import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 			__snake_case  (      lowerCAmelCase_     ):
					__lowerCamelCase	 =       (DPMSolverMultistepScheduler,)
					__lowerCamelCase	 =       (("""num_inference_steps""", 25),)




					def       __a      (       self   ,	**__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







									snake_case__ :     int 							=				{
									    'num_train_timesteps': 1000,
									    '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',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'		),
									    'variance_type': None,
									}

									config.update(**_snake_case		)
									return config




					def       __a      (       self   ,	__UpperCamelCase=0   ,	**__UpperCamelCase		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				dict(self.forward_default_kwargs		)
									snake_case__ :     Union[str, Any] 							=				kwargs.pop('num_inference_steps'   ,	_snake_case		)
									snake_case__ :     Optional[Any] 							=				self.dummy_sample
									snake_case__ :     Optional[int] 							=				0.1 * sample
									snake_case__ :     Optional[Any] 							=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									pass




					def       __a      (       self   ,	__UpperCamelCase=0   ,	**__UpperCamelCase		)					->  Any:



									'''simple docstring'''







									snake_case__ :     Any 							=				dict(self.forward_default_kwargs		)
									snake_case__ :     List[str] 							=				kwargs.pop('num_inference_steps'   ,	_snake_case		)
									snake_case__ :     str 							=				self.dummy_sample
									snake_case__ :     Dict 							=				0.1 * sample
									snake_case__ :     Dict 							=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

									for scheduler_class in self.scheduler_classes:
													snake_case__ :     int 							=				self.get_scheduler_config()
													snake_case__ :     List[str] 							=				scheduler_class(**_snake_case		)
													scheduler.set_timesteps(_snake_case		)

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

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

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

													snake_case__ :     int 							=				scheduler.step(_snake_case   ,	_snake_case   ,	_snake_case   ,	**_snake_case		).prev_sample
													snake_case__ :     str 							=				new_scheduler.step(_snake_case   ,	_snake_case   ,	_snake_case   ,	**_snake_case		).prev_sample

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




					def       __a      (       self   ,	__UpperCamelCase=None   ,	**__UpperCamelCase		)					->  Union[str, Any]:



									'''simple docstring'''







									if scheduler is None:
													snake_case__ :     Union[str, Any] 							=				self.scheduler_classes[0]
													snake_case__ :     Optional[int] 							=				self.get_scheduler_config(**_snake_case		)
													snake_case__ :     str 							=				scheduler_class(**_snake_case		)

									snake_case__ :     Optional[int] 							=				10
									snake_case__ :     Optional[int] 							=				self.dummy_model()
									snake_case__ :     Dict 							=				self.dummy_sample_deter
									scheduler.set_timesteps(_snake_case		)

									for i, t in enumerate(scheduler.timesteps		):
													snake_case__ :     Optional[Any] 							=				model(_snake_case   ,	_snake_case		)
													snake_case__ :     Tuple 							=				scheduler.step(_snake_case   ,	_snake_case   ,	_snake_case		).prev_sample

									return sample




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__ :     str 							=				dict(self.forward_default_kwargs		)

									snake_case__ :     Any 							=				kwargs.pop('num_inference_steps'   ,	_snake_case		)

									for scheduler_class in self.scheduler_classes:
													snake_case__ :     Any 							=				self.get_scheduler_config()
													snake_case__ :     Union[str, Any] 							=				scheduler_class(**_snake_case		)

													snake_case__ :     Optional[int] 							=				self.dummy_sample
													snake_case__ :     str 							=				0.1 * sample

													if num_inference_steps is not None and hasattr(_snake_case   ,	'set_timesteps'		):
																	scheduler.set_timesteps(_snake_case		)
													elif num_inference_steps is not None and not hasattr(_snake_case   ,	'set_timesteps'		):
																	snake_case__ :     Tuple 							=				num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													snake_case__ :     Tuple 							=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													snake_case__ :     Any 							=				dummy_past_residuals[: scheduler.config.solver_order]

													snake_case__ :     Dict 							=				scheduler.timesteps[5]
													snake_case__ :     Dict 							=				scheduler.timesteps[6]

													snake_case__ :     str 							=				scheduler.step(_snake_case   ,	_snake_case   ,	_snake_case   ,	**_snake_case		).prev_sample
													snake_case__ :     Tuple 							=				scheduler.step(_snake_case   ,	_snake_case   ,	_snake_case   ,	**_snake_case		).prev_sample

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




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=_snake_case		)




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									self.check_over_configs(thresholding=_snake_case		)
									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=_snake_case   ,	prediction_type=_snake_case   ,	sample_max_value=_snake_case   ,	algorithm_type='dpmsolver++'   ,	solver_order=_snake_case   ,	solver_type=_snake_case   ,	)




					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







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




					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									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=_snake_case   ,	solver_type=_snake_case   ,	prediction_type=_snake_case   ,	algorithm_type=_snake_case   ,	)
																									snake_case__ :     Dict 							=				self.full_loop(
																									    solver_order=_snake_case   ,	solver_type=_snake_case   ,	prediction_type=_snake_case   ,	algorithm_type=_snake_case   ,	)
																									assert not torch.isnan(_snake_case		).any(), "Samples have nan numbers"




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									self.check_over_configs(lower_order_final=_snake_case		)
									self.check_over_configs(lower_order_final=_snake_case		)




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
									self.check_over_configs(lambda_min_clipped=-5.1		)




					def       __a      (       self		)					->  str:



									'''simple docstring'''







									self.check_over_configs(variance_type=_snake_case		)
									self.check_over_configs(variance_type='learned_range'		)




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=_snake_case   ,	time_step=0		)




					def       __a      (       self		)					->  List[Any]:



