
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    __A:

					def __init__(						self       :  Dict ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Optional[int]=None ,       __UpperCamelCase       :  Any=None ,       __UpperCamelCase       :  Dict=None ,       __UpperCamelCase       :  List[Any]="resnet50" ,       __UpperCamelCase       :  List[str]=3 ,       __UpperCamelCase       :  Dict=3_2 ,       __UpperCamelCase       :  int=3 ,       __UpperCamelCase       :  List[Any]=True ,       __UpperCamelCase       :  Optional[int]=True ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       out_indices if out_indices is not None else [4]
												lowerCamelCase_					=       stage_names
												lowerCamelCase_					=       out_features
												lowerCamelCase_					=       backbone
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       image_size
												lowerCamelCase_					=       num_channels
												lowerCamelCase_					=       use_pretrained_backbone
												lowerCamelCase_					=       is_training

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
												lowerCamelCase_					=       self.get_config()

												return config, pixel_values

					def  lowercase__   (						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  lowercase__   (						self       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       TimmBackbone(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												with torch.no_grad():
																			lowerCamelCase_					=       model(__UpperCamelCase )
												self.parent.assertEqual(
												    result.feature_map[-1].shape ,       (self.batch_size, model.channels[-1], 1_4, 1_4) ,       )



					def  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       self.prepare_config_and_inputs()
												lowerCamelCase_	, lowerCamelCase_					=       config_and_inputs
												lowerCamelCase_					=       {"""pixel_values""": pixel_values}
												return config, inputs_dict





@require_torch
@require_timm
class    __A(					UpperCAmelCase  ,		UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (TimmBackbone,) if is_torch_available() else ()
					SCREAMING_SNAKE_CASE           =     {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       TimmBackboneModelTester(self )
												lowerCamelCase_					=       ConfigTester(self ,       config_class=__UpperCamelCase ,       has_text_modality=__UpperCamelCase )

					def  lowercase__   (						self       :  List[Any] ):
												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  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       """resnet18"""
												lowerCamelCase_					=       """microsoft/resnet-18"""

												lowerCamelCase_					=       AutoBackbone.from_pretrained(__UpperCamelCase ,       use_timm_backbone=__UpperCamelCase )
												lowerCamelCase_					=       AutoBackbone.from_pretrained(__UpperCamelCase )

												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] )

												lowerCamelCase_					=       AutoBackbone.from_pretrained(__UpperCamelCase ,       use_timm_backbone=__UpperCamelCase ,       out_indices=[1, 2, 3] )
												lowerCamelCase_					=       AutoBackbone.from_pretrained(__UpperCamelCase ,       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  lowercase__   (						self       :  Optional[int] ):
												pass

					@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""" )
					def  lowercase__   (						self       :  List[Any] ):
												pass

					@unittest.skip("""TimmBackbone initialization is managed on the timm side""" )
					def  lowercase__   (						self       :  Dict ):
												pass

					@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""" )
					def  lowercase__   (						self       :  Dict ):
												pass

					@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""" )
					def  lowercase__   (						self       :  Dict ):
												pass

					@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""" )
					def  lowercase__   (						self       :  str ):
												pass

					@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
					def  lowercase__   (						self       :  int ):
												pass

					@unittest.skip("""model weights aren't tied in TimmBackbone.""" )
					def  lowercase__   (						self       :  Tuple ):
												pass

					@unittest.skip("""model weights aren't tied in TimmBackbone.""" )
					def  lowercase__   (						self       :  Optional[Any] ):
												pass

					@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
					def  lowercase__   (						self       :  Optional[Any] ):
												pass

					@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
					def  lowercase__   (						self       :  Optional[Any] ):
												pass

					@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""" )
					def  lowercase__   (						self       :  Any ):
												pass

					@unittest.skip("""TimmBackbone doesn't support output_attentions.""" )
					def  lowercase__   (						self       :  Tuple ):
												pass

					@unittest.skip("""Safetensors is not supported by timm.""" )
					def  lowercase__   (						self       :  str ):
												pass

					@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
					def  lowercase__   (						self       :  Tuple ):
												pass

					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

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

																			lowerCamelCase_					=       ["""pixel_values"""]
																			self.assertListEqual(arg_names[:1] ,       __UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()
												lowerCamelCase_					=       True
												lowerCamelCase_					=       self.has_attentions

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

												lowerCamelCase_					=       self._prepare_for_class(__UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       model(**__UpperCamelCase )
												lowerCamelCase_					=       outputs[0][-1]

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

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

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

												self.assertIsNotNone(hidden_states.grad )

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



					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_	, lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			lowerCamelCase_					=       model_class(__UpperCamelCase )
																			model.to(__UpperCamelCase )
																			model.eval()
																			lowerCamelCase_					=       model(**__UpperCamelCase )

																			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
																			lowerCamelCase_					=       copy.deepcopy(__UpperCamelCase )
																			lowerCamelCase_					=       None
																			lowerCamelCase_					=       model_class(__UpperCamelCase )
																			model.to(__UpperCamelCase )
																			model.eval()
																			lowerCamelCase_					=       model(**__UpperCamelCase )

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

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







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


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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     int:
							warnings.warn(
							    """The preprocess method is deprecated and will be removed in a future version. Please"""
							    """ use VaeImageProcessor.preprocess instead"""      ,					UpperCAmelCase__      ,					)
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return image
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [image]

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

														lowerCamelCase_					=       [np.array(i.resize((w, h)      ,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       np.array(UpperCAmelCase__							).astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       image.transpose(0      ,					3      ,					1      ,					2							)
														lowerCamelCase_					=       2.0 * image - 1.0
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(image[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return image



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     Any:
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return mask
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       mask[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														lowerCamelCase_					=       [np.array(m.convert("""L"""							).resize((w, h)      ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       mask.astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       0
														lowerCamelCase_					=       1
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(mask[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return mask





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase ,       scheduler=__UpperCamelCase )



					@torch.no_grad()
					def __call__(						self       :  List[str] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  int = 2_5_0 ,       __UpperCamelCase       :  float = 0.0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       __UpperCamelCase       :  Optional[str] = "pil" ,       __UpperCamelCase       :  bool = True ,       ):
												lowerCamelCase_					=       image

												lowerCamelCase_					=       _preprocess_image(__UpperCamelCase )
												lowerCamelCase_					=       original_image.to(device=self.device ,       dtype=self.unet.dtype )
												lowerCamelCase_					=       _preprocess_mask(__UpperCamelCase )
												lowerCamelCase_					=       mask_image.to(device=self.device ,       dtype=self.unet.dtype )

												lowerCamelCase_					=       original_image.shape[0]

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

												lowerCamelCase_					=       original_image.shape
												lowerCamelCase_					=       randn_tensor(__UpperCamelCase ,       generator=__UpperCamelCase ,       device=self.device ,       dtype=self.unet.dtype )

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       self.device )
												lowerCamelCase_					=       eta

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

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

												lowerCamelCase_					=       (image / 2 + 0.5).clamp(0 ,       1 )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 ).numpy()
												if output_type == "pil":
																			lowerCamelCase_					=       self.numpy_to_pil(__UpperCamelCase )

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase )





