



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		List[Any]      =(DDIMScheduler,)
							__lowerCamelCase		:		str      =(('eta', 0.0), ('num_inference_steps', 50))





							def   UpperCamelCase_   (				self						:						Optional[int] ,			**__lowercase						:						Optional[int]		):

											'''simple docstring'''
											__a					       =						{
											    """num_train_timesteps""": 1000,
											    """beta_start""": 0.0001,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											    """clip_sample""": True,
											}

											config.update(**__lowercase		)
											return config





							def   UpperCamelCase_   (				self						:						Tuple ,			**__lowercase						:						Tuple		):

											'''simple docstring'''
											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config(**__lowercase		)
											__a					       =						scheduler_class(**__lowercase		)

											__a       ,   __a					       =						10, 0.0

											__a					       =						self.dummy_model()
											__a					       =						self.dummy_sample_deter

											scheduler.set_timesteps(__lowercase		)

											for t in scheduler.timesteps:
															__a					       =						model(__lowercase ,			__lowercase		)
															__a					       =						scheduler.step(__lowercase ,			__lowercase ,			__lowercase ,			__lowercase		).prev_sample

											return sample





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											for timesteps in [100, 500, 1000]:
															self.check_over_configs(num_train_timesteps=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											for steps_offset in [0, 1]:
															self.check_over_configs(steps_offset=__lowercase		)

											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config(steps_offset=1		)
											__a					       =						scheduler_class(**__lowercase		)
											scheduler.set_timesteps(5		)
											assert torch.equal(scheduler.timesteps ,			torch.LongTensor([801, 601, 401, 201, 1]		)		)





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,			[0.002, 0.02, 0.2, 2]		):
															self.check_over_configs(beta_start=__lowercase ,			beta_end=__lowercase		)





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

											'''simple docstring'''
											for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=__lowercase		)





							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=__lowercase		)





							def   UpperCamelCase_   (				self						:						Optional[int]		):

											'''simple docstring'''
											for timestep_spacing in ["trailing", "leading"]:
															self.check_over_configs(timestep_spacing=__lowercase		)





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

											'''simple docstring'''
											for rescale_betas_zero_snr in [True, False]:
															self.check_over_configs(rescale_betas_zero_snr=__lowercase		)





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											self.check_over_configs(thresholding=__lowercase		)
											for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=__lowercase ,			prediction_type=__lowercase ,			sample_max_value=__lowercase ,			)





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

											'''simple docstring'''
											for t in [1, 10, 49]:
															self.check_over_forward(time_step=__lowercase		)





							def   UpperCamelCase_   (				self						:						Dict		):

											'''simple docstring'''
											for t, num_inference_steps in zip([1, 10, 50] ,			[10, 50, 500]		):
															self.check_over_forward(time_step=__lowercase ,			num_inference_steps=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]		):
															self.check_over_forward(time_step=__lowercase ,			eta=__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											__a					       =						self.scheduler_classes[0]
											__a					       =						self.get_scheduler_config()
											__a					       =						scheduler_class(**__lowercase		)

											assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(420 ,			400		) - 0.14771		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(980 ,			960		) - 0.32460		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(487 ,			486		) - 0.00979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999 ,			998		) - 0.02		)		) < 1E-5





							def   UpperCamelCase_   (				self						:						Any		):

											'''simple docstring'''
											__a					       =						self.full_loop()

											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 172.0067		) < 1E-2
											assert abs(result_mean.item() - 0.223967		) < 1E-3





							def   UpperCamelCase_   (				self						:						Tuple		):

											'''simple docstring'''
											__a					       =						self.full_loop(prediction_type="""v_prediction"""		)

											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 52.5302		) < 1E-2
											assert abs(result_mean.item() - 0.0684		) < 1E-3





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											# We specify different beta, so that the first alpha is 0.99
											__a					       =						self.full_loop(set_alpha_to_one=__lowercase ,			beta_start=0.01		)
											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 149.8295		) < 1E-2
											assert abs(result_mean.item() - 0.1951		) < 1E-3







							def   UpperCamelCase_   (				self						:						int		):

											'''simple docstring'''
											# We specify different beta, so that the first alpha is 0.99
											__a					       =						self.full_loop(set_alpha_to_one=__lowercase ,			beta_start=0.01		)
											__a					       =						torch.sum(torch.abs(__lowercase		)		)
											__a					       =						torch.mean(torch.abs(__lowercase		)		)

											assert abs(result_sum.item() - 149.0784		) < 1E-2
											assert abs(result_mean.item() - 0.1941		) < 1E-3





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__     =	logging.getLogger(__name__)






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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						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.
								__a       ,   __a       ,   __a					       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								__a       ,   __a       ,   __a					       =						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"""			,       _SCREAMING_SNAKE_CASE      )

				# 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.

				__a					       =						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			,       )
				__a					       =						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=_SCREAMING_SNAKE_CASE			,       )
				__a					       =						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )			,       config=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )

				# Get datasets
				__a					       =						hasattr(model.config			,       """lang2id"""      )
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir      )
				    if training_args.do_train
				    else None
				)
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       mode="""dev"""			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )
				    if training_args.do_eval
				    else None
				)

				# Data collator
				__a					       =						DataCollatorWithPadding(_SCREAMING_SNAKE_CASE			,       pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				__a					       =						Trainer(
				    model=_SCREAMING_SNAKE_CASE			,       args=_SCREAMING_SNAKE_CASE			,       train_dataset=_SCREAMING_SNAKE_CASE			,       eval_dataset=_SCREAMING_SNAKE_CASE			,       data_collator=_SCREAMING_SNAKE_CASE			,       )

				# 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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				main()


if __name__ == "__main__":
						main()