									'''simple docstring'''







									snake_case__ :     List[str] 							=				self.full_loop()
									snake_case__ :     str 							=				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3




					def       __a      (       self		)					->  Dict:



									'''simple docstring'''







									snake_case__ :     Any 							=				self.full_loop(thresholding=_snake_case   ,	dynamic_thresholding_ratio=0.8_7   ,	sample_max_value=0.5		)
									snake_case__ :     List[str] 							=				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_mean.item() - 1.1_3_6_4		) < 1E-3




					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     Tuple 							=				self.full_loop(prediction_type='v_prediction'		)
									snake_case__ :     Optional[int] 							=				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_mean.item() - 0.2_2_5_1		) < 1E-3




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				self.full_loop(prediction_type='v_prediction'   ,	use_karras_sigmas=_snake_case		)
									snake_case__ :     Optional[int] 							=				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_mean.item() - 0.2_0_9_6		) < 1E-3




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     int 							=				DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
									snake_case__ :     str 							=				self.full_loop(scheduler=_snake_case		)
									snake_case__ :     Union[str, Any] 							=				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3

									snake_case__ :     Dict 							=				DPMSolverSinglestepScheduler.from_config(scheduler.config		)
									snake_case__ :     Optional[int] 							=				UniPCMultistepScheduler.from_config(scheduler.config		)
									snake_case__ :     Any 							=				DEISMultistepScheduler.from_config(scheduler.config		)
									snake_case__ :     List[str] 							=				DPMSolverMultistepScheduler.from_config(scheduler.config		)

									snake_case__ :     Any 							=				self.full_loop(scheduler=_snake_case		)
									snake_case__ :     Dict 							=				torch.mean(torch.abs(_snake_case		)		)

									assert abs(result_mean.item() - 0.3_3_0_1		) < 1E-3




					def       __a      (       self		)					->  Optional[int]:



									'''simple docstring'''







									snake_case__ :     Optional[int] 							=				self.scheduler_classes[0]
									snake_case__ :     Optional[int] 							=				self.get_scheduler_config(thresholding=_snake_case   ,	dynamic_thresholding_ratio=0		)
									snake_case__ :     Union[str, Any] 							=				scheduler_class(**_snake_case		)

									snake_case__ :     int 							=				10
									snake_case__ :     Optional[Any] 							=				self.dummy_model()
									snake_case__ :     int 							=				self.dummy_sample_deter.half()
									scheduler.set_timesteps(_snake_case		)

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

									assert sample.dtype == torch.floataa




					def       __a      (       self   ,	**__UpperCamelCase		)					->  List[str]:



									'''simple docstring'''







									for scheduler_class in self.scheduler_classes:
													snake_case__ :     Any 							=				self.get_scheduler_config(**_snake_case		)
													snake_case__ :     int 							=				scheduler_class(**_snake_case		)

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



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCAmelCase__					:       Tuple		=       logging.getLogger(__name__)





@dataclass
class 			__snake_case  :
					__lowerCamelCase	 =       field(
					    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
					__lowerCamelCase	 =       field(
					    default=_lowerCamelCase   ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
					__lowerCamelCase	 =       field(
					    default=_lowerCamelCase   ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
					__lowerCamelCase	 =       field(default=_lowerCamelCase   ,metadata={"""help""": """Set this flag to use fast tokenization."""}     )
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					__lowerCamelCase	 =       field(
					    default=_lowerCamelCase   ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,)




def 			UpperCamelCase__						(    )       -> Optional[Any]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				snake_case__ :     Optional[Any] 							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )

				if len(sys.argv   ) == 2 and sys.argv[1].endswith('.json'   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case__							,  snake_case__							,  snake_case__ :     Tuple 							=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								snake_case__							,  snake_case__							,  snake_case__ :     Optional[int] 							=				parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir   )
				    and os.listdir(training_args.output_dir   )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ' --overwrite_output_dir to overcome.'   )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,			datefmt='%m/%d/%Y %H:%M:%S'      ,			level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,			)
				logger.warning(
				    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'      ,			training_args.local_rank      ,			training_args.device      ,			training_args.n_gpu      ,			bool(training_args.local_rank != -1   )      ,			training_args.fpaa      ,			)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank   ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('Training/evaluation parameters %s'      ,			A__   )

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				snake_case__ :     Optional[int] 							=				AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,			cache_dir=model_args.cache_dir      ,			)
				snake_case__ :     str 							=				AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,			cache_dir=model_args.cache_dir      ,			use_fast=A__      ,			)
				snake_case__ :     Optional[int] 							=				AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path      ,			from_tf=bool('.ckpt' in model_args.model_name_or_path   )      ,			config=A__      ,			cache_dir=model_args.cache_dir      ,			)

				# Get datasets
				snake_case__ :     Dict 							=				hasattr(model.config      ,			'lang2id'   )
				snake_case__ :     List[str] 							=				(
				    SquadDataset(
				        A__      ,			tokenizer=A__      ,			is_language_sensitive=A__      ,			cache_dir=model_args.cache_dir   )
				    if training_args.do_train
				    else None
				)
				snake_case__ :     str 							=				(
				    SquadDataset(
				        A__      ,			tokenizer=A__      ,			mode='dev'      ,			is_language_sensitive=A__      ,			cache_dir=model_args.cache_dir      ,			)
				    if training_args.do_eval
				    else None
				)

				# Data collator
				snake_case__ :     Dict 							=				DataCollatorWithPadding(A__      ,			pad_to_multiple_of=8   ) if training_args.fpaa else None

				# Initialize our Trainer
				snake_case__ :     str 							=				Trainer(
				    model=A__      ,			args=A__      ,			train_dataset=A__      ,			eval_dataset=A__      ,			data_collator=A__      ,			)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir   )
def 			UpperCamelCase__						(    A__   )       -> Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
				main()


