


'''simple docstring'''




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


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





def   _a		(lowercase__			:   Union[List, PIL.Image.Image, torch.Tensor]						)			->			Optional[int]:






     """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						):
          __snake_case           =						[image]

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

          __snake_case           =						[np.array(i.resize((w, h)							,   resample=PIL_INTERPOLATION['lanczos']						)						)[None, :] for i in image]
          __snake_case           =						np.concatenate(lowercase__							,   axis=0						)
          __snake_case           =						np.array(lowercase__						).astype(np.floataa						) / 2_55.0
          __snake_case           =						image.transpose(0							,   3							,   1							,   2						)
          __snake_case           =						2.0 * image - 1.0
          __snake_case           =						torch.from_numpy(lowercase__						)
     elif isinstance(image[0]							,   torch.Tensor						):
          __snake_case           =						torch.cat(lowercase__							,   dim=0						)
     return image





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






     """simple docstring"""
     if isinstance(lowercase__							,   torch.Tensor						):
          return mask
     elif isinstance(lowercase__							,   PIL.Image.Image						):
          __snake_case           =						[mask]

     if isinstance(mask[0]							,   PIL.Image.Image						):
          __snake_case      ,       __snake_case           =						mask[0].size
          __snake_case      ,       __snake_case           =						(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
          __snake_case           =						[np.array(m.convert('L'						).resize((w, h)							,   resample=PIL_INTERPOLATION['nearest']						)						)[None, :] for m in mask]
          __snake_case           =						np.concatenate(lowercase__							,   axis=0						)
          __snake_case           =						mask.astype(np.floataa						) / 2_55.0
          __snake_case           =						0
          __snake_case           =						1
          __snake_case           =						torch.from_numpy(lowercase__						)
     elif isinstance(mask[0]							,   torch.Tensor						):
          __snake_case           =						torch.cat(lowercase__							,   dim=0						)
     return mask




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  UNetaDModel
   _SCREAMING_SNAKE_CASE  :  RePaintScheduler
   def __init__(						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      Tuple       ,   SCREAMING_SNAKE_CASE_       :      Tuple      ) ->							Optional[Any]:
        super().__init__()
        self.register_modules(unet=SCREAMING_SNAKE_CASE_       ,   scheduler=SCREAMING_SNAKE_CASE_      )





   @torch.no_grad()
   def __call__(						self       :      List[Any]       ,   SCREAMING_SNAKE_CASE_       :      Union[torch.Tensor, PIL.Image.Image]       ,   SCREAMING_SNAKE_CASE_       :      Union[torch.Tensor, PIL.Image.Image]       ,   SCREAMING_SNAKE_CASE_       :      int = 250       ,   SCREAMING_SNAKE_CASE_       :      float = 0.0       ,   SCREAMING_SNAKE_CASE_       :      int = 10       ,   SCREAMING_SNAKE_CASE_       :      int = 10       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[str] = "pil"       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   ) ->							Union[ImagePipelineOutput, Tuple]:
        __snake_case           =						image

        __snake_case           =						_preprocess_image(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						original_image.to(device=self.device       ,   dtype=self.unet.dtype      )
        __snake_case           =						_preprocess_mask(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						mask_image.to(device=self.device       ,   dtype=self.unet.dtype      )

        __snake_case           =						original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) and len(SCREAMING_SNAKE_CASE_      ) != batch_size:
             raise ValueError(
                 f'You have passed a list of generators of length {len(SCREAMING_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           =						original_image.shape
        __snake_case           =						randn_tensor(SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_       ,   device=self.device       ,   dtype=self.unet.dtype      )

        # set step values
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   self.device      )
        __snake_case           =						eta

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

             else:
                  # compute the reverse: x_t-1 -> x_t
                  __snake_case           =						self.scheduler.undo_step(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
             __snake_case           =						t

        __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(SCREAMING_SNAKE_CASE_      )

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_      )








import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin




class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_="resnet50"       ,  lowercase_=3       ,  lowercase_=32       ,  lowercase_=3       ,  lowercase_=True       ,  lowercase_=True       ,  )  -> Union[str, Any]:
											a__      =parent
											a__      =out_indices if out_indices is not None else [4]
											a__      =stage_names
											a__      =out_features
											a__      =backbone
											a__      =batch_size
											a__      =image_size
											a__      =num_channels
											a__      =use_pretrained_backbone
											a__      =is_training





