
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				(UniPCMultistepScheduler,)
			snake_case	      =				(("num_inference_steps", 25),)




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   **_snake_case						:					Optional[int]	)      -> str:







								"""simple docstring"""
								A_				 =						{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**_snake_case	)
								return config




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					Dict=0       ,   **_snake_case						:					int	)      -> Any:







								"""simple docstring"""
								A_				 =						dict(self.forward_default_kwargs	)
								A_				 =						kwargs.pop("num_inference_steps"       ,   _snake_case	)
								A_				 =						self.dummy_sample
								A_				 =						0.1 * sample
								A_				 =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config(**_snake_case	)
													A_				 =						scheduler_class(**_snake_case	)
													scheduler.set_timesteps(_snake_case	)
													# copy over dummy past residuals
													A_				 =						dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(_snake_case	)
																		A_				 =						scheduler_class.from_pretrained(_snake_case	)
																		new_scheduler.set_timesteps(_snake_case	)
																		# copy over dummy past residuals
																		A_				 =						dummy_past_residuals[: new_scheduler.config.solver_order]

													A_ ,    A_				 =						sample, sample
													for t in range(_snake_case       ,   time_step + scheduler.config.solver_order + 1	):
																		A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample
																		A_				 =						new_scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

																		assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Dict=0       ,   **_snake_case						:					int	)      -> int:







								"""simple docstring"""
								A_				 =						dict(self.forward_default_kwargs	)
								A_				 =						kwargs.pop("num_inference_steps"       ,   _snake_case	)
								A_				 =						self.dummy_sample
								A_				 =						0.1 * sample
								A_				 =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config()
													A_				 =						scheduler_class(**_snake_case	)
													scheduler.set_timesteps(_snake_case	)

													# copy over dummy past residuals (must be after setting timesteps)
													A_				 =						dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(_snake_case	)
																		A_				 =						scheduler_class.from_pretrained(_snake_case	)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(_snake_case	)

																		# copy over dummy past residual (must be after setting timesteps)
																		A_				 =						dummy_past_residuals[: new_scheduler.config.solver_order]

													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample
													A_				 =						new_scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

													assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"




			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Tuple=None       ,   **_snake_case						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								if scheduler is None:
													A_				 =						self.scheduler_classes[0]
													A_				 =						self.get_scheduler_config(**_snake_case	)
													A_				 =						scheduler_class(**_snake_case	)

								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config(**_snake_case	)
								A_				 =						scheduler_class(**_snake_case	)

								A_				 =						10
								A_				 =						self.dummy_model()
								A_				 =						self.dummy_sample_deter
								scheduler.set_timesteps(_snake_case	)

								for i, t in enumerate(scheduler.timesteps	):
													A_				 =						model(_snake_case       ,   _snake_case	)
													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case	).prev_sample

								return sample




			def      lowerCamelCase__     (				self						:					Any	)      -> List[str]:







								"""simple docstring"""
								A_				 =						dict(self.forward_default_kwargs	)

								A_				 =						kwargs.pop("num_inference_steps"       ,   _snake_case	)

								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config()
													A_				 =						scheduler_class(**_snake_case	)

													A_				 =						self.dummy_sample
													A_				 =						0.1 * sample

													if num_inference_steps is not None and hasattr(_snake_case       ,   "set_timesteps"	):
																		scheduler.set_timesteps(_snake_case	)
													elif num_inference_steps is not None and not hasattr(_snake_case       ,   "set_timesteps"	):
																		A_				 =						num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													A_				 =						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													A_				 =						dummy_past_residuals[: scheduler.config.solver_order]

													A_				 =						scheduler.timesteps[5]
													A_				 =						scheduler.timesteps[6]

													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample
													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case       ,   **_snake_case	).prev_sample

													self.assertEqual(output_a.shape       ,   sample.shape	)
													self.assertEqual(output_a.shape       ,   output_a.shape	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> str:







								"""simple docstring"""
								A_				 =						UniPCMultistepScheduler(**self.get_scheduler_config()	)
								A_				 =						self.full_loop(scheduler=_snake_case	)
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3

								A_				 =						DPMSolverSinglestepScheduler.from_config(scheduler.config	)
								A_				 =						DEISMultistepScheduler.from_config(scheduler.config	)
								A_				 =						DPMSolverMultistepScheduler.from_config(scheduler.config	)
								A_				 =						UniPCMultistepScheduler.from_config(scheduler.config	)

								A_				 =						self.full_loop(scheduler=_snake_case	)
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3




			def      lowerCamelCase__     (				self						:					int	)      -> Any:







								"""simple docstring"""
								for timesteps in [25, 50, 100, 999, 1_000]:
													self.check_over_configs(num_train_timesteps=_snake_case	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								self.check_over_configs(thresholding=_snake_case	)
								for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																							for prediction_type in ["epsilon", "sample"]:
																												self.check_over_configs(
																												    thresholding=_snake_case       ,   prediction_type=_snake_case       ,   sample_max_value=_snake_case       ,   solver_order=_snake_case       ,   solver_type=_snake_case       ,   )




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=_snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Tuple:







								"""simple docstring"""
								for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    solver_order=_snake_case       ,   solver_type=_snake_case       ,   prediction_type=_snake_case       ,   )
																							A_				 =						self.full_loop(
																							    solver_order=_snake_case       ,   solver_type=_snake_case       ,   prediction_type=_snake_case       ,   )
																							assert not torch.isnan(_snake_case	).any(), "Samples have nan numbers"




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								self.check_over_configs(lower_order_final=_snake_case	)
								self.check_over_configs(lower_order_final=_snake_case	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
													self.check_over_forward(num_inference_steps=_snake_case       ,   time_step=0	)




