				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2,					sigma_prev / 2						).sample
             lowercase__					  =				self.scheduler.step_correct(
                 __lowercase,					__lowercase,					__lowercase,					__lowercase,					step_output.prev_sample,					step_output["derivative"],					)
          lowercase__					  =				step_output.prev_sample

       lowercase__					  =				(sample / 2 + 0.5).clamp(0,					1						)
       lowercase__					  =				sample.cpu().permute(0,					2,					3,					1						).numpy()
       if output_type == "pil":
          lowercase__					  =				self.numpy_to_pil(__lowercase						)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=__lowercase						)









# 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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XmodForCausalLM""",
							    """XmodForMaskedLM""",
							    """XmodForMultipleChoice""",
							    """XmodForQuestionAnswering""",
							    """XmodForSequenceClassification""",
							    """XmodForTokenClassification""",
							    """XmodModel""",
							    """XmodPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xmod import (
														    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XmodForCausalLM,
														    XmodForMaskedLM,
														    XmodForMultipleChoice,
														    XmodForQuestionAnswering,
														    XmodForSequenceClassification,
														    XmodForTokenClassification,
														    XmodModel,
														    XmodPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




