





import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class  _a  :






							"""simple docstring"""

							snake_case_							=    ()
							snake_case_							=    True

							def   A_	(   self					:  Optional[int]     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.config_class

													# test default config
													SCREAMING_SNAKE_CASE__  :					int =       config_class()
													self.assertIsNotNone(a     )
													SCREAMING_SNAKE_CASE__  :					Any =       ["stem"] + [f"""stage{idx}""" for idx in range(1				,						len(config.depths     ) + 1     )]
													self.assertEqual(config.stage_names				,						a     )
													self.assertTrue(set(config.out_features     ).issubset(set(config.stage_names     )     )     )

													# Test out_features and out_indices are correctly set
													# out_features and out_indices both None
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       config_class(out_features=a				,						out_indices=a     )
													self.assertEqual(config.out_features				,						[config.stage_names[-1]]     )
													self.assertEqual(config.out_indices				,						[len(config.stage_names     ) - 1]     )

													# out_features and out_indices both set
													SCREAMING_SNAKE_CASE__  :					List[Any] =       config_class(out_features=["stem", "stage1"]				,						out_indices=[0, 1]     )
													self.assertEqual(config.out_features				,						["stem", "stage1"]     )
													self.assertEqual(config.out_indices				,						[0, 1]     )

													# Only out_features set
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       config_class(out_features=["stage1", "stage3"]     )
													self.assertEqual(config.out_features				,						["stage1", "stage3"]     )
													self.assertEqual(config.out_indices				,						[1, 3]     )

													# Only out_indices set
													SCREAMING_SNAKE_CASE__  :					Dict =       config_class(out_indices=[0, 2]     )
													self.assertEqual(config.out_features				,						[config.stage_names[0], config.stage_names[2]]     )
													self.assertEqual(config.out_indices				,						[0, 2]     )

													# Error raised when out_indices do not correspond to out_features
													with self.assertRaises(a     ):
																			SCREAMING_SNAKE_CASE__  :					Dict =       config_class(out_features=["stage1", "stage2"]				,						out_indices=[0, 2]     )

							def   A_	(   self					:  Tuple     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Tuple =       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model_class(a     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       inspect.signature(model.forward     )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			SCREAMING_SNAKE_CASE__  :					Dict =       [*signature.parameters.keys()]
																			SCREAMING_SNAKE_CASE__  :					int =       ["pixel_values"]
																			self.assertListEqual(arg_names[:1]				,						a     )

							def   A_	(   self					:  Any     )						->List[str]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       model_class(a     )
																			self.assertEqual(len(model.channels     )				,						len(config.out_features     )     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model.num_features
																			SCREAMING_SNAKE_CASE__  :					Any =       [config.stage_names.index(a     ) for feat in config.out_features]
																			SCREAMING_SNAKE_CASE__  :					int =       [num_features[idx] for idx in out_indices]
																			self.assertListEqual(model.channels				,						a     )

																			SCREAMING_SNAKE_CASE__  :					List[str] =       copy.deepcopy(a     )
																			SCREAMING_SNAKE_CASE__  :					Dict =       None
																			SCREAMING_SNAKE_CASE__  :					Tuple =       model_class(a     )
																			self.assertEqual(len(model.channels     )				,						1     )
																			self.assertListEqual(model.channels				,						[num_features[-1]]     )

																			SCREAMING_SNAKE_CASE__  :					Any =       copy.deepcopy(a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       None
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model_class(a     )
																			self.assertEqual(len(model.channels     )				,						1     )
																			self.assertListEqual(model.channels				,						[num_features[-1]]     )

							def   A_	(   self					:  Dict     )						->str:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       model_class(a     )
																			model.to(a     )
																			model.eval()
																			SCREAMING_SNAKE_CASE__  :					str =       model(**a     )

																			self.assertEqual(len(result.feature_maps     )				,						len(config.out_features     )     )
																			self.assertEqual(len(model.channels     )				,						len(config.out_features     )     )
																			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__  :					str =       copy.deepcopy(a     )
																			SCREAMING_SNAKE_CASE__  :					str =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       model_class(a     )
																			model.to(a     )
																			model.eval()
																			SCREAMING_SNAKE_CASE__  :					int =       model(**a     )

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

																			SCREAMING_SNAKE_CASE__  :					List[str] =       copy.deepcopy(a     )
																			SCREAMING_SNAKE_CASE__  :					str =       None
																			SCREAMING_SNAKE_CASE__  :					int =       model_class(a     )
																			model.to(a     )
																			model.eval()
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model(**a     )

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

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

							def   A_	(   self					:  Dict     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       self.model_tester.prepare_config_and_inputs_for_common()

													for backbone_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					List[str] =       backbone_class(a     )

																			self.assertTrue(hasattr(a				,						"backbone_type"     )     )
																			self.assertTrue(hasattr(a				,						"stage_names"     )     )
																			self.assertTrue(hasattr(a				,						"num_features"     )     )
																			self.assertTrue(hasattr(a				,						"out_indices"     )     )
																			self.assertTrue(hasattr(a				,						"out_features"     )     )
																			self.assertTrue(hasattr(a				,						"out_feature_channels"     )     )
																			self.assertTrue(hasattr(a				,						"channels"     )     )

																			self.assertIsInstance(backbone.backbone_type				,						a     )
																			# Verify num_features has been initialized in the backbone init
																			self.assertIsNotNone(backbone.num_features     )
																			self.assertTrue(len(backbone.channels     ) == len(backbone.out_indices     )     )
																			self.assertTrue(len(backbone.stage_names     ) == len(backbone.num_features     )     )
																			self.assertTrue(len(backbone.channels     ) <= len(backbone.num_features     )     )
																			self.assertTrue(len(backbone.out_feature_channels     ) == len(backbone.stage_names     )     )






							def   A_	(   self					:  Any     )						->Tuple:
													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.model_tester.prepare_config_and_inputs_for_common()
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       inputs_dict["pixel_values"].shape[0]

													for backbone_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       backbone_class(a     )
																			backbone.to(a     )
																			backbone.eval()

																			SCREAMING_SNAKE_CASE__  :					Dict =       backbone(**a     )

																			# Test default outputs and verify feature maps
																			self.assertIsInstance(outputs.feature_maps				,						a     )
																			self.assertTrue(len(outputs.feature_maps     ) == len(backbone.channels     )     )
																			for feature_map, n_channels in zip(outputs.feature_maps				,						backbone.channels     ):
																									self.assertTrue(feature_map.shape[:2]				,						(batch_size, n_channels)     )
																			self.assertIsNone(outputs.hidden_states     )
																			self.assertIsNone(outputs.attentions     )

																			# Test output_hidden_states=True
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       backbone(**a				,						output_hidden_states=a     )
																			self.assertIsNotNone(outputs.hidden_states     )
																			self.assertTrue(len(outputs.hidden_states     )				,						len(backbone.stage_names     )     )
																			for hidden_state, n_channels in zip(outputs.hidden_states				,						backbone.channels     ):
																									self.assertTrue(hidden_state.shape[:2]				,						(batch_size, n_channels)     )

																			# Test output_attentions=True
																			if self.has_attentions:
																									SCREAMING_SNAKE_CASE__  :					str =       backbone(**a				,						output_attentions=a     )
																									self.assertIsNotNone(outputs.attentions     )






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )