


'''simple docstring'''


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__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case=None    ,					__snake_case="resnet50"    ,					__snake_case=3    ,					__snake_case=32    ,					__snake_case=3    ,					__snake_case=True    ,					__snake_case=True    ,					):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      parent
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      out_indices if out_indices is not None else [4]
											_SCREAMING_SNAKE_CASE :       str     		=      stage_names
											_SCREAMING_SNAKE_CASE :       List[str]     		=      out_features
											_SCREAMING_SNAKE_CASE :       int     		=      backbone
											_SCREAMING_SNAKE_CASE :       Any     		=      batch_size
											_SCREAMING_SNAKE_CASE :       List[str]     		=      image_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_channels
											_SCREAMING_SNAKE_CASE :       int     		=      use_pretrained_backbone
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      is_training






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.get_config()

											return config, pixel_values






							def UpperCAmelCase_	(    self  ):
											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    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      TimmBackbone(config=__snake_case  )
											model.to(__snake_case  )
											model.eval()
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(__snake_case  )
											self.parent.assertEqual(
											    result.feature_map[-1].shape    ,					(self.batch_size, model.channels[-1], 14, 14)    ,					)






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Any     		=      config_and_inputs
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      {"""pixel_values""": pixel_values}
											return config, inputs_dict





@require_torch
@require_timm
class     lowercase__  (						_snake_case     ,     _snake_case     ,     _snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Optional[Any]      	=  (TimmBackbone,) if is_torch_available() else ()
							A_							:      Tuple      	=  {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
							A_							:      Optional[Any]      	=  False
							A_							:      List[Any]      	=  False
							A_							:      Dict      	=  False
							A_							:      int      	=  False






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      TimmBackboneModelTester(self  )
											_SCREAMING_SNAKE_CASE :       int     		=      ConfigTester(self    ,					config_class=__snake_case    ,					has_text_modality=__snake_case  )






							def UpperCAmelCase_	(    self  ):
											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  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """resnet18"""
											_SCREAMING_SNAKE_CASE :       Tuple     		=      """microsoft/resnet-18"""

											_SCREAMING_SNAKE_CASE :       List[str]     		=      AutoBackbone.from_pretrained(__snake_case    ,					use_timm_backbone=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      AutoBackbone.from_pretrained(__snake_case  )

											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]  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      AutoBackbone.from_pretrained(__snake_case    ,					use_timm_backbone=__snake_case    ,					out_indices=[1, 2, 3]  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      AutoBackbone.from_pretrained(__snake_case    ,					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  ):
											pass






							@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""  )
							def UpperCAmelCase_	(    self  ):
											pass






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






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






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






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






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






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






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






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






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






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






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






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






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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      model_class(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      inspect.signature(model.forward  )
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE :       int     		=      [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE :       List[Any]     		=      ["""pixel_values"""]
															self.assertListEqual(arg_names[:1]    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()
											_SCREAMING_SNAKE_CASE :       Tuple     		=      True
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.has_attentions

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

											_SCREAMING_SNAKE_CASE :       Tuple     		=      self._prepare_for_class(__snake_case    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(**__snake_case  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      outputs[0][-1]

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

											if self.has_attentions:
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      outputs.attentions[0]
															attentions.retain_grad()

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

											self.assertIsNotNone(hidden_states.grad  )

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






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       str     		=      model_class(__snake_case  )
															model.to(__snake_case  )
															model.eval()
															_SCREAMING_SNAKE_CASE :       List[str]     		=      model(**__snake_case  )

															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
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      copy.deepcopy(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      None
															_SCREAMING_SNAKE_CASE :       Tuple     		=      model_class(__snake_case  )
															model.to(__snake_case  )
															model.eval()
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model(**__snake_case  )

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

															# Check backbone can be initialized with fresh weights
															_SCREAMING_SNAKE_CASE :       str     		=      copy.deepcopy(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      False
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model_class(__snake_case  )
															model.to(__snake_case  )
															model.eval()
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      model(**__snake_case  )








'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