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
											a__      =self.get_config()

											return config, pixel_values





				def 						__UpperCamelCase    (				self)  -> Tuple:
											return TimmBackboneConfig(
											    image_size=self.image_size       ,  num_channels=self.num_channels       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  stage_names=self.stage_names       ,  use_pretrained_backbone=self.use_pretrained_backbone       ,  backbone=self.backbone       ,  )





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =TimmBackbone(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											with torch.no_grad():
																		a__      =model(lowercase_)
											self.parent.assertEqual(
											    result.feature_map[-1].shape       ,  (self.batch_size, model.channels[-1], 14, 14)       ,  )





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
@require_timm
class       lowercase_					(lowercase__      ,					lowercase__      ,					lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(TimmBackbone,) if is_torch_available() else ()
				snake_case	    ={'feature-extraction': TimmBackbone} if is_torch_available() else {}
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =TimmBackboneModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()





				def 						__UpperCamelCase    (				self)  -> str:
											a__      ='resnet18'
											a__      ='microsoft/resnet-18'

											a__      =AutoBackbone.from_pretrained(lowercase_       ,  use_timm_backbone=lowercase_)
											a__      =AutoBackbone.from_pretrained(lowercase_)

											self.assertEqual(len(timm_model.out_features)       ,  len(transformers_model.out_features))
											self.assertEqual(len(timm_model.stage_names)       ,  len(transformers_model.stage_names))
											self.assertEqual(timm_model.channels       ,  transformers_model.channels)
											# Out indices are set to the last layer by default. For timm models, we don't know
											# the number of layers in advance, so we set it to (-1,), whereas for transformers
											# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
											self.assertEqual(timm_model.out_indices       ,  (-1,))
											self.assertEqual(transformers_model.out_indices       ,  [len(timm_model.stage_names) - 1])

											a__      =AutoBackbone.from_pretrained(lowercase_       ,  use_timm_backbone=lowercase_       ,  out_indices=[1, 2, 3])
											a__      =AutoBackbone.from_pretrained(lowercase_       ,  out_indices=[1, 2, 3])

											self.assertEqual(timm_model.out_indices       ,  transformers_model.out_indices)
											self.assertEqual(len(timm_model.out_features)       ,  len(transformers_model.out_features))
											self.assertEqual(timm_model.channels       ,  transformers_model.channels)





				@unittest.skip('TimmBackbone doesn\'t support feed forward chunking')
				def 						__UpperCamelCase    (				self)  -> int:
											pass





				@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('TimmBackbone initialization is managed on the timm side')
				def 						__UpperCamelCase    (				self)  -> Any:
											pass





				@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> Any:
											pass





				@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





				@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											pass





				@unittest.skip('model weights aren\'t tied in TimmBackbone.')
				def 						__UpperCamelCase    (				self)  -> Dict:
											pass





				@unittest.skip('model weights aren\'t tied in TimmBackbone.')
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											pass





				@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											pass





				@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
				def 						__UpperCamelCase    (				self)  -> int:
											pass





				@unittest.skip('TimmBackbone doesn\'t support output_attentions.')
				def 						__UpperCamelCase    (				self)  -> str:
											pass





				@unittest.skip('Safetensors is not supported by timm.')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Any:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		a__      =inspect.signature(model.forward)
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		a__      =[*signature.parameters.keys()]

																		a__      =['pixel_values']
																		self.assertListEqual(arg_names[:1]       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Any:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()
											a__      =True
											a__      =self.has_attentions

											# no need to test all models as different heads yield the same functionality
											a__      =self.all_model_classes[0]
											a__      =model_class(lowercase_)
											model.to(lowercase_)

											a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
											a__      =model(**lowercase_)
											a__      =outputs[0][-1]

											# Encoder-/Decoder-only models
											a__      =outputs.hidden_states[0]
											hidden_states.retain_grad()

											if self.has_attentions:
																		a__      =outputs.attentions[0]
																		attentions.retain_grad()

											output.flatten()[0].backward(retain_graph=lowercase_)

											self.assertIsNotNone(hidden_states.grad)

											if self.has_attentions:
																		self.assertIsNotNone(attentions.grad)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

																		self.assertEqual(len(result.feature_maps)       ,  len(config.out_indices))
																		self.assertEqual(len(model.channels)       ,  len(config.out_indices))

																		# Check output of last stage is taken if out_features=None, out_indices=None
																		a__      =copy.deepcopy(lowercase_)
																		a__      =None
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

																		self.assertEqual(len(result.feature_maps)       ,  1)
																		self.assertEqual(len(model.channels)       ,  1)

																		# Check backbone can be initialized with fresh weights
																		a__      =copy.deepcopy(lowercase_)
																		a__      =False
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

