
'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class       lowerCAmelCase			(					__UpperCAmelCase      ):
		a				:       Optional[Any]							=       (DPMSolverMultistepScheduler,)
		a				:       Optional[Any]							=       (("""num_inference_steps""", 25),)




		def      lowercase							(					self       ,    **UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								    "sample_max_value": 1.0,
								    "algorithm_type": "dpmsolver++",
								    "solver_type": "midpoint",
								    "lower_order_final": False,
								    "lambda_min_clipped": -float("inf"   ),
								    "variance_type": None,
								}

								config.update(**UpperCamelCase   )
								return config




		def      lowercase							(					self       ,    UpperCamelCase=0       ,    **UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			dict(self.forward_default_kwargs   )
								_SCREAMING_SNAKE_CASE					=			kwargs.pop("num_inference_steps"       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			self.dummy_sample
								_SCREAMING_SNAKE_CASE					=			0.1 * sample
								_SCREAMING_SNAKE_CASE					=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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




		def      lowercase							(					self   ):
								pass




		def      lowercase							(					self       ,    UpperCamelCase=0       ,    **UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			dict(self.forward_default_kwargs   )
								_SCREAMING_SNAKE_CASE					=			kwargs.pop("num_inference_steps"       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			self.dummy_sample
								_SCREAMING_SNAKE_CASE					=			0.1 * sample
								_SCREAMING_SNAKE_CASE					=			[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE					=			self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE					=			scheduler_class(**UpperCamelCase   )
														scheduler.set_timesteps(UpperCamelCase   )

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(UpperCamelCase   )
																				_SCREAMING_SNAKE_CASE					=			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					=			dummy_past_residuals[: new_scheduler.config.solver_order]

														_SCREAMING_SNAKE_CASE					=			scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    **UpperCamelCase   ).prev_sample
														_SCREAMING_SNAKE_CASE					=			new_scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    **UpperCamelCase   ).prev_sample

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




		def      lowercase							(					self       ,    UpperCamelCase=None       ,    **UpperCamelCase   ):
								if scheduler is None:
														_SCREAMING_SNAKE_CASE					=			self.scheduler_classes[0]
														_SCREAMING_SNAKE_CASE					=			self.get_scheduler_config(**UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			scheduler_class(**UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			10
								_SCREAMING_SNAKE_CASE					=			self.dummy_model()
								_SCREAMING_SNAKE_CASE					=			self.dummy_sample_deter
								scheduler.set_timesteps(UpperCamelCase   )

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

								return sample




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

								_SCREAMING_SNAKE_CASE					=			kwargs.pop("num_inference_steps"       ,    UpperCamelCase   )

								for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE					=			self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE					=			scheduler_class(**UpperCamelCase   )

														_SCREAMING_SNAKE_CASE					=			self.dummy_sample
														_SCREAMING_SNAKE_CASE					=			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					=			num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_SCREAMING_SNAKE_CASE					=			[residual + 0.2, residual + 0.15, residual + 0.10]
														_SCREAMING_SNAKE_CASE					=			dummy_past_residuals[: scheduler.config.solver_order]

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

														_SCREAMING_SNAKE_CASE					=			scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    **UpperCamelCase   ).prev_sample
														_SCREAMING_SNAKE_CASE					=			scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    **UpperCamelCase   ).prev_sample

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




		def      lowercase							(					self   ):
								for timesteps in [25, 50, 100, 999, 1_000]:
														self.check_over_configs(num_train_timesteps=UpperCamelCase   )




		def      lowercase							(					self   ):
								self.check_over_configs(thresholding=UpperCamelCase   )
								for order in [1, 2, 3]:
														for solver_type in ["midpoint", "heun"]:
																				for threshold in [0.5, 1.0, 2.0]:
																										for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=UpperCamelCase       ,    prediction_type=UpperCamelCase       ,    sample_max_value=UpperCamelCase       ,    algorithm_type="dpmsolver++"       ,    solver_order=UpperCamelCase       ,    solver_type=UpperCamelCase       ,    )




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




		def      lowercase							(					self   ):
								for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
														for solver_type in ["midpoint", "heun"]:
																				for order in [1, 2, 3]:
																										for prediction_type in ["epsilon", "sample"]:
																																if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																						if order == 3:
																																												continue
																																else:
																																						self.check_over_configs(
																																						    solver_order=UpperCamelCase       ,    solver_type=UpperCamelCase       ,    prediction_type=UpperCamelCase       ,    algorithm_type=UpperCamelCase       ,    )
																																_SCREAMING_SNAKE_CASE					=			self.full_loop(
																																    solver_order=UpperCamelCase       ,    solver_type=UpperCamelCase       ,    prediction_type=UpperCamelCase       ,    algorithm_type=UpperCamelCase       ,    )
																																assert not torch.isnan(UpperCamelCase   ).any(), "Samples have nan numbers"




