'''simple docstring'''

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


_A			:      List[Any]					      =				logging.getLogger(__name__)







def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]						,		snake_case_       :       List[Any]      )					->     Dict:






 '''simple docstring'''

 return (preds == labels).mean()







@dataclass
class 				_lowercase     :


 '''simple docstring'''






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







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       str      	=					field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()			)}			)
 _SCREAMING_SNAKE_CASE     :       str      	=					field(metadata={"""help""": """Should contain the data files for the task."""}			)
 _SCREAMING_SNAKE_CASE     :       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."""
         )
     }  ,							)
 _SCREAMING_SNAKE_CASE     :       bool      	=					field(
     default=UpperCAmelCase__  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)







def      UpperCamelCase_						(					)					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 __lowerCAmelCase											=						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"""						,		SCREAMING_SNAKE_CASE__      )

 # Set seed
 set_seed(training_args.seed      )

 try:
  __lowerCAmelCase											=						processors[data_args.task_name]()
  __lowerCAmelCase											=						processor.get_labels()
  __lowerCAmelCase											=						len(SCREAMING_SNAKE_CASE__      )
 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.

 __lowerCAmelCase											=						AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path						,		num_labels=SCREAMING_SNAKE_CASE__						,		finetuning_task=data_args.task_name						,		cache_dir=model_args.cache_dir						,		)
 __lowerCAmelCase											=						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						,		)
 __lowerCAmelCase											=						AutoModelForMultipleChoice.from_pretrained(
     model_args.model_name_or_path						,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )						,		config=SCREAMING_SNAKE_CASE__						,		cache_dir=model_args.cache_dir						,		)

 # Get datasets
 __lowerCAmelCase											=						(
     MultipleChoiceDataset(
         data_dir=data_args.data_dir						,		tokenizer=SCREAMING_SNAKE_CASE__						,		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
 )
 __lowerCAmelCase											=						(
     MultipleChoiceDataset(
         data_dir=data_args.data_dir						,		tokenizer=SCREAMING_SNAKE_CASE__						,		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(snake_case_       :       int      ) -> Dict:
  __lowerCAmelCase											=						np.argmax(p.predictions						,		axis=1      )
  return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__						,		p.label_ids      )}

 # Data collator
 __lowerCAmelCase											=						DataCollatorWithPadding(SCREAMING_SNAKE_CASE__						,		pad_to_multiple_of=8      ) if training_args.fpaa else None

 # Initialize our Trainer
 __lowerCAmelCase											=						Trainer(
     model=SCREAMING_SNAKE_CASE__						,		args=SCREAMING_SNAKE_CASE__						,		train_dataset=SCREAMING_SNAKE_CASE__						,		eval_dataset=SCREAMING_SNAKE_CASE__						,		compute_metrics=SCREAMING_SNAKE_CASE__						,		data_collator=SCREAMING_SNAKE_CASE__						,		)

 # 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
 __lowerCAmelCase											=						{}
 if training_args.do_eval:
  logger.info("""*** Evaluate ***"""      )

  __lowerCAmelCase											=						trainer.evaluate()

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

    results.update(SCREAMING_SNAKE_CASE__      )

 return results







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     Optional[Any]:






 '''simple docstring'''

 main()


if __name__ == "__main__":
 main()








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
