




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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def   lowerCAmelCase						(						_lowerCAmelCase							:      Optional[int]		):




 """simple docstring"""







 UpperCAmelCase__       ,       UpperCAmelCase__							       =  image.size
 UpperCAmelCase__       ,       UpperCAmelCase__							       =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
 UpperCAmelCase__							       =  image.resize((w, h)  ,							resample=PIL_INTERPOLATION["lanczos"]		)
 UpperCAmelCase__							       =  np.array(_UpperCamelCase		).astype(np.floataa		) / 255.0
 UpperCAmelCase__							       =  image[None].transpose(0  ,							3  ,							1  ,							2		)
 UpperCAmelCase__							       =  torch.from_numpy(_UpperCamelCase		)
 return 2.0 * image - 1.0


class 	_UpperCamelCase					( A__						):
      def __init__(	self      :Tuple       ,					lowerCamelCase      :VQModel       ,					lowerCamelCase      :UNetaDModel       ,					lowerCamelCase      :Union[
              DDIMScheduler,
              PNDMScheduler,
              LMSDiscreteScheduler,
              EulerDiscreteScheduler,
              EulerAncestralDiscreteScheduler,
              DPMSolverMultistepScheduler,
          ]       ,					)   ->	int:
       super().__init__()
       self.register_modules(vqvae=_a       ,					unet=_a       ,					scheduler=_a					)







      @torch.no_grad()
      def __call__(	self      :Any       ,					lowerCamelCase      :Union[torch.Tensor, PIL.Image.Image] = None       ,					lowerCamelCase      :Optional[int] = 1       ,					lowerCamelCase      :Optional[int] = 100       ,					lowerCamelCase      :Optional[float] = 0.0       ,					lowerCamelCase      :Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,					lowerCamelCase      :Optional[str] = "pil"       ,					lowerCamelCase      :bool = True       ,					)   ->	Union[Tuple, ImagePipelineOutput]:






       if isinstance(_a       ,					PIL.Image.Image					):
        UpperCAmelCase__							       =  1
       elif isinstance(_a       ,					torch.Tensor					):
        UpperCAmelCase__							       =  image.shape[0]
       else:
        raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a					)}'''					)

       if isinstance(_a       ,					PIL.Image.Image					):
        UpperCAmelCase__							       =  preprocess(_a					)

       UpperCAmelCase__       ,       UpperCAmelCase__							       =  image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       UpperCAmelCase__							       =  (batch_size, self.unet.config.in_channels // 2, height, width)
       UpperCAmelCase__							       =  next(self.unet.parameters()					).dtype

       UpperCAmelCase__							       =  randn_tensor(_a       ,					generator=_a       ,					device=self.device       ,					dtype=_a					)

       UpperCAmelCase__							       =  image.to(device=self.device       ,					dtype=_a					)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(_a       ,					device=self.device					)
       UpperCAmelCase__							       =  self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase__							       =  latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCAmelCase__							       =  "eta" in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
       UpperCAmelCase__							       =  {}
       if accepts_eta:
        UpperCAmelCase__							       =  eta

       for t in self.progress_bar(_a					):
        # concat latents and low resolution image in the channel dimension.
        UpperCAmelCase__							       =  torch.cat([latents, image]       ,					dim=1					)
        UpperCAmelCase__							       =  self.scheduler.scale_model_input(_a       ,					_a					)
        # predict the noise residual
        UpperCAmelCase__							       =  self.unet(_a       ,					_a					).sample
        # compute the previous noisy sample x_t -> x_t-1
        UpperCAmelCase__							       =  self.scheduler.step(_a       ,					_a       ,					_a       ,					**_a					).prev_sample

       # decode the image latents with the VQVAE
       UpperCAmelCase__							       =  self.vqvae.decode(_a					).sample
       UpperCAmelCase__							       =  torch.clamp(_a       ,					-1.0       ,					1.0					)
       UpperCAmelCase__							       =  image / 2 + 0.5
       UpperCAmelCase__							       =  image.cpu().permute(0       ,					2       ,					3       ,					1					).numpy()

       if output_type == "pil":
        UpperCAmelCase__							       =  self.numpy_to_pil(_a					)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=_a					)








'''simple docstring'''
# 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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
     from PIL import Image







class 							A__    (       A__  ):
  A__		 =				(
      'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
      'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
      'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
  )
  A__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
