

"""simple docstring"""




import copy
import inspect

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





@require_torch
class 			__UpperCamelCase :
		SCREAMING_SNAKE_CASE								=    ()
		SCREAMING_SNAKE_CASE								=    True


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			=					self.config_class

							# test default config
							A			=					config_class()
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
							A			=					["stem"] + [F"""stage{idx}""" for idx in range(1     ,     len(config.depths) + 1)]
							self.assertEqual(config.stage_names     ,     _SCREAMING_SNAKE_CASE)
							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
							A			=					config_class(out_features=_SCREAMING_SNAKE_CASE     ,     out_indices=_SCREAMING_SNAKE_CASE)
							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
							A			=					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
							A			=					config_class(out_features=["stage1", "stage3"])
							self.assertEqual(config.out_features     ,     ["stage1", "stage3"])
							self.assertEqual(config.out_indices     ,     [1, 3])

							# Only out_indices set
							A			=					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(_SCREAMING_SNAKE_CASE):
												A			=					config_class(out_features=["stage1", "stage2"]     ,     out_indices=[0, 2])


		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												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]     ,     _SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		List[Any]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												self.assertEqual(len(model.channels)     ,     len(config.out_features))
												A			=					model.num_features
												A			=					[config.stage_names.index(_SCREAMING_SNAKE_CASE) for feat in config.out_features]
												A			=					[num_features[idx] for idx in out_indices]
												self.assertListEqual(model.channels     ,     _SCREAMING_SNAKE_CASE)

												A			=					copy.deepcopy(_SCREAMING_SNAKE_CASE)
												A			=					None
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												self.assertEqual(len(model.channels)     ,     1)
												self.assertListEqual(model.channels     ,     [num_features[-1]])

												A			=					copy.deepcopy(_SCREAMING_SNAKE_CASE)
												A			=					None
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												self.assertEqual(len(model.channels)     ,     1)
												self.assertListEqual(model.channels     ,     [num_features[-1]])


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												model.to(_SCREAMING_SNAKE_CASE)
												model.eval()
												A			=					model(**_SCREAMING_SNAKE_CASE)

												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
												A			=					copy.deepcopy(_SCREAMING_SNAKE_CASE)
												A			=					None
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												model.to(_SCREAMING_SNAKE_CASE)
												model.eval()
												A			=					model(**_SCREAMING_SNAKE_CASE)

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

												A			=					copy.deepcopy(_SCREAMING_SNAKE_CASE)
												A			=					None
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												model.to(_SCREAMING_SNAKE_CASE)
												model.eval()
												A			=					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
												A			=					copy.deepcopy(_SCREAMING_SNAKE_CASE)
												A			=					False
												A			=					model_class(_SCREAMING_SNAKE_CASE)
												model.to(_SCREAMING_SNAKE_CASE)
												model.eval()
												A			=					model(**_SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()

							for backbone_class in self.all_model_classes:
												A			=					backbone_class(_SCREAMING_SNAKE_CASE)

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

												self.assertIsInstance(backbone.backbone_type     ,     _SCREAMING_SNAKE_CASE)
												# 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 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			,    A			=					self.model_tester.prepare_config_and_inputs_for_common()
							A			=					inputs_dict["pixel_values"].shape[0]

							for backbone_class in self.all_model_classes:
												A			=					backbone_class(_SCREAMING_SNAKE_CASE)
												backbone.to(_SCREAMING_SNAKE_CASE)
												backbone.eval()

												A			=					backbone(**_SCREAMING_SNAKE_CASE)

												# Test default outputs and verify feature maps
												self.assertIsInstance(outputs.feature_maps     ,     _SCREAMING_SNAKE_CASE)
												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
												A			=					backbone(**_SCREAMING_SNAKE_CASE     ,     output_hidden_states=_SCREAMING_SNAKE_CASE)
												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:
																	A			=					backbone(**_SCREAMING_SNAKE_CASE     ,     output_attentions=_SCREAMING_SNAKE_CASE)
																	self.assertIsNotNone(outputs.attentions)






'''simple docstring'''




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Dict:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 5]    ,     _SCREAMING_SNAKE_CASE=[5, 4]    ,     )		->     List[str]:
           pass




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               ConfigMixin.load_config('''dummy_path''' )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   SampleObject()
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           A_     =   SampleObject(_name_or_path='''lalala''' )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           A_     =   SampleObject(c=6 )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           A_     =   SampleObject(1    ,     c=6 )
           A_     =   obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 					__A       (      self )		->     int:
           A_     =   SampleObject()
           A_     =   obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_SCREAMING_SNAKE_CASE )
               A_     =   SampleObject.from_config(SampleObject.load_config(_SCREAMING_SNAKE_CASE ) )
               A_     =   new_obj.config

           # unfreeze configs
           A_     =   dict(_SCREAMING_SNAKE_CASE )
           A_     =   dict(_SCREAMING_SNAKE_CASE )

           assert config.pop('''c''' ) == (2, 5)  # instantiated as tuple
           assert new_config.pop('''c''' ) == [2, 5]  # saved & loaded as list because of json
           config.pop('''_use_default_values''' )
           assert config == new_config

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDIMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     List[str]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerAncestralDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   PNDMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Tuple:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDPMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler'''    ,     prediction_type='''sample'''    ,     beta_end=8    ,     )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger_a:
               A_     =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''    ,     beta_start=88 )

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DPMSolverMultistepScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           A_     =   SampleObject()

           A_     =   {k: v for k, v in config.config.items() if not k.startswith('''_''' )}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys() ) == set(config.config._use_default_values )

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_SCREAMING_SNAKE_CASE )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               A_     =   SampleObjecta.from_config(_SCREAMING_SNAKE_CASE )

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]






