import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_			    =							logging.get_logger(__name__)  # pylint: disable=invalid-name




def       lowerCAmelCase_     (		lowercase:							Union[List, PIL.Image.Image, torch.Tensor]		)    ->				Union[str, Any]:







 '''simple docstring'''
 warnings.warn(
     '''The preprocess method is deprecated and will be removed in a future version. Please'''
     ''' use VaeImageProcessor.preprocess instead'''  ,					lowercase  ,					)
 if isinstance(lowercase  ,					torch.Tensor		):
  return image
 elif isinstance(lowercase  ,					PIL.Image.Image		):
  _UpperCamelCase:     Dict							= [image]

 if isinstance(image[0]  ,					PIL.Image.Image		):
  _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= image[0].size
  _UpperCamelCase	,							_UpperCamelCase:     Any							= (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

  _UpperCamelCase:     Optional[Any]							= [np.array(i.resize((w, h)  ,					resample=PIL_INTERPOLATION['''lanczos''']		)		)[None, :] for i in image]
  _UpperCamelCase:     List[Any]							= np.concatenate(lowercase  ,					axis=0		)
  _UpperCamelCase:     int							= np.array(lowercase		).astype(np.floataa		) / 255.0
  _UpperCamelCase:     Union[str, Any]							= image.transpose(0  ,					3  ,					1  ,					2		)
  _UpperCamelCase:     int							= 2.0 * image - 1.0
  _UpperCamelCase:     Any							= torch.from_numpy(lowercase		)
 elif isinstance(image[0]  ,					torch.Tensor		):
  _UpperCamelCase:     Tuple							= torch.cat(lowercase  ,					dim=0		)
 return image




def       lowerCAmelCase_     (		lowercase:							Union[List, PIL.Image.Image, torch.Tensor]		)    ->				Dict:







 '''simple docstring'''
 if isinstance(lowercase  ,					torch.Tensor		):
  return mask
 elif isinstance(lowercase  ,					PIL.Image.Image		):
  _UpperCamelCase:     Any							= [mask]

 if isinstance(mask[0]  ,					PIL.Image.Image		):
  _UpperCamelCase	,							_UpperCamelCase:     Dict							= mask[0].size
  _UpperCamelCase	,							_UpperCamelCase:     List[Any]							= (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  _UpperCamelCase:     List[Any]							= [np.array(m.convert('''L'''		).resize((w, h)  ,					resample=PIL_INTERPOLATION['''nearest''']		)		)[None, :] for m in mask]
  _UpperCamelCase:     List[Any]							= np.concatenate(lowercase  ,					axis=0		)
  _UpperCamelCase:     Dict							= mask.astype(np.floataa		) / 255.0
  _UpperCamelCase:     int							= 0
  _UpperCamelCase:     Optional[Any]							= 1
  _UpperCamelCase:     Union[str, Any]							= torch.from_numpy(lowercase		)
 elif isinstance(mask[0]  ,					torch.Tensor		):
  _UpperCamelCase:     Union[str, Any]							= torch.cat(lowercase  ,					dim=0		)
 return mask





class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    UNetaDModel
   lowerCAmelCase      :    RePaintScheduler



   def __init__(	self					:  Tuple		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Tuple							):



    """simple docstring"""



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



   @torch.no_grad()
   def __call__(	self					:  Optional[int]		,  _lowercase					:  Union[torch.Tensor, PIL.Image.Image]		,  _lowercase					:  Union[torch.Tensor, PIL.Image.Image]		,  _lowercase					:  int = 250		,  _lowercase					:  float = 0.0		,  _lowercase					:  int = 10		,  _lowercase					:  int = 10		,  _lowercase					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None		,  _lowercase					:  Optional[str] = "pil"		,  _lowercase					:  bool = True		,  ):



    """simple docstring"""



    _UpperCamelCase:     Optional[int]							= image

    _UpperCamelCase:     Any							= _preprocess_image(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= original_image.to(device=self.device		,  dtype=self.unet.dtype							)
    _UpperCamelCase:     Optional[Any]							= _preprocess_mask(_lowercase							)
    _UpperCamelCase:     Dict							= mask_image.to(device=self.device		,  dtype=self.unet.dtype							)

    _UpperCamelCase:     Optional[Any]							= original_image.shape[0]

    # sample gaussian noise to begin the loop
    if isinstance(_lowercase		,  _lowercase							) and len(_lowercase							) != batch_size:
     raise ValueError(
         f"""You have passed a list of generators of length {len(_lowercase							)}, but requested an effective batch"""
         f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)

    _UpperCamelCase:     str							= original_image.shape
    _UpperCamelCase:     List[str]							= randn_tensor(_lowercase		,  generator=_lowercase		,  device=self.device		,  dtype=self.unet.dtype							)

    # set step values
    self.scheduler.set_timesteps(_lowercase		,  _lowercase		,  _lowercase		,  self.device							)
    _UpperCamelCase:     Any							= eta

    _UpperCamelCase:     Optional[int]							= self.scheduler.timesteps[0] + 1
    _UpperCamelCase:     str							= generator[0] if isinstance(_lowercase		,  _lowercase							) else generator
    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
     if t < t_last:
      # predict the noise residual
      _UpperCamelCase:     Union[str, Any]							= self.unet(_lowercase		,  _lowercase							).sample
      # compute previous image: x_t -> x_t-1
      _UpperCamelCase:     Optional[int]							= self.scheduler.step(_lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase							).prev_sample

     else:
      # compute the reverse: x_t-1 -> x_t
      _UpperCamelCase:     Any							= self.scheduler.undo_step(_lowercase		,  _lowercase		,  _lowercase							)
     _UpperCamelCase:     Any							= t

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

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=_lowercase							)
# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
       '''and returns a summary of the text.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)[0]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.pre_processor.decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)