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						self.full_loop()
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.2_4_6_4	) < 1e-3




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								A_				 =						self.full_loop(prediction_type="v_prediction"	)
								A_				 =						torch.mean(torch.abs(_snake_case	)	)

								assert abs(result_mean.item() - 0.1_0_1_4	) < 1e-3




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.scheduler_classes[0]
								A_				 =						self.get_scheduler_config(thresholding=_snake_case       ,   dynamic_thresholding_ratio=0	)
								A_				 =						scheduler_class(**_snake_case	)

								A_				 =						10
								A_				 =						self.dummy_model()
								A_				 =						self.dummy_sample_deter.half()
								scheduler.set_timesteps(_snake_case	)

								for i, t in enumerate(scheduler.timesteps	):
													A_				 =						model(_snake_case       ,   _snake_case	)
													A_				 =						scheduler.step(_snake_case       ,   _snake_case       ,   _snake_case	).prev_sample

								assert sample.dtype == torch.floataa






			def      lowerCamelCase__     (				self						:					List[Any]       ,   **_snake_case						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								for scheduler_class in self.scheduler_classes:
													A_				 =						self.get_scheduler_config(**_snake_case	)
													A_				 =						scheduler_class(**_snake_case	)

													scheduler.set_timesteps(scheduler.config.num_train_timesteps	)
													assert len(scheduler.timesteps.unique()	) == scheduler.num_inference_steps






"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import RegNetForImageClassification, RegNetModel
		from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Any       ,   _snake_case						:					int=3       ,   _snake_case						:					List[Any]=32       ,   _snake_case						:					str=3       ,   _snake_case						:					Any=10       ,   _snake_case						:					List[Any]=[10, 20, 30, 40]       ,   _snake_case						:					Dict=[1, 1, 2, 1]       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Any=True       ,   _snake_case						:					List[str]="relu"       ,   _snake_case						:					List[Any]=3       ,   _snake_case						:					List[str]=None       ,   )      -> int:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						image_size
								A_				 =						num_channels
								A_				 =						embeddings_size
								A_				 =						hidden_sizes
								A_				 =						depths
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						hidden_act
								A_				 =						num_labels
								A_				 =						scope
								A_				 =						len(_snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

								A_				 =						None
								if self.use_labels:
													A_				 =						ids_tensor([self.batch_size]       ,   self.num_labels	)

								A_				 =						self.get_config()

								return config, pixel_values, labels




			def      lowerCamelCase__     (				self						:					List[str]	)      -> Union[str, Any]:







								"""simple docstring"""
								return RegNetConfig(
								    num_channels=self.num_channels       ,   embeddings_size=self.embeddings_size       ,   hidden_sizes=self.hidden_sizes       ,   depths=self.depths       ,   hidden_act=self.hidden_act       ,   num_labels=self.num_labels       ,   )




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Tuple       ,   _snake_case						:					Tuple	)      -> List[str]:







								"""simple docstring"""
								A_				 =						RegNetModel(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,   )




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Tuple       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						RegNetForImageClassification(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case       ,   labels=_snake_case	)
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)






			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.prepare_config_and_inputs()
								A_ ,    A_ ,    A_				 =						config_and_inputs
								A_				 =						{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			snake_case	      =				(
			    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						RegNetModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								self.create_and_test_config_common_properties()
								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						:					int	)      -> List[Any]:







								"""simple docstring"""
								return




			@unittest.skip(reason="RegNet does not use inputs_embeds"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""simple docstring"""
								pass




			@unittest.skip(reason="RegNet does not support input and output embeddings"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> List[Any]:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(_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]       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> int:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(config=_snake_case	)
													for name, module in model.named_modules():
																		if isinstance(_snake_case       ,   (nn.BatchNormad, nn.GroupNorm)	):
																							self.assertTrue(
																							    torch.all(module.weight == 1	)       ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,   )
																							self.assertTrue(
																							    torch.all(module.bias == 0	)       ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,   )




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> List[str]:







								"""simple docstring"""
								def check_hidden_states_output(_snake_case						:					Tuple       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Optional[int]	):
													A_				 =						model_class(_snake_case	)
													model.to(_snake_case	)
													model.eval()

													with torch.no_grad():
																		A_				 =						model(**self._prepare_for_class(_snake_case       ,   _snake_case	)	)

													A_				 =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A_				 =						self.model_tester.num_stages
													self.assertEqual(len(_snake_case	)       ,   expected_num_stages + 1	)

													# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]	)       ,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,   )

								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								A_				 =						["basic", "bottleneck"]
								for model_class in self.all_model_classes:
													for layer_type in layers_type:
																		A_				 =						layer_type
																		A_				 =						True
																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		A_				 =						True

																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					str	)      -> List[str]:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_				 =						RegNetModel.from_pretrained(_snake_case	)
													self.assertIsNotNone(_snake_case	)





def 							A_     ():

					'''simple docstring'''





					A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					return image




@require_torch
@require_vision
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			@cached_property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
								    if is_vision_available()
								    else None
								)






			@slow
			def      lowerCamelCase__     (				self						:					str	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_snake_case	)

								A_				 =						self.default_image_processor
								A_				 =						prepare_img()
								A_				 =						image_processor(images=_snake_case       ,   return_tensors="pt"	).to(_snake_case	)

								# forward pass
								with torch.no_grad():
													A_				 =						model(**_snake_case	)

								# verify the logits
								A_				 =						torch.Size((1, 1_000)	)
								self.assertEqual(outputs.logits.shape       ,   _snake_case	)

								A_				 =						torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]	).to(_snake_case	)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,   _snake_case       ,   atol=1e-4	)	)




