


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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__ :			Dict         =logging.getLogger(__name__)







@dataclass
class   __lowercase						:





							"""simple docstring"""







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







@dataclass
class   __lowercase						:





							"""simple docstring"""







							_UpperCAmelCase						     =	field(
							    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()     )}     )
							_UpperCAmelCase						     =	field(
							    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
							_UpperCAmelCase						     =	field(
							    default=1_2_8  ,					metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }  ,					)
							_UpperCAmelCase						     =	field(
							    default=__lowerCamelCase  ,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )







def 	a__ (							A__							):
				SCREAMING_SNAKE_CASE_		:    str 		=				default_data_collator(__lowerCamelCase							)
				SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				batch.pop('pairID',			__lowerCamelCase							)
				return batch






def 	a__ (							):
				SCREAMING_SNAKE_CASE_		:    List[str] 		=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
				SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				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',			__lowerCamelCase							)

				# Set seed
				set_seed(training_args.seed							)

				try:
								SCREAMING_SNAKE_CASE_		:    str 		=				hans_tasks_num_labels[data_args.task_name]
				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.

				SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,			num_labels=__lowerCamelCase,			finetuning_task=data_args.task_name,			cache_dir=model_args.cache_dir,			)
				SCREAMING_SNAKE_CASE_		:    List[Any] 		=				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,			)
				SCREAMING_SNAKE_CASE_		:    int 		=				AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path,			from_tf=bool('.ckpt' in model_args.model_name_or_path							),			config=__lowerCamelCase,			cache_dir=model_args.cache_dir,			)

				# Get datasets
				SCREAMING_SNAKE_CASE_		:    List[Any] 		=				(
				    HansDataset(
				        data_dir=data_args.data_dir,			tokenizer=__lowerCamelCase,			task=data_args.task_name,			max_seq_length=data_args.max_seq_length,			overwrite_cache=data_args.overwrite_cache,			)
				    if training_args.do_train
				    else None
				)
				SCREAMING_SNAKE_CASE_		:    Tuple 		=				(
				    HansDataset(
				        data_dir=data_args.data_dir,			tokenizer=__lowerCamelCase,			task=data_args.task_name,			max_seq_length=data_args.max_seq_length,			overwrite_cache=data_args.overwrite_cache,			evaluate=__lowerCamelCase,			)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				SCREAMING_SNAKE_CASE_		:    int 		=				Trainer(
				    model=__lowerCamelCase,			args=__lowerCamelCase,			train_dataset=__lowerCamelCase,			eval_dataset=__lowerCamelCase,			data_collator=__lowerCamelCase,			)

				# 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
				if training_args.do_eval:
								logger.info('*** Evaluate ***'							)

								SCREAMING_SNAKE_CASE_		:    Dict 		=				trainer.predict(__lowerCamelCase							)
								SCREAMING_SNAKE_CASE_		:    List[str] 		=				output.predictions
								SCREAMING_SNAKE_CASE_		:    List[str] 		=				np.argmax(__lowerCamelCase,			axis=1							)

								SCREAMING_SNAKE_CASE_		:    Dict 		=				[ex.pairID for ex in eval_dataset]
								SCREAMING_SNAKE_CASE_		:    Dict 		=				os.path.join(training_args.output_dir,			'hans_predictions.txt'							)
								SCREAMING_SNAKE_CASE_		:    int 		=				eval_dataset.get_labels()
								if trainer.is_world_master():
												with open(__lowerCamelCase,			'w'							) as writer:
																writer.write('pairID,gold_label\n'							)
																for pid, pred in zip(__lowerCamelCase,			__lowerCamelCase							):
																				writer.write('ex' + str(__lowerCamelCase							) + ',' + label_list[int(__lowerCamelCase							)] + '\n'							)

								trainer._log(output.metrics							)






def 	a__ (							A__							):
				main()


if __name__ == "__main__":
				main()









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

										self.register_modules(unet=lowerCAmelCase__ ,	scheduler=lowerCAmelCase__					)






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





