




import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    """The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
    """ StableDiffusionInpaintPipeline` instead."""
)








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  __magic_name__						:
				'''simple docstring'''







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


				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
											SCREAMING_SNAKE_CASE_   	=    self.get_config()

											return config, pixel_values


				def     _A							(							self:   List[Any]		):
											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     _A							(							self:   str		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    TimmBackbone(config=_lowerCamelCase		)
											model.to(_lowerCamelCase		)
											model.eval()
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_   	=    model(_lowerCamelCase		)
											self.parent.assertEqual(
											    result.feature_map[-1].shape		,					(self.batch_size, model.channels[-1], 14, 14)		,					)





				def     _A							(							self:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    config_and_inputs
											SCREAMING_SNAKE_CASE_   	=    {'''pixel_values''': pixel_values}
											return config, inputs_dict






@require_torch
@require_timm
class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase    ,  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







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


				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    TimmBackboneModelTester(self		)
											SCREAMING_SNAKE_CASE_   	=    ConfigTester(self		,					config_class=_lowerCamelCase		,					has_text_modality=_lowerCamelCase		)


				def     _A							(							self:   str		):
											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     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    '''resnet18'''
											SCREAMING_SNAKE_CASE_   	=    '''microsoft/resnet-18'''

											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		,					use_timm_backbone=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		)

											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_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		,					use_timm_backbone=_lowerCamelCase		,					out_indices=[1, 2, 3]		)
											SCREAMING_SNAKE_CASE_   	=    AutoBackbone.from_pretrained(_lowerCamelCase		,					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     _A							(							self:   Optional[Any]		):
											pass


				@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''		)
				def     _A							(							self:   Any		):
											pass


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


				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''		)
				def     _A							(							self:   Optional[Any]		):
											pass


				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''		)
				def     _A							(							self:   List[Any]		):
											pass


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


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


				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''		)
				def     _A							(							self:   Any		):
											pass


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


				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''		)
				def     _A							(							self:   Optional[int]		):
											pass


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


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


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


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


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


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE_   	=    ['''pixel_values''']
																		self.assertListEqual(arg_names[:1]		,					_lowerCamelCase		)


				def     _A							(							self:   Dict		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE_   	=    True
											SCREAMING_SNAKE_CASE_   	=    self.has_attentions

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

											SCREAMING_SNAKE_CASE_   	=    self._prepare_for_class(_lowerCamelCase		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    outputs[0][-1]

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

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

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

											self.assertIsNotNone(hidden_states.grad		)

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





				def     _A							(							self:   List[str]		):
											SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
																		model.to(_lowerCamelCase		)
																		model.eval()
																		SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)

																		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_   	=    copy.deepcopy(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    None
																		SCREAMING_SNAKE_CASE_   	=    model_class(_lowerCamelCase		)
																		model.to(_lowerCamelCase		)
																		model.eval()
																		SCREAMING_SNAKE_CASE_   	=    model(**_lowerCamelCase		)

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

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


