

"""simple docstring"""






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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case_   :   Dict        =       logging.getLogger(__name__)



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		str	):




					'''simple docstring'''






					return (preds == labels).mean()







@dataclass
class       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}     )
			SCREAMING_SNAKE_CASE__     				=						field(
			    default=__a					,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
			SCREAMING_SNAKE_CASE__     				=						field(
			    default=__a					,  metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}     )
			SCREAMING_SNAKE_CASE__     				=						field(
			    default=__a					,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					,  )







@dataclass
class       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()     )}     )
			SCREAMING_SNAKE_CASE__     				=						field(metadata={'''help''': '''Should contain the data files for the task.'''}     )
			SCREAMING_SNAKE_CASE__     				=						field(
			    default=128					,  metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }					,  )
			SCREAMING_SNAKE_CASE__     				=						field(
			    default=__a					,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   List[str]      =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
					UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   str      =				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"							,      lowercase__	)

					# Set seed
					set_seed(training_args.seed	)

					try:
										UpperCAmelCase			:   Optional[int]      =				processors[data_args.task_name]()
										UpperCAmelCase			:   int      =				processor.get_labels()
										UpperCAmelCase			:   Union[str, Any]      =				len(lowercase__	)
					except KeyError:
										raise ValueError("Task not found: %s" % (data_args.task_name)	)

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

					UpperCAmelCase			:   str      =				AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      num_labels=lowercase__							,      finetuning_task=data_args.task_name							,      cache_dir=model_args.cache_dir							,      )
					UpperCAmelCase			:   Tuple      =				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							,      )
					UpperCAmelCase			:   int      =				AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path							,      from_tf=bool(".ckpt" in model_args.model_name_or_path	)							,      config=lowercase__							,      cache_dir=model_args.cache_dir							,      )

					# Get datasets
					UpperCAmelCase			:   Optional[int]      =				(
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir							,      tokenizer=lowercase__							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      mode=Split.train							,      )
					    if training_args.do_train
					    else None
					)
					UpperCAmelCase			:   Any      =				(
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir							,      tokenizer=lowercase__							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      mode=Split.dev							,      )
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(_lowercase       :		List[str]	) -> Dict:
										UpperCAmelCase			:   List[Any]      =				np.argmax(p.predictions							,      axis=1	)
										return {"acc": simple_accuracy(lowercase__							,      p.label_ids	)}

					# Data collator
					UpperCAmelCase			:   Tuple      =				DataCollatorWithPadding(lowercase__							,      pad_to_multiple_of=8	) if training_args.fpaa else None

					# Initialize our Trainer
					UpperCAmelCase			:   str      =				Trainer(
					    model=lowercase__							,      args=lowercase__							,      train_dataset=lowercase__							,      eval_dataset=lowercase__							,      compute_metrics=lowercase__							,      data_collator=lowercase__							,      )

					# 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	)

    # Evaluation
					UpperCAmelCase			:   List[str]      =				{}
					if training_args.do_eval:
										logger.info("*** Evaluate ***"	)

										UpperCAmelCase			:   List[Any]      =				trainer.evaluate()

										UpperCAmelCase			:   Tuple      =				os.path.join(training_args.output_dir							,      "eval_results.txt"	)
										if trainer.is_world_master():
															with open(lowercase__							,      "w"	) as writer:
																				logger.info("***** Eval results *****"	)
																				for key, value in result.items():
																									logger.info("  %s = %s"							,      lowercase__							,      lowercase__	)
																									writer.write("%s = %s\n" % (key, value)	)

																				results.update(lowercase__	)

					return results



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




					'''simple docstring'''






					main()


if __name__ == "__main__":
	main()





"""simple docstring"""






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						(UniPCMultistepScheduler,)
			SCREAMING_SNAKE_CASE__     				=						(('''num_inference_steps''', 25),)







			def 			__lowerCAmelCase				(			self			: Tuple			,  **lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**lowercase  )
								return config







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[str]=0			,  **lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Dict      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   str      =				self.dummy_sample
								UpperCAmelCase			:   Tuple      =				0.1 * sample
								UpperCAmelCase			:   int      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

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







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: int=0			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Union[str, Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   Tuple      =				self.dummy_sample
								UpperCAmelCase			:   Any      =				0.1 * sample
								UpperCAmelCase			:   int      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Optional[int]      =				self.get_scheduler_config()
													UpperCAmelCase			:   List[Any]      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )

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

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

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

													UpperCAmelCase			:   Dict      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   Any      =				new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

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







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Optional[int]=None			,  **lowercase			: int  ):





								'''simple docstring'''



								if scheduler is None:
													UpperCAmelCase			:   Dict      =				self.scheduler_classes[0]
													UpperCAmelCase			:   int      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   str      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Optional[int]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Dict      =				self.get_scheduler_config(**lowercase  )
								UpperCAmelCase			:   int      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   List[str]      =				10
								UpperCAmelCase			:   Optional[Any]      =				self.dummy_model()
								UpperCAmelCase			:   Union[str, Any]      =				self.dummy_sample_deter
								scheduler.set_timesteps(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   List[str]      =				model(lowercase			,  lowercase  )
													UpperCAmelCase			:   Optional[Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample

								return sample







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





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				dict(self.forward_default_kwargs  )

								UpperCAmelCase			:   List[Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   str      =				self.get_scheduler_config()
													UpperCAmelCase			:   Dict      =				scheduler_class(**lowercase  )

													UpperCAmelCase			:   Any      =				self.dummy_sample
													UpperCAmelCase			:   Any      =				0.1 * sample

													if num_inference_steps is not None and hasattr(lowercase			,  "set_timesteps"  ):
																		scheduler.set_timesteps(lowercase  )
													elif num_inference_steps is not None and not hasattr(lowercase			,  "set_timesteps"  ):
																		UpperCAmelCase			:   str      =				num_inference_steps

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

													UpperCAmelCase			:   str      =				scheduler.timesteps[5]
													UpperCAmelCase			:   List[Any]      =				scheduler.timesteps[6]

													UpperCAmelCase			:   Union[str, Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   int      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

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







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





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				UniPCMultistepScheduler(**self.get_scheduler_config()  )
								UpperCAmelCase			:   Tuple      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3

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

								UpperCAmelCase			:   Dict      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=lowercase  )







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





								'''simple docstring'''



								self.check_over_configs(thresholding=lowercase  )
								for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																							for prediction_type in ["epsilon", "sample"]:
																												self.check_over_configs(
																												    thresholding=lowercase			,  prediction_type=lowercase			,  sample_max_value=lowercase			,  solver_order=lowercase			,  solver_type=lowercase			,  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							UpperCAmelCase			:   Union[str, Any]      =				self.full_loop(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							assert not torch.isnan(lowercase  ).any(), "Samples have nan numbers"







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.check_over_configs(lower_order_final=lowercase  )
								self.check_over_configs(lower_order_final=lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=lowercase			,  time_step=0  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop()
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop(prediction_type="v_prediction"  )
								UpperCAmelCase			:   int      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.1_0_1_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   int      =				self.get_scheduler_config(thresholding=lowercase			,  dynamic_thresholding_ratio=0  )
								UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Any      =				10
								UpperCAmelCase			:   Dict      =				self.dummy_model()
								UpperCAmelCase			:   List[str]      =				self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowercase  )

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

								assert sample.dtype == torch.floataa







			def 			__lowerCAmelCase				(			self			: List[str]			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



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

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


