



'''simple docstring'''







import argparse
import json
import logging
import os
import sys
from unittest import skip
from unittest.mock import patch

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__     : Optional[int]				=   [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-generation""",
        """text-classification""",
        """token-classification""",
        """language-modeling""",
        """multiple-choice""",
        """question-answering""",
        """summarization""",
        """translation""",
        """image-classification""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
							import run_clm
							import run_image_classification
							import run_mlm
							import run_ner
							import run_qa as run_squad
							import run_summarization
							import run_swag
							import run_text_classification
							import run_translation


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.getLogger()







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Any										=      argparse.ArgumentParser()
		parser.add_argument('-f'     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      parser.parse_args()
		return args.f







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      {}
		SCREAMING_SNAKE_CASE_						:int										=      os.path.join(SCREAMING_SNAKE_CASE ,		'all_results.json'     )
		if os.path.exists(SCREAMING_SNAKE_CASE     ):
				with open(SCREAMING_SNAKE_CASE ,		'r'     ) as f:
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      json.load(SCREAMING_SNAKE_CASE     )
		else:
				raise ValueError(F'can\'t find {path}'     )
		return results







def  SCREAMING_SNAKE_CASE__ (				):
		return bool(tf.config.list_physical_devices('GPU'     )     )


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			@skip('Skipping until shape inference for to_tf_dataset PR is merged.'	)
			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[str]										=      f'\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

					if is_cuda_available():
							testargs.append('--fp16'	)

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_text_classification.main()
							# Reset the mixed precision policy so we don't break other tests
							tf.keras.mixed_precision.set_global_policy('float32'	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['eval_accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

					if len(tf.config.list_physical_devices('GPU'	)	) > 1:
							# Skipping because there are not enough batches to train the model + would need a drop_last to work.
							return

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_clm.main()
							SCREAMING_SNAKE_CASE_						:Any										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       100	)






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:int										=      f'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_mlm.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertLess(result['eval_perplexity']      ,       42	)






			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      7 if get_gpu_count() > 1 else 2

					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:str										=      f'\n            run_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_ner.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.75	)






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_squad.main()
							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['f1']      ,       30	)
							self.assertGreaterEqual(result['exact']      ,       30	)






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




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      f'\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_swag.main()
							SCREAMING_SNAKE_CASE_						:int										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['val_accuracy']      ,       0.8	)






			@slow
			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      f'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_summarization.main()
							SCREAMING_SNAKE_CASE_						:Dict										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['rouge1']      ,       10	)
							self.assertGreaterEqual(result['rouge2']      ,       2	)
							self.assertGreaterEqual(result['rougeL']      ,       7	)
							self.assertGreaterEqual(result['rougeLsum']      ,       7	)






			@slow
			def 				_lowercase	(   self  :							Optional[Any]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:List[Any]										=      f'\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_translation.main()
							SCREAMING_SNAKE_CASE_						:str										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['bleu']      ,       30	)






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[str]										=      self.get_auto_remove_tmp_dir()
					SCREAMING_SNAKE_CASE_						:Any										=      f'\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            '.split()

					with patch.object(SCREAMING_SNAKE_CASE      ,       'argv'      ,       SCREAMING_SNAKE_CASE	):
							run_image_classification.main()
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      get_results(SCREAMING_SNAKE_CASE	)
							self.assertGreaterEqual(result['accuracy']      ,       0.7	)


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			(DPMSolverMultistepScheduler,)
						__a     :    Dict												=			(("""num_inference_steps""", 2_5),)


						def    A__       (  self  , **A	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    {
									    'num_train_timesteps': 10_00,
									    'beta_start': 0.0001,
									    '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(**A	)
									return config


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:int 		=    self.dummy_sample
									UpperCAmelCase__		:Optional[int] 		=    0.1 * sample
									UpperCAmelCase__		:Any 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:List[Any] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)
												# copy over dummy past residuals
												UpperCAmelCase__		:Optional[Any] 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:List[Any] 		=    scheduler_class.from_pretrained(A	)
															new_scheduler.set_timesteps(A	)
															# copy over dummy past residuals
															UpperCAmelCase__		:Union[str, Any] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    sample, sample
												for t in range(A  , time_step + scheduler.config.solver_order + 1	):
															UpperCAmelCase__		:Dict 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
															UpperCAmelCase__		:Optional[Any] 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->List[Any]:
									pass


