

'''simple docstring'''


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__      				=			logging.getLogger(__name__)



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



@dataclass
class        lowercase    :
							_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=128,       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=snake_case_,       metadata={"help": "Overwrite the cached training and evaluation sets"}			)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     str:
	UpperCAmelCase_		:   Optional[int]													=       default_data_collator(_lowerCamelCase	)
	UpperCAmelCase_		:   Dict													=       batch.pop('pairID' ,_lowerCamelCase	)
	return batch







def     SCREAMING_SNAKE_CASE(	)     ->     Tuple:
	# 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.

	UpperCAmelCase_		:   List[Any]													=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
	UpperCAmelCase_		:   Dict													=       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:
		UpperCAmelCase_		:   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.

	UpperCAmelCase_		:   List[str]													=       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 ,)
	UpperCAmelCase_		:   List[str]													=       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 ,)
	UpperCAmelCase_		:   List[Any]													=       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
	UpperCAmelCase_		:   Union[str, 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
	)
	UpperCAmelCase_		:   Dict													=       (
	    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
	UpperCAmelCase_		:   List[str]													=       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 ***'	)

		UpperCAmelCase_		:   List[str]													=       trainer.predict(_lowerCamelCase	)
		UpperCAmelCase_		:   Dict													=       output.predictions
		UpperCAmelCase_		:   List[Any]													=       np.argmax(_lowerCamelCase ,axis=1	)

		UpperCAmelCase_		:   List[str]													=       [ex.pairID for ex in eval_dataset]
		UpperCAmelCase_		:   Dict													=       os.path.join(training_args.output_dir ,'hans_predictions.txt'	)
		UpperCAmelCase_		:   List[Any]													=       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     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     List[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
						main()














"""simple docstring"""
def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase     ,  ):
 lowerCamelCase__	:     Any								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     List[Any]								=		list(_lowerCamelCase							)
 lowerCamelCase__	:     int								=		len(_lowerCamelCase							)
 lowerCamelCase__	:     Optional[int]								=		len(_lowerCamelCase							)

 lowerCamelCase__	:     Tuple								=		[
     [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]
 lowerCamelCase__	:     Dict								=		[
     ['0' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
 ]

 for i in range(1     ,  len_source_seq + 1							):
  lowerCamelCase__	:     Optional[int]								=		i * delete_cost
  lowerCamelCase__	:     List[str]								=		f'''D{source_seq[i - 1]:c}'''

 for i in range(1     ,  len_destination_seq + 1							):
  lowerCamelCase__	:     int								=		i * insert_cost
  lowerCamelCase__	:     str								=		f'''I{destination_seq[i - 1]:c}'''

 for i in range(1     ,  len_source_seq + 1							):
  for j in range(1     ,  len_destination_seq + 1							):
   if source_seq[i - 1] == destination_seq[j - 1]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j - 1] + copy_cost
    lowerCamelCase__	:     str								=		f'''C{source_seq[i - 1]:c}'''
   else:
    lowerCamelCase__	:     Tuple								=		costs[i - 1][j - 1] + replace_cost
    lowerCamelCase__	:     List[str]								=		f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]							)

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    lowerCamelCase__	:     Any								=		costs[i - 1][j] + delete_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''D{source_seq[i - 1]:c}'''

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    lowerCamelCase__	:     Optional[Any]								=		costs[i][j - 1] + insert_cost
    lowerCamelCase__	:     Union[str, Any]								=		f'''I{destination_seq[j - 1]:c}'''

 return costs, ops






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase     ,  _lowerCamelCase							):
 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   lowerCamelCase__	:     List[str]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq
  elif ops[i][j][0] == "D":
   lowerCamelCase__	:     Union[str, Any]								=		assemble_transformation(_lowerCamelCase     ,  i - 1     ,  _lowerCamelCase							)
   seq.append(ops[i][j]							)
   return seq
  else:
   lowerCamelCase__	:     Tuple								=		assemble_transformation(_lowerCamelCase     ,  _lowerCamelCase     ,  j - 1							)
   seq.append(ops[i][j]							)
   return seq


if __name__ == "__main__":
      A_,			A_     :					List[str]							=     compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

      A_     :					str							=     len(operations)
      A_     :					Dict							=     len(operations[0])
      A_     :					Any							=     assemble_transformation(operations, m - 1, n - 1)

      A_     :					int							=     list("Python")
      A_     :					Optional[Any]							=     0
      A_     :					Tuple							=     0

      with open("min_cost.txt", "w") as file:
            for op in sequence:
                  print("".join(string))

                  if op[0] == "C":
                        file.write("%-16s" % "Copy %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost -= 1
                  elif op[0] == "R":
                        A_     :					str							=     op[2]

                        file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
                        file.write("\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 1
                  elif op[0] == "D":
                        string.pop(i)

                        file.write("%-16s" % "Delete %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2
                  else:
                        string.insert(i, op[1])

                        file.write("%-16s" % "Insert %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2

                  i += 1

            print("".join(string))
            print("Cost: ", cost)

            file.write("\r\nMinimum cost: " + str(cost))

