

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


a_         =      logging.getLogger(__name__)






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







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






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







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

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    default_data_collator(_a)
	SCREAMING_SNAKE_CASE				: Tuple			       =    batch.pop("pairID"							,     _a)
	return batch

def 	lowerCamelCase__	(   ):
	# 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.

	SCREAMING_SNAKE_CASE				: Tuple			       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
	SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    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"							,     _a)

	# Set seed
	set_seed(training_args.seed)

	try:
		SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    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				: Any			       =    AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,     num_labels=_a							,     finetuning_task=data_args.task_name							,     cache_dir=model_args.cache_dir							,     )
	SCREAMING_SNAKE_CASE				: 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							,     )
	SCREAMING_SNAKE_CASE				: List[Any]			       =    AutoModelForSequenceClassification.from_pretrained(
	    model_args.model_name_or_path							,     from_tf=bool(".ckpt" in model_args.model_name_or_path)							,     config=_a							,     cache_dir=model_args.cache_dir							,     )

	# Get datasets
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    (
	    HansDataset(
	        data_dir=data_args.data_dir							,     tokenizer=_a							,     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				: str			       =    (
	    HansDataset(
	        data_dir=data_args.data_dir							,     tokenizer=_a							,     task=data_args.task_name							,     max_seq_length=data_args.max_seq_length							,     overwrite_cache=data_args.overwrite_cache							,     evaluate=_a							,     )
	    if training_args.do_eval
	    else None
	)

	# Initialize our Trainer
	SCREAMING_SNAKE_CASE				: Any			       =    Trainer(
	    model=_a							,     args=_a							,     train_dataset=_a							,     eval_dataset=_a							,     data_collator=_a							,     )

	# 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(_a)
		SCREAMING_SNAKE_CASE				: Tuple			       =    output.predictions
		SCREAMING_SNAKE_CASE				: List[Any]			       =    np.argmax(_a							,     axis=1)

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

		trainer._log(output.metrics)

def 	lowerCamelCase__	(   _a):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
			main()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			a_         =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)