






"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       (DPMSolverSinglestepScheduler,)
	__magic_name__			   =       (('''num_inference_steps''', 25),)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			**lowerCAmelCase_      :				List[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				Tuple      =      {
				    "num_train_timesteps": 1_000,
				    "beta_start": 0.0_0_0_1,
				    "beta_end": 0.0_2,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				    "sample_max_value": 1.0,
				    "algorithm_type": "dpmsolver++",
				    "solver_type": "midpoint",
				    "lambda_min_clipped": -float("inf"		),
				    "variance_type": None,
				}

				config.update(**lowerCAmelCase_		)
				return config





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Tuple=0			,			**lowerCAmelCase_      :				Any		)		->    int:
				UpperCAmelCase_  :				Tuple      =      dict(self.forward_default_kwargs		)
				UpperCAmelCase_  :				str      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      self.dummy_sample
				UpperCAmelCase_  :				Optional[int]      =      0.1 * sample
				UpperCAmelCase_  :				Union[str, Any]      =      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				Dict      =      self.get_scheduler_config(**lowerCAmelCase_		)
							UpperCAmelCase_  :				int      =      scheduler_class(**lowerCAmelCase_		)
							scheduler.set_timesteps(lowerCAmelCase_		)
							# copy over dummy past residuals
							UpperCAmelCase_  :				Optional[int]      =      dummy_past_residuals[: scheduler.config.solver_order]

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase_		)
										UpperCAmelCase_  :				Dict      =      scheduler_class.from_pretrained(lowerCAmelCase_		)
										new_scheduler.set_timesteps(lowerCAmelCase_		)
										# copy over dummy past residuals
										UpperCAmelCase_  :				List[Any]      =      dummy_past_residuals[: new_scheduler.config.solver_order]

							UpperCAmelCase_  :				Tuple      =      sample, sample
							for t in range(lowerCAmelCase_			,			time_step + scheduler.config.solver_order + 1		):
										UpperCAmelCase_  :				List[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
										UpperCAmelCase_  :				List[Any]      =      new_scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample

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





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Union[str, Any]=0			,			**lowerCAmelCase_      :				int		)		->    int:
				UpperCAmelCase_  :				Dict      =      dict(self.forward_default_kwargs		)
				UpperCAmelCase_  :				List[Any]      =      kwargs.pop("num_inference_steps"			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      self.dummy_sample
				UpperCAmelCase_  :				Any      =      0.1 * sample
				UpperCAmelCase_  :				List[str]      =      [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

				for scheduler_class in self.scheduler_classes:
							UpperCAmelCase_  :				int      =      self.get_scheduler_config()
							UpperCAmelCase_  :				Dict      =      scheduler_class(**lowerCAmelCase_		)
							scheduler.set_timesteps(lowerCAmelCase_		)

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowerCAmelCase_		)
										UpperCAmelCase_  :				Union[str, Any]      =      scheduler_class.from_pretrained(lowerCAmelCase_		)
										# copy over dummy past residuals
										new_scheduler.set_timesteps(lowerCAmelCase_		)

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

							UpperCAmelCase_  :				int      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample
							UpperCAmelCase_  :				Tuple      =      new_scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			**lowerCAmelCase_		).prev_sample

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				Optional[Any]=None			,			**lowerCAmelCase_      :				Any		)		->    Optional[Any]:
				if scheduler is None:
							UpperCAmelCase_  :				List[str]      =      self.scheduler_classes[0]
							UpperCAmelCase_  :				Dict      =      self.get_scheduler_config(**lowerCAmelCase_		)
							UpperCAmelCase_  :				List[str]      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				Any      =      10
				UpperCAmelCase_  :				Dict      =      self.dummy_model()
				UpperCAmelCase_  :				int      =      self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase_  :				List[str]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				int      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				return sample





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				List[Any]      =      DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
				UpperCAmelCase_  :				Dict      =      50
				UpperCAmelCase_  :				Any      =      self.dummy_model()
				UpperCAmelCase_  :				Dict      =      self.dummy_sample_deter
				scheduler.set_timesteps(lowerCAmelCase_		)

