


import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class       lowercase_					(lowercase__   ):
				snake_case	    =(UnCLIPScheduler,)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											a__      ={
											    'num_train_timesteps': 1000,
											    'variance_type': 'fixed_small_log',
											    'clip_sample': True,
											    'clip_sample_range': 1.0,
											    'prediction_type': 'epsilon',
											}

											config.update(**lowercase_)
											return config





				def 						__UpperCamelCase    (				self)  -> Dict:
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(prediction_type=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

																									self.check_over_forward(time_step=lowercase_       ,  prev_timestep=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.scheduler_classes[0]
											a__      =self.get_scheduler_config(variance_type='fixed_small_log')
											a__      =scheduler_class(**lowercase_)

											assert torch.sum(torch.abs(scheduler._get_variance(0) - 1.0000e-10)) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_54_96_25)) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.9_99_49_87)) < 1e-5





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.scheduler_classes[0]
											a__      =self.get_scheduler_config(variance_type='learned_range')
											a__      =scheduler_class(**lowercase_)

											a__      =0.5

											assert scheduler._get_variance(1       ,  predicted_variance=lowercase_) - -10.1_71_27_90 < 1e-5
											assert scheduler._get_variance(487       ,  predicted_variance=lowercase_) - -5.7_99_80_52 < 1e-5
											assert scheduler._get_variance(999       ,  predicted_variance=lowercase_) - -0.0_01_00_11 < 1e-5





				def 						__UpperCamelCase    (				self)  -> Any:
											a__      =self.scheduler_classes[0]
											a__      =self.get_scheduler_config()
											a__      =scheduler_class(**lowercase_)

											a__      =scheduler.timesteps

											a__      =self.dummy_model()
											a__      =self.dummy_sample_deter
											a__      =torch.manual_seed(0)

											for i, t in enumerate(lowercase_):
																		# 1. predict noise residual
																		a__      =model(lowercase_       ,  lowercase_)

																		# 2. predict previous mean of sample x_t-1
																		a__      =scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  generator=lowercase_).prev_sample

																		a__      =pred_prev_sample

											a__      =torch.sum(torch.abs(lowercase_))
											a__      =torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 2_52.2_68_24_95) < 1e-2
											assert abs(result_mean.item() - 0.3_28_47_43) < 1e-3





				def 						__UpperCamelCase    (				self)  -> Dict:
											a__      =self.scheduler_classes[0]
											a__      =self.get_scheduler_config()
											a__      =scheduler_class(**lowercase_)

											scheduler.set_timesteps(25)

											a__      =scheduler.timesteps

											a__      =self.dummy_model()
											a__      =self.dummy_sample_deter
											a__      =torch.manual_seed(0)

											for i, t in enumerate(lowercase_):
																		# 1. predict noise residual
																		a__      =model(lowercase_       ,  lowercase_)

																		if i + 1 == timesteps.shape[0]:
																									a__      =None
																		else:
																									a__      =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		a__      =scheduler.step(
																		    lowercase_       ,  lowercase_       ,  lowercase_       ,  prev_timestep=lowercase_       ,  generator=lowercase_).prev_sample

																		a__      =pred_prev_sample

											a__      =torch.sum(torch.abs(lowercase_))
											a__      =torch.mean(torch.abs(lowercase_))

											assert abs(result_sum.item() - 2_58.2_04_49_83) < 1e-2
											assert abs(result_mean.item() - 0.3_36_20_38) < 1e-3





				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





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_)