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:str 		=    self.dummy_sample
									UpperCAmelCase__		:Union[str, Any] 		=    0.1 * sample
									UpperCAmelCase__		:Union[str, Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:List[Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:List[str] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)

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

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:int 		=    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)
															UpperCAmelCase__		:Optional[int] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		:Any 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:Dict 		=    new_scheduler.step(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=None  , **A	)  ->List[str]:
									if scheduler is None:
												UpperCAmelCase__		:Optional[Any] 		=    self.scheduler_classes[0]
												UpperCAmelCase__		:Optional[Any] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler_class(**A	)

									UpperCAmelCase__		:List[Any] 		=    10
									UpperCAmelCase__		:int 		=    self.dummy_model()
									UpperCAmelCase__		:int 		=    self.dummy_sample_deter
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:Union[str, Any] 		=    model(A  , A	)
												UpperCAmelCase__		:str 		=    scheduler.step(A  , A  , A	).prev_sample

									return sample


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    dict(self.forward_default_kwargs	)

									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('num_inference_steps'  , A	)

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:Optional[Any] 		=    scheduler_class(**A	)

												UpperCAmelCase__		:Dict 		=    self.dummy_sample
												UpperCAmelCase__		:Dict 		=    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'	):
															UpperCAmelCase__		:List[Any] 		=    num_inference_steps

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

												UpperCAmelCase__		:str 		=    scheduler.timesteps[5]
												UpperCAmelCase__		:str 		=    scheduler.timesteps[6]

												UpperCAmelCase__		:List[str] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:List[Any] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->Optional[Any]:
									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
												self.check_over_configs(num_train_timesteps=A	)


						def    A__       (  self	)  ->str:
									self.check_over_configs(thresholding=A	)
									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=A  , prediction_type=A  , sample_max_value=A  , algorithm_type='dpmsolver++'  , solver_order=A  , solver_type=A  , )


						def    A__       (  self	)  ->Optional[int]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=A	)


						def    A__       (  self	)  ->List[str]:
									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=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					UpperCAmelCase__		:Optional[int] 		=    self.full_loop(
																					    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					assert not torch.isnan(A	).any(), "Samples have nan numbers"


						def    A__       (  self	)  ->List[Any]:
									self.check_over_configs(lower_order_final=A	)
									self.check_over_configs(lower_order_final=A	)


						def    A__       (  self	)  ->Optional[Any]:
									self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
									self.check_over_configs(lambda_min_clipped=-5.1	)


						def    A__       (  self	)  ->Union[str, Any]:
									self.check_over_configs(variance_type=A	)
									self.check_over_configs(variance_type='learned_range'	)


						def    A__       (  self	)  ->List[str]:
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
												self.check_over_forward(num_inference_steps=A  , time_step=0	)


						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    self.full_loop()
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(thresholding=A  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5	)
									UpperCAmelCase__		:Any 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 1.1364	) < 1e-3


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.full_loop(prediction_type='v_prediction'	)
									UpperCAmelCase__		:int 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2251	) < 1e-3


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(prediction_type='v_prediction'  , use_karras_sigmas=A	)
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2096	) < 1e-3


						def    A__       (  self	)  ->List[Any]:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									UpperCAmelCase__		:Tuple 		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:List[Any] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3

									UpperCAmelCase__		:Dict 		=    DPMSolverSinglestepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    UniPCMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    DEISMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    DPMSolverMultistepScheduler.from_config(scheduler.config	)

									UpperCAmelCase__		:Dict 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:Dict 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    self.scheduler_classes[0]
									UpperCAmelCase__		:List[str] 		=    self.get_scheduler_config(thresholding=A  , dynamic_thresholding_ratio=0	)
									UpperCAmelCase__		:str 		=    scheduler_class(**A	)

									UpperCAmelCase__		:int 		=    10
									UpperCAmelCase__		:Optional[int] 		=    self.dummy_model()
									UpperCAmelCase__		:List[str] 		=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:str 		=    model(A  , A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler.step(A  , A  , A	).prev_sample

									assert sample.dtype == torch.floataa


						def    A__       (  self  , **A	)  ->Any:
									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:int 		=    scheduler_class(**A	)

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





