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


UpperCamelCase      			=     logging.getLogger(__name__)


@dataclass
class _a       :

	'''simple docstring'''



	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_   :      Optional[str]       =	field(
	    default=lowerCAmelCase__   ,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   ,							)


@dataclass
class _a       :

	'''simple docstring'''



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



def    lowerCamelCase_   (  _lowercase    )							->	Dict[str, torch.Tensor]:
		__A							:   Any	  =				default_data_collator(_lowercase    )
		__A							:   Optional[Any]	  =				batch.pop("pairID" ,     _lowercase    )
		return batch



def    lowerCamelCase_   (  )							->	Optional[int]:
		# 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.

		__A							:   Tuple	  =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		__A  ,       __A  ,       __A							:   List[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" ,     _lowercase    )

		# Set seed
		set_seed(training_args.seed    )

		try:
				__A							:   Tuple	  =				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							:   Any	  =				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 ,     )
		__A							:   Dict	  =				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							:   List[Any]	  =				AutoModelForSequenceClassification.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
		__A							:   Union[str, Any]	  =				(
		    HansDataset(
		        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 ,     )
		    if training_args.do_train
		    else None
		)
		__A							:   Any	  =				(
		    HansDataset(
		        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 ,     evaluate=_lowercase ,     )
		    if training_args.do_eval
		    else None
		)

		# Initialize our Trainer
		__A							:   str	  =				Trainer(
		    model=_lowercase ,     args=_lowercase ,     train_dataset=_lowercase ,     eval_dataset=_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
		if training_args.do_eval:
				logger.info("*** Evaluate ***"    )

				__A							:   Optional[Any]	  =				trainer.predict(_lowercase    )
				__A							:   Any	  =				output.predictions
				__A							:   Tuple	  =				np.argmax(_lowercase ,     axis=1    )

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

				trainer._log(output.metrics    )



def    lowerCamelCase_   (  _lowercase    )							->	int:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
					main()






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





