import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		(PNDMScheduler,)
						UpperCamelCase_     =		(("""num_inference_steps""", 50),)


						def        __A       (       self	:				List[Any]    ,		**UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							{
												    '''num_train_timesteps''': 1000,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												}

												config.update(**UpperCamelCase__						)
												return config


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				Tuple=0    ,		**UpperCamelCase__	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							dict(self.forward_default_kwargs						)
												SCREAMING_SNAKE_CASE							:    str							=							kwargs.pop('''num_inference_steps'''    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.dummy_sample
												SCREAMING_SNAKE_CASE							:    List[str]							=							0.1 * sample
												SCREAMING_SNAKE_CASE							:    List[str]							=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																		SCREAMING_SNAKE_CASE							:    Any							=							self.get_scheduler_config(**UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scheduler_class(**UpperCamelCase__						)
																		scheduler.set_timesteps(UpperCamelCase__						)
																		# copy over dummy past residuals
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							dummy_past_residuals[:]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							scheduler_class.from_pretrained(UpperCamelCase__						)
																								new_scheduler.set_timesteps(UpperCamelCase__						)
																								# copy over dummy past residuals
																								SCREAMING_SNAKE_CASE							:    Dict							=							dummy_past_residuals[:]

																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							scheduler.step_prk(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample
																		SCREAMING_SNAKE_CASE							:    Any							=							new_scheduler.step_prk(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample

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

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							scheduler.step_plms(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample
																		SCREAMING_SNAKE_CASE							:    List[str]							=							new_scheduler.step_plms(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample

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


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												pass


						def        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				Optional[int]=0    ,		**UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							dict(self.forward_default_kwargs						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							kwargs.pop('''num_inference_steps'''    ,		UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.dummy_sample
												SCREAMING_SNAKE_CASE							:    Any							=							0.1 * sample
												SCREAMING_SNAKE_CASE							:    List[Any]							=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scheduler_class(**UpperCamelCase__						)
																		scheduler.set_timesteps(UpperCamelCase__						)

																		# copy over dummy past residuals (must be after setting timesteps)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							dummy_past_residuals[:]

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							scheduler_class.from_pretrained(UpperCamelCase__						)
																								# copy over dummy past residuals
																								new_scheduler.set_timesteps(UpperCamelCase__						)

																								# copy over dummy past residual (must be after setting timesteps)
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							dummy_past_residuals[:]

																		SCREAMING_SNAKE_CASE							:    str							=							scheduler.step_prk(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample
																		SCREAMING_SNAKE_CASE							:    str							=							new_scheduler.step_prk(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample

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

																		SCREAMING_SNAKE_CASE							:    Tuple							=							scheduler.step_plms(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							new_scheduler.step_plms(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample

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


						def        __A       (       self	:				Optional[int]    ,		**UpperCamelCase__	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_scheduler_config(**UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Dict							=							scheduler_class(**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    int							=							10
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.dummy_model()
												SCREAMING_SNAKE_CASE							:    Tuple							=							self.dummy_sample_deter
												scheduler.set_timesteps(UpperCamelCase__						)

												for i, t in enumerate(scheduler.prk_timesteps						):
																		SCREAMING_SNAKE_CASE							:    str							=							model(UpperCamelCase__    ,		UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							scheduler.step_prk(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						).prev_sample

												for i, t in enumerate(scheduler.plms_timesteps						):
																		SCREAMING_SNAKE_CASE							:    int							=							model(UpperCamelCase__    ,		UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							scheduler.step_plms(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						).prev_sample

												return sample


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							dict(self.forward_default_kwargs						)

												SCREAMING_SNAKE_CASE							:    int							=							kwargs.pop('''num_inference_steps'''    ,		UpperCamelCase__						)

												for scheduler_class in self.scheduler_classes:
																		SCREAMING_SNAKE_CASE							:    Dict							=							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:    Tuple							=							scheduler_class(**UpperCamelCase__						)

