





'''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  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Tuple								=       (
     "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."
 )
 __lowerCamelCase    :				int								=       "CIDAS/clipseg-rd64-refined"
 __lowerCamelCase    :				Union[str, Any]								=       "image_segmenter"
 __lowerCamelCase    :				Dict								=       CLIPSegForImageSegmentation

 __lowerCamelCase    :				Tuple								=       ["image", "text"]
 __lowerCamelCase    :				Optional[int]								=       ["image"]




 def __init__(						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Any:
     requires_backends(self      ,  ["vision"]		)
     super().__init__(*_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[str]:
     return self.pre_processor(text=[label]      ,  images=[image]      ,  padding=_lowerCAmelCase      ,  return_tensors="pt"		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Dict:
     with torch.no_grad():
         _lowerCAmelCase						      =   self.model(**_lowerCAmelCase		).logits
     return logits





 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   outputs.cpu().detach().numpy()
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   1
     return Image.fromarray((array * 255).astype(np.uinta		)		)




'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
