




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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase_		:					str          =  logging.getLogger(__name__)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return (preds == labels).mean()




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




@dataclass
class 		__A   :
						UpperCamelCase      =       field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()      )}      )
						UpperCamelCase      =       field(metadata={"""help""": """Should contain the data files for the task."""}      )
						UpperCamelCase      =       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."""
						        )
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCAmelCase_       ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )




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.

				__magic_name__						:						Union[str, Any]										=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				__magic_name__					,			__magic_name__					,			__magic_name__						:						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:
								__magic_name__						:						List[str]										=processors[data_args.task_name]()
								__magic_name__						:						str										=processor.get_labels()
								__magic_name__						:						Tuple										=len(lowerCamelCase )
				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.

				__magic_name__						:						Optional[int]										=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						,  )
				__magic_name__						:						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						,  )
				__magic_name__						:						str										=AutoModelForMultipleChoice.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
				__magic_name__						:						Optional[Any]										=(
				    MultipleChoiceDataset(
				        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						,  mode=Split.train						,  )
				    if training_args.do_train
				    else None
				)
				__magic_name__						:						Optional[int]										=(
				    MultipleChoiceDataset(
				        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						,  mode=Split.dev						,  )
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(lowerCamelCase ) -> Dict:
								__magic_name__						:						List[str]										=np.argmax(p.predictions						,  axis=1 )
								return {"acc": simple_accuracy(lowerCamelCase						,  p.label_ids )}

				# Data collator
				__magic_name__						:						Union[str, Any]										=DataCollatorWithPadding(lowerCamelCase						,  pad_to_multiple_of=8 ) if training_args.fpaa else None

				# Initialize our Trainer
				__magic_name__						:						List[str]										=Trainer(
				    model=lowerCamelCase						,  args=lowerCamelCase						,  train_dataset=lowerCamelCase						,  eval_dataset=lowerCamelCase						,  compute_metrics=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
				__magic_name__						:						Tuple										={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***""" )

								__magic_name__						:						List[Any]										=trainer.evaluate()

								__magic_name__						:						Optional[Any]										=os.path.join(training_args.output_dir						,  """eval_results.txt""" )
								if trainer.is_world_master():
												with open(lowerCamelCase						,  """w""" ) as writer:
																logger.info("""***** Eval results *****""" )
																for key, value in result.items():
																				logger.info("""  %s = %s"""						,  lowerCamelCase						,  lowerCamelCase )
																				writer.write("""%s = %s\n""" % (key, value) )

																results.update(lowerCamelCase )

				return results




def 		lowerCAmelCase_ (						lowerCamelCase ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()











def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    bool:
		__lowercase											=     len(snake_case							) + 1
		__lowercase											=     len(snake_case							) + 1

		# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
		# length i of input_string matches with prefix string of length j of
		# given pattern.
		# "dp" stands for dynamic programming.
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		# since string of zero length match pattern of zero length
		__lowercase											=     1

		# since pattern of zero length will never match with string of non-zero length
		for i in range(1						,   snake_case							):
				__lowercase											=     0

		# since string of zero length will match with pattern where there
		# is at least one * alternatively
		for j in range(1						,   snake_case							):
				__lowercase											=     dp[0][j - 2] if pattern[j - 1] == '*' else 0

		# now using bottom-up approach to find for all remaining lengths
		for i in range(1						,   snake_case							):
				for j in range(1						,   snake_case							):
						if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
								__lowercase											=     dp[i - 1][j - 1]

						elif pattern[j - 1] == "*":
								if dp[i][j - 2] == 1:
										__lowercase											=     1
								elif pattern[j - 2] in (input_string[i - 1], "."):
										__lowercase											=     dp[i - 1][j]
								else:
										__lowercase											=     0
						else:
								__lowercase											=     0

		return bool(dp[-1][-1]							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# inputing the strings
		# input_string = input("input a string :")
		# pattern = input("input a pattern :")

		SCREAMING_SNAKE_CASE_   :      Any			=   '''aab'''
		SCREAMING_SNAKE_CASE_   :      str			=   '''c*a*b'''

		# using function to check whether given string matches the given pattern
		if match_pattern(input_string, pattern):
				print(F"""{input_string} matches the given pattern {pattern}""")
		else:
				print(F"""{input_string} does not match with the given pattern {pattern}""")




