
"""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  A__       :

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						Optional[int]									=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
			__lowerCAmelCase			:						str									=      self.get_config()

			return config, pixel_values

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







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      self.prepare_config_and_inputs()
			__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[Any]									=      config_and_inputs
			__lowerCAmelCase			:						Union[str, Any]									=      {'pixel_values': pixel_values}
			return config, inputs_dict




@require_torch
@require_timm
class  A__       (	_lowerCamelCase       ,     _lowerCamelCase       ,     _lowerCamelCase       ,     unittest.TestCase):
		A_						:       Tuple				   =	(TimmBackbone,) if is_torch_available() else ()
		A_						:       Dict				   =	{'feature-extraction': TimmBackbone} if is_torch_available() else {}
		A_						:       Optional[int]				   =	False
		A_						:       List[str]				   =	False
		A_						:       str				   =	False
		A_						:       Dict				   =	False

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						int									=      TimmBackboneModelTester(self )
			__lowerCAmelCase			:						List[str]									=      ConfigTester(self       ,		config_class=_SCREAMING_SNAKE_CASE       ,		has_text_modality=_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      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 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			:						List[Any]									=      'resnet18'
			__lowerCAmelCase			:						Optional[int]									=      'microsoft/resnet-18'

			__lowerCAmelCase			:						Optional[Any]									=      AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE       ,		use_timm_backbone=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Tuple									=      AutoBackbone.from_pretrained(_SCREAMING_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] )

			__lowerCAmelCase			:						int									=      AutoBackbone.from_pretrained(_SCREAMING_SNAKE_CASE       ,		use_timm_backbone=_SCREAMING_SNAKE_CASE       ,		out_indices=[1, 2, 3] )
			__lowerCAmelCase			:						Optional[int]									=      AutoBackbone.from_pretrained(_SCREAMING_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 							__lowerCamelCase							(      self ):
			pass

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

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

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

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

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

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

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

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

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

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

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

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

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

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

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase			:						Optional[int]									=      ['pixel_values']
				self.assertListEqual(arg_names[:1]       ,		_SCREAMING_SNAKE_CASE )

		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase			:						Any									=      True
			__lowerCAmelCase			:						Optional[Any]									=      self.has_attentions

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

			__lowerCAmelCase			:						Tuple									=      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						List[str]									=      model(**_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      outputs[0][-1]

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

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

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

			self.assertIsNotNone(hidden_states.grad )

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







		def 							__lowerCamelCase							(      self ):
			__lowerCAmelCase			,   __lowerCAmelCase			:						Tuple									=      self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__lowerCAmelCase			:						Optional[Any]									=      model_class(_SCREAMING_SNAKE_CASE )
				model.to(_SCREAMING_SNAKE_CASE )
				model.eval()
				__lowerCAmelCase			:						Optional[int]									=      model(**_SCREAMING_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
				__lowerCAmelCase			:						Optional[int]									=      copy.deepcopy(_SCREAMING_SNAKE_CASE )
				__lowerCAmelCase			:						int									=      None
				__lowerCAmelCase			:						int									=      model_class(_SCREAMING_SNAKE_CASE )
				model.to(_SCREAMING_SNAKE_CASE )
				model.eval()
				__lowerCAmelCase			:						int									=      model(**_SCREAMING_SNAKE_CASE )

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

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





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   (UnCLIPScheduler,)



		def        SCREAMING_SNAKE_CASE__			(     self,	**UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {
								    '''num_train_timesteps''': 1000,
								    '''variance_type''': '''fixed_small_log''',
								    '''clip_sample''': True,
								    '''clip_sample_range''': 1.0,
								    '''prediction_type''': '''epsilon''',
								}

								config.update(**UpperCamelCase__   )
								return config



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for timesteps in [1, 5, 100, 1000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for variance in ["fixed_small_log", "learned_range"]:
														self.check_over_configs(variance_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample in [True, False]:
														self.check_over_configs(clip_sample=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for clip_sample_range in [1, 5, 10, 20]:
														self.check_over_configs(clip_sample_range=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								for prediction_type in ["epsilon", "sample"]:
														self.check_over_configs(prediction_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								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=UpperCamelCase__,	prev_timestep=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''fixed_small_log'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								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_549_625   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_994_987   )   ) < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config(variance_type='''learned_range'''   )
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  0.5

								assert scheduler._get_variance(1,	predicted_variance=UpperCamelCase__   ) - -10.1_712_790 < 1E-5
								assert scheduler._get_variance(487,	predicted_variance=UpperCamelCase__   ) - -5.7_998_052 < 1E-5
								assert scheduler._get_variance(999,	predicted_variance=UpperCamelCase__   ) - -0.0_010_011 < 1E-5



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								lowerCAmelCase_      			=  scheduler.timesteps

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

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

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

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 252.2_682_495   ) < 1E-2
								assert abs(result_mean.item() - 0.3_284_743   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.scheduler_classes[0]
								lowerCAmelCase_      			=  self.get_scheduler_config()
								lowerCAmelCase_      			=  scheduler_class(**UpperCamelCase__   )

								scheduler.set_timesteps(25   )

								lowerCAmelCase_      			=  scheduler.timesteps

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

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

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

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

														lowerCAmelCase_      			=  pred_prev_sample

								lowerCAmelCase_      			=  torch.sum(torch.abs(UpperCamelCase__   )   )
								lowerCAmelCase_      			=  torch.mean(torch.abs(UpperCamelCase__   )   )

								assert abs(result_sum.item() - 258.2_044_983   ) < 1E-2
								assert abs(result_mean.item() - 0.3_362_038   ) < 1E-3



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass




