


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


snake_case__			 =						logging.getLogger(__name__)


def 				lowerCamelCase__	(      a	:   int	,  a	:   List[Any]				)    ->	Any:





			"""simple docstring"""
			return (preds == labels).mean()


@dataclass
class 					lowerCAmelCase_ :
						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 					lowerCAmelCase_ :
						lowerCamelCase_         =     field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys())})
						lowerCamelCase_         =     field(metadata={'help': 'Should contain the 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__	(      )    ->	Tuple:





			"""simple docstring"""
			# 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__ :Any						  =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
			a__  ,  a__  ,  a__ :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"	,  a				)

			# Set seed
			set_seed(training_args.seed				)

			try:
						a__ :Tuple						  =    processors[data_args.task_name]()
						a__ :str						  =    processor.get_labels()
						a__ :Any						  =    len(a				)
			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__ :str						  =    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	,  )
			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__ :str						  =    AutoModelForMultipleChoice.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
			a__ :Tuple						  =    (
			    MultipleChoiceDataset(
			        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	,  mode=Split.train	,  )
			    if training_args.do_train
			    else None
			)
			a__ :Any						  =    (
			    MultipleChoiceDataset(
			        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	,  mode=Split.dev	,  )
			    if training_args.do_eval
			    else None
			)

			def compute_metrics(a	:   EvalPrediction				) -> Dict:
						a__ :List[Any]						  =    np.argmax(p.predictions	,  axis=1				)
						return {"acc": simple_accuracy(a	,  p.label_ids				)}

			# Data collator
			a__ :str						  =    DataCollatorWithPadding(a	,  pad_to_multiple_of=8				) if training_args.fpaa else None

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

						a__ :Optional[Any]						  =    trainer.evaluate()

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

												results.update(a				)

			return results


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Tuple:





			"""simple docstring"""
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
		main()






from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'speech_to_text_2'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Dict   ,     __A						:   Union[str, Any]=10000   ,     __A						:   Tuple=6   ,     __A						:   Tuple=2048   ,     __A						:   Any=4   ,     __A						:   str=0.0   ,     __A						:   Tuple=True   ,     __A						:   List[Any]="relu"   ,     __A						:   Union[str, Any]=256   ,     __A						:   str=0.1   ,     __A						:   Optional[Any]=0.0   ,     __A						:   Tuple=0.0   ,     __A						:   Any=0.02   ,     __A						:   int=2   ,     __A						:   Union[str, Any]=True   ,     __A						:   Tuple=1   ,     __A						:   List[str]=0   ,     __A						:   List[str]=2   ,     __A						:   Union[str, Any]=1024   ,     **__A						:   Tuple   ,     )      ->List[str]:






									"""simple docstring"""






									a__ :Tuple						  =    vocab_size
									a__ :Dict						  =    d_model
									a__ :Optional[int]						  =    decoder_ffn_dim
									a__ :Optional[int]						  =    decoder_layers
									a__ :Optional[int]						  =    decoder_attention_heads
									a__ :Dict						  =    dropout
									a__ :Optional[Any]						  =    attention_dropout
									a__ :Any						  =    activation_dropout
									a__ :int						  =    activation_function
									a__ :Optional[int]						  =    init_std
									a__ :List[Any]						  =    decoder_layerdrop
									a__ :Optional[Any]						  =    use_cache
									a__ :List[str]						  =    decoder_layers
									a__ :int						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									a__ :Optional[int]						  =    max_target_positions

									super().__init__(
									    pad_token_id=__A   ,     bos_token_id=__A   ,     eos_token_id=__A   ,     decoder_start_token_id=__A   ,     **__A   ,     )