				# make sure that the first t is uneven
				for i, t in enumerate(scheduler.timesteps[3:]		):
							UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				Optional[Any]      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				UpperCAmelCase_  :				Dict      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_5_7_4		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Any:
				for timesteps in [25, 50, 100, 999, 1_000]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    str:
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				UpperCAmelCase_  :				List[str]      =      DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
				UpperCAmelCase_  :				Any      =      self.full_loop(scheduler=lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_7_9_1		) < 1e-3

				UpperCAmelCase_  :				Tuple      =      DEISMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase_  :				Tuple      =      DPMSolverMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase_  :				Any      =      UniPCMultistepScheduler.from_config(scheduler.config		)
				UpperCAmelCase_  :				Optional[Any]      =      DPMSolverSinglestepScheduler.from_config(scheduler.config		)

				UpperCAmelCase_  :				Any      =      self.full_loop(scheduler=lowerCAmelCase_		)
				UpperCAmelCase_  :				Dict      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_7_9_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    int:
				self.check_over_configs(thresholding=lowerCAmelCase_		)
				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=lowerCAmelCase_			,			prediction_type=lowerCAmelCase_			,			sample_max_value=lowerCAmelCase_			,			algorithm_type="dpmsolver++"			,			solver_order=lowerCAmelCase_			,			solver_type=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				for prediction_type in ["epsilon", "v_prediction"]:
							self.check_over_configs(prediction_type=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Union[str, Any]:
				for algorithm_type in ["dpmsolver", "dpmsolver++"]:
							for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(
																    solver_order=lowerCAmelCase_			,			solver_type=lowerCAmelCase_			,			prediction_type=lowerCAmelCase_			,			algorithm_type=lowerCAmelCase_			,			)
																UpperCAmelCase_  :				str      =      self.full_loop(
																    solver_order=lowerCAmelCase_			,			solver_type=lowerCAmelCase_			,			prediction_type=lowerCAmelCase_			,			algorithm_type=lowerCAmelCase_			,			)
																assert not torch.isnan(lowerCAmelCase_		).any(), "Samples have nan numbers"





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Optional[Any]:
				self.check_over_configs(lower_order_final=lowerCAmelCase_		)
				self.check_over_configs(lower_order_final=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Any:
				self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
				self.check_over_configs(lambda_min_clipped=-5.1		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Dict:
				self.check_over_configs(variance_type=lowerCAmelCase_		)
				self.check_over_configs(variance_type="learned_range"		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
							self.check_over_forward(num_inference_steps=lowerCAmelCase_			,			time_step=0		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.full_loop()
				UpperCAmelCase_  :				List[str]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_7_9_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				Any      =      self.full_loop(use_karras_sigmas=lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.2_2_4_8		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    int:
				UpperCAmelCase_  :				List[str]      =      self.full_loop(prediction_type="v_prediction"		)
				UpperCAmelCase_  :				Tuple      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.1_4_5_3		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Optional[Any]:
				UpperCAmelCase_  :				Any      =      self.full_loop(prediction_type="v_prediction"			,			use_karras_sigmas=lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      torch.mean(torch.abs(lowerCAmelCase_		)		)

				assert abs(result_mean.item() - 0.0_6_4_9		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				List[Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config(thresholding=lowerCAmelCase_			,			dynamic_thresholding_ratio=0		)
				UpperCAmelCase_  :				Any      =      scheduler_class(**lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      10
				UpperCAmelCase_  :				Optional[int]      =      self.dummy_model()
				UpperCAmelCase_  :				Optional[Any]      =      self.dummy_sample_deter.half()
				scheduler.set_timesteps(lowerCAmelCase_		)

				for i, t in enumerate(scheduler.timesteps		):
							UpperCAmelCase_  :				List[Any]      =      model(lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      scheduler.step(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		).prev_sample

				assert sample.dtype == torch.floataa












"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]			,			dtype=tf.intaa			,			)  # J'aime le camembert !"

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			dtype=tf.floataa			,			)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,			expected_slice.numpy()			,			atol=1e-4		)		)



