
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Optional[Any]       =(PNDMScheduler,)
							lowercase_ : List[Any]       =(('''num_inference_steps''', 50),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)
																	# copy over dummy past residuals
																	lowercase					=		dummy_past_residuals[:]

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

																	lowercase					=		scheduler.step_prk(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step_prk(A__     ,A__     ,A__     ,**A__).prev_sample

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

																	lowercase					=		scheduler.step_plms(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step_plms(A__     ,A__     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self):
												pass




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)

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

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

																						# copy over dummy past residual (must be after setting timesteps)
																						lowercase					=		dummy_past_residuals[:]

																	lowercase					=		scheduler.step_prk(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step_prk(A__     ,A__     ,A__     ,**A__).prev_sample

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

																	lowercase					=		scheduler.step_plms(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step_plms(A__     ,A__     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self     ,**A__):
												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(**A__)
												lowercase					=		scheduler_class(**A__)

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.prk_timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step_prk(A__     ,A__     ,A__).prev_sample

												for i, t in enumerate(scheduler.plms_timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step_plms(A__     ,A__     ,A__).prev_sample

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

																	if num_inference_steps is not None and hasattr(A__     ,'''set_timesteps'''):
																						scheduler.set_timesteps(A__)
																	elif num_inference_steps is not None and not hasattr(A__     ,'''set_timesteps'''):
																						lowercase					=		num_inference_steps

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

																	lowercase					=		scheduler.step_prk(A__     ,0     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step_prk(A__     ,1     ,A__     ,**A__).prev_sample

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

																	lowercase					=		scheduler.step_plms(A__     ,0     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step_plms(A__     ,1     ,A__     ,**A__).prev_sample

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




							def 				A__   (       self):
												for timesteps in [1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




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

												lowercase					=		self.scheduler_classes[0]
												lowercase					=		self.get_scheduler_config(steps_offset=1)
												lowercase					=		scheduler_class(**A__)
												scheduler.set_timesteps(1_0)
												assert torch.equal(
												    scheduler.timesteps     ,torch.LongTensor(
												        [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1])     ,)




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




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




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




							def 				A__   (       self):
												for t in [1, 5, 1_0]:
																	self.check_over_forward(time_step=A__)




							def 				A__   (       self):
												for t, num_inference_steps in zip([1, 5, 1_0]     ,[1_0, 5_0, 1_0_0]):
																	self.check_over_forward(num_inference_steps=A__)




							def 				A__   (       self):
												# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
												lowercase					=		2_7

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	scheduler.set_timesteps(A__)

																	# 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]):
																						lowercase					=		scheduler.step_prk(A__     ,A__     ,A__).prev_sample




							def 				A__   (       self):
												with self.assertRaises(A__):
																	lowercase					=		self.scheduler_classes[0]
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

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




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.sum(torch.abs(A__))
												lowercase					=		torch.mean(torch.abs(A__))

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




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.sum(torch.abs(A__))
												lowercase					=		torch.mean(torch.abs(A__))

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




							def 				A__   (       self):
												# We specify different beta, so that the first alpha is 0.99
												lowercase					=		self.full_loop(set_alpha_to_one=A__     ,beta_start=0.01)
												lowercase					=		torch.sum(torch.abs(A__))
												lowercase					=		torch.mean(torch.abs(A__))

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





							def 				A__   (       self):
												# We specify different beta, so that the first alpha is 0.99
												lowercase					=		self.full_loop(set_alpha_to_one=A__     ,beta_start=0.01)
												lowercase					=		torch.sum(torch.abs(A__))
												lowercase					=		torch.mean(torch.abs(A__))

												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


lowercase__	:Tuple										=					{
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:List[str]										=					[
							    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "InstructBlipQFormerModel",
							    "InstructBlipPreTrainedModel",
							    "InstructBlipForConditionalGeneration",
							    "InstructBlipVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_instructblip import (
							    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    InstructBlipConfig,
							    InstructBlipQFormerConfig,
							    InstructBlipVisionConfig,
							)
							from .processing_instructblip import InstructBlipProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_instructblip import (
														    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    InstructBlipForConditionalGeneration,
														    InstructBlipPreTrainedModel,
														    InstructBlipQFormerModel,
														    InstructBlipVisionModel,
														)

else:
							import sys

							lowercase__	:List[str]										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




