



from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__A      		=     transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  if isinstance(__lowerCamelCase			,	torch.Tensor					):
    return image
  elif isinstance(__lowerCamelCase			,	PIL.Image.Image					):
    _snake_case						=							[image]

  _snake_case						=							[trans(img.convert('''RGB'''					)					) for img in image]
  _snake_case						=							torch.stack(__lowerCamelCase					)
  return image



class   lowercase_   (  A_					):



      def __init__(      self				:    Union[str, Any]      , A__				:    int      , A__				:    Optional[int]		)   ->					Optional[int]:
        super().__init__()

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

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



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Tuple		)   ->					List[Any]:


        if strength < 0 or strength > 1:
          raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Union[str, Any]      , A__				:    List[Any]      , A__				:    Optional[Any]		)   ->					Dict:
        _snake_case						=							min(int(num_inference_steps * strength		)      , snake_case__		)

        _snake_case						=							max(num_inference_steps - init_timestep      , 0		)
        _snake_case						=							self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Union[str, Any]      , A__				:    Any      , A__				:    Any      , A__				:    Union[str, Any]      , A__				:    Dict      , A__				:    int=None		)   ->					List[str]:


        if not isinstance(snake_case__      , (torch.Tensor, PIL.Image.Image, list)		):
          raise ValueError(
              f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case__		)}"""		)

        _snake_case						=							image.to(device=snake_case__      , dtype=snake_case__		)

        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."""		)

        _snake_case						=							init_latents.shape
        _snake_case						=							randn_tensor(snake_case__      , generator=snake_case__      , device=snake_case__      , dtype=snake_case__		)

        # get latents
        print('''add noise to latents at timestep'''      , snake_case__		)
        _snake_case						=							self.scheduler.add_noise(snake_case__      , snake_case__      , snake_case__		)
        _snake_case						=							init_latents

        return latents







      @torch.no_grad()
      def __call__(      self				:    str      , A__				:    Union[torch.FloatTensor, PIL.Image.Image] = None      , A__				:    float = 0.8      , A__				:    int = 1      , A__				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None      , A__				:    float = 0.0      , A__				:    int = 50      , A__				:    Optional[bool] = None      , A__				:    Optional[str] = "pil"      , A__				:    bool = True      , )   ->					Union[ImagePipelineOutput, Tuple]:
        self.check_inputs(snake_case__		)

        # 2. Preprocess image
        _snake_case						=							preprocess(snake_case__		)

        # 3. set timesteps
        self.scheduler.set_timesteps(snake_case__      , device=self.device		)
        _snake_case						=							self.get_timesteps(snake_case__      , snake_case__      , self.device		)
        _snake_case						=							timesteps[:1].repeat(snake_case__		)

        # 4. Prepare latent variables
        _snake_case						=							self.prepare_latents(snake_case__      , snake_case__      , snake_case__      , self.unet.dtype      , self.device      , snake_case__		)
        _snake_case						=							latents

        # 5. Denoising loop
        for t in self.progress_bar(snake_case__		):
          # 1. predict noise model_output
          _snake_case						=							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
          _snake_case						=							self.scheduler.step(
              snake_case__      , snake_case__      , snake_case__      , eta=snake_case__      , use_clipped_model_output=snake_case__      , generator=snake_case__      , ).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(snake_case__		)

        if not return_dict:
          return (image, latent_timestep.item())

        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_tokenizers_available, is_torch_available


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