																		SCREAMING_SNAKE_CASE							:    Any							=							self.dummy_sample
																		SCREAMING_SNAKE_CASE							:    Dict							=							0.1 * sample

																		if num_inference_steps is not None and hasattr(UpperCamelCase__    ,		'''set_timesteps'''						):
																								scheduler.set_timesteps(UpperCamelCase__						)
																		elif num_inference_steps is not None and not hasattr(UpperCamelCase__    ,		'''set_timesteps'''						):
																								SCREAMING_SNAKE_CASE							:    str							=							num_inference_steps

																		# copy over dummy past residuals (must be done after set_timesteps)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							dummy_past_residuals[:]

																		SCREAMING_SNAKE_CASE							:    Dict							=							scheduler.step_prk(UpperCamelCase__    ,		0    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample
																		SCREAMING_SNAKE_CASE							:    str							=							scheduler.step_prk(UpperCamelCase__    ,		1    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample

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

																		SCREAMING_SNAKE_CASE							:    List[str]							=							scheduler.step_plms(UpperCamelCase__    ,		0    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							scheduler.step_plms(UpperCamelCase__    ,		1    ,		UpperCamelCase__    ,		**UpperCamelCase__						).prev_sample

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


						def        __A       (       self	:				str						):





												'''simple docstring'''





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


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.get_scheduler_config(steps_offset=1						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							scheduler_class(**UpperCamelCase__						)
												scheduler.set_timesteps(10						)
												assert torch.equal(
												    scheduler.timesteps    ,		torch.LongTensor(
												        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]						)    ,		)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												for beta_start, beta_end in zip([0.0001, 0.001]    ,		[0.002, 0.02]						):
																		self.check_over_configs(beta_start=UpperCamelCase__    ,		beta_end=UpperCamelCase__						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=UpperCamelCase__						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





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


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												for t in [1, 5, 10]:
																		self.check_over_forward(time_step=UpperCamelCase__						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for t, num_inference_steps in zip([1, 5, 10]    ,		[10, 50, 100]						):
																		self.check_over_forward(num_inference_steps=UpperCamelCase__						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							27

												for scheduler_class in self.scheduler_classes:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.dummy_sample
																		SCREAMING_SNAKE_CASE							:    List[str]							=							0.1 * sample

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:    str							=							scheduler_class(**UpperCamelCase__						)

																		scheduler.set_timesteps(UpperCamelCase__						)

																		# before power of 3 fix, would error on first step, so we only need to do two
																		for i, t in enumerate(scheduler.prk_timesteps[:2]						):
																								SCREAMING_SNAKE_CASE							:    str							=							scheduler.step_prk(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						).prev_sample


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												with self.assertRaises(UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.scheduler_classes[0]
																		SCREAMING_SNAKE_CASE							:    str							=							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							scheduler_class(**UpperCamelCase__						)

																		scheduler.step_plms(self.dummy_sample    ,		1    ,		self.dummy_sample						).prev_sample


						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.full_loop()
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.sum(torch.abs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.mean(torch.abs(UpperCamelCase__						)						)

												assert abs(result_sum.item() - 198.1318						) < 1E-2
												assert abs(result_mean.item() - 0.2580						) < 1E-3


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.full_loop(prediction_type='''v_prediction'''						)
												SCREAMING_SNAKE_CASE							:    Dict							=							torch.sum(torch.abs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.mean(torch.abs(UpperCamelCase__						)						)

												assert abs(result_sum.item() - 67.3986						) < 1E-2
												assert abs(result_mean.item() - 0.0878						) < 1E-3


						def        __A       (       self	:				str						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.full_loop(set_alpha_to_one=UpperCamelCase__    ,		beta_start=0.01						)
												SCREAMING_SNAKE_CASE							:    Any							=							torch.sum(torch.abs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							torch.mean(torch.abs(UpperCamelCase__						)						)

												assert abs(result_sum.item() - 230.0399						) < 1E-2
												assert abs(result_mean.item() - 0.2995						) < 1E-3



						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							self.full_loop(set_alpha_to_one=UpperCamelCase__    ,		beta_start=0.01						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							torch.sum(torch.abs(UpperCamelCase__						)						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.mean(torch.abs(UpperCamelCase__						)						)

												assert abs(result_sum.item() - 186.9482						) < 1E-2
												assert abs(result_mean.item() - 0.2434						) < 1E-3






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





