
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


SCREAMING_SNAKE_CASE							:Dict											=       logging.getLogger(__name__)





@dataclass
class 							UpperCAmelCase   :




		'''simple docstring'''





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





@dataclass
class 							UpperCAmelCase   :




		'''simple docstring'''





		snake_case_      					=  field(
		    metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()						)}						)
		snake_case_      					=  field(
		    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
		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."
		        )
		    }    ,    )
		snake_case_      					=  field(
		    default=__lowerCamelCase    ,    metadata={"help": "Overwrite the cached training and evaluation sets"}						)





def 		UpperCAmelCase    (			a_		)	->			Dict[str, torch.Tensor]:







				"""simple docstring"""



				__A         =				default_data_collator(__lowerCamelCase		)
				__A         =				batch.pop("pairID"	,     __lowerCamelCase		)
				return batch


def 		UpperCAmelCase    (			)	->			Optional[Any]:







				"""simple docstring"""



				__A         =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
				__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"	,     __lowerCamelCase		)

				# Set seed
				set_seed(training_args.seed		)

				try:
								__A         =				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.

				__A         =				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	,     )
				__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	,     )
				__A         =				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
				__A         =				(
				    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
				)
				__A         =				(
				    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
				__A         =				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 ***"		)

								__A         =				trainer.predict(__lowerCamelCase		)
								__A         =				output.predictions
								__A         =				np.argmax(__lowerCamelCase	,     axis=1		)

								__A         =				[ex.pairID for ex in eval_dataset]
								__A         =				os.path.join(training_args.output_dir	,     "hans_predictions.txt"		)
								__A         =				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 		UpperCAmelCase    (			a_		)	->			Optional[Any]:







				"""simple docstring"""



				main()


if __name__ == "__main__":
					main()


from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
					from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