		def      lowercase							(					self   ):
								self.check_over_configs(lower_order_final=UpperCamelCase   )
								self.check_over_configs(lower_order_final=UpperCamelCase   )




		def      lowercase							(					self   ):
								self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
								self.check_over_configs(lambda_min_clipped=-5.1   )




		def      lowercase							(					self   ):
								self.check_over_configs(variance_type=UpperCamelCase   )
								self.check_over_configs(variance_type="learned_range"   )




		def      lowercase							(					self   ):
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
														self.check_over_forward(num_inference_steps=UpperCamelCase       ,    time_step=0   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.full_loop()
								_SCREAMING_SNAKE_CASE					=			torch.mean(torch.abs(UpperCamelCase   )   )

								assert abs(result_mean.item() - 0.33_01   ) < 1e-3




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.full_loop(thresholding=UpperCamelCase       ,    dynamic_thresholding_ratio=0.87       ,    sample_max_value=0.5   )
								_SCREAMING_SNAKE_CASE					=			torch.mean(torch.abs(UpperCamelCase   )   )

								assert abs(result_mean.item() - 1.13_64   ) < 1e-3




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.full_loop(prediction_type="v_prediction"   )
								_SCREAMING_SNAKE_CASE					=			torch.mean(torch.abs(UpperCamelCase   )   )

								assert abs(result_mean.item() - 0.22_51   ) < 1e-3




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.full_loop(prediction_type="v_prediction"       ,    use_karras_sigmas=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			torch.mean(torch.abs(UpperCamelCase   )   )

								assert abs(result_mean.item() - 0.20_96   ) < 1e-3




		def      lowercase							(					self   ):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								_SCREAMING_SNAKE_CASE					=			DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
								_SCREAMING_SNAKE_CASE					=			self.full_loop(scheduler=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			torch.mean(torch.abs(UpperCamelCase   )   )

								assert abs(result_mean.item() - 0.33_01   ) < 1e-3

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

								_SCREAMING_SNAKE_CASE					=			self.full_loop(scheduler=UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			torch.mean(torch.abs(UpperCamelCase   )   )

								assert abs(result_mean.item() - 0.33_01   ) < 1e-3




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.scheduler_classes[0]
								_SCREAMING_SNAKE_CASE					=			self.get_scheduler_config(thresholding=UpperCamelCase       ,    dynamic_thresholding_ratio=0   )
								_SCREAMING_SNAKE_CASE					=			scheduler_class(**UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			10
								_SCREAMING_SNAKE_CASE					=			self.dummy_model()
								_SCREAMING_SNAKE_CASE					=			self.dummy_sample_deter.half()
								scheduler.set_timesteps(UpperCamelCase   )

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

								assert sample.dtype == torch.floataa




		def      lowercase							(					self       ,    **UpperCamelCase   ):
								for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE					=			self.get_scheduler_config(**UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			scheduler_class(**UpperCamelCase   )

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

'''simple docstring'''







import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						import torch

						from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCAmelCase			(					unittest.TestCase      ):




		@slow
		@unittest.skip("Model is not available."   )
		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"nvidia/megatron-gpt2-345m/"
								if "MYDIR" in os.environ:
														_SCREAMING_SNAKE_CASE					=			os.path.join(os.environ["MYDIR"]       ,    UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			GPTaLMHeadModel.from_pretrained(UpperCamelCase   )
								model.to(UpperCamelCase   )
								model.half()

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       ,    device=UpperCamelCase       ,    dtype=torch.long       ,    )

								with torch.no_grad():
														_SCREAMING_SNAKE_CASE					=			model(UpperCamelCase   ).logits

								_SCREAMING_SNAKE_CASE					=			torch.Size((1, 9, 50_257)   )
								self.assertEqual(output.shape       ,    UpperCamelCase   )

								_SCREAMING_SNAKE_CASE					=			torch.tensor(
								    [
								        4.94_14,
								        -0.29_20,
								        -1.21_48,
								        -4.02_73,
								        -0.51_61,
								        -5.21_09,
								        -1.24_12,
								        -1.83_01,
								        -1.77_34,
								        -4.71_48,
								        -0.23_17,
								        -1.08_11,
								        -2.17_77,
								        0.41_41,
								        -3.79_69,
								        -4.05_86,
								        -2.53_32,
								        -3.38_09,
								        4.38_67,
								    ]       ,    device=UpperCamelCase       ,    dtype=torch.half       ,    )

								for i in range(19   ):
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			8 * i // 17, 2_792 * i  # along the diagonal
														_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			output[0, r, c], expected_diag[i]
														_SCREAMING_SNAKE_CASE					=			F'row={r} col={c} computed={computed} expected={expected}'
														self.assertAlmostEqual(UpperCamelCase       ,    UpperCamelCase       ,    delta=1e-4       ,    msg=UpperCamelCase   )