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


SCREAMING_SNAKE_CASE		:     int         =	logging.getLogger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Dict			):
						return (preds == labels).mean()

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


@dataclass
class        UpperCamelCase						:
						a__		:str      =			field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()				)}				)
						a__		:str      =			field(metadata={'''help''': '''Should contain the data files for the task.'''}				)
						a__		: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.'''
						        )
						    }	,	)
						a__		:bool      =			field(
						    default=__a	,	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.

						UpperCamelCase_		:							str  =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
						UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							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"""		, _SCREAMING_SNAKE_CASE			)

						# Set seed
						set_seed(training_args.seed			)

						try:
												UpperCamelCase_		:							List[str]  =		processors[data_args.task_name]()
												UpperCamelCase_		:							Optional[int]  =		processor.get_labels()
												UpperCamelCase_		:							int  =		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.

						UpperCamelCase_		:							Tuple  =		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		, )
						UpperCamelCase_		:							List[Any]  =		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_		:							Optional[Any]  =		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
						UpperCamelCase_		:							Dict  =		(
						    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
						)
						UpperCamelCase_		:							int  =		(
						    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(_SCREAMING_SNAKE_CASE     :				EvalPrediction			) -> Dict:
												UpperCamelCase_		:							List[str]  =		np.argmax(p.predictions		, axis=1			)
												return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE		, p.label_ids			)}

						# Data collator
						UpperCamelCase_		:							Any  =		DataCollatorWithPadding(_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=8			) if training_args.fpaa else None

						# Initialize our Trainer
						UpperCamelCase_		:							Dict  =		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
						UpperCamelCase_		:							int  =		{}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""			)

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

												UpperCamelCase_		:							Optional[int]  =		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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Dict			):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
	main()







import math
from typing import Optional

import numpy as np

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


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:     Tuple         =	{
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class        UpperCamelCase						(				__a				):
						a__		:int      =			'''encodec'''





						def __init__(self			,     __UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]			,     __UpperCamelCase=24_000			,     __UpperCamelCase=1			,     __UpperCamelCase=False			,     __UpperCamelCase=None			,     __UpperCamelCase=None			,     __UpperCamelCase=128			,     __UpperCamelCase=32			,     __UpperCamelCase=1			,     __UpperCamelCase=[8, 5, 4, 2]			,     __UpperCamelCase="weight_norm"			,     __UpperCamelCase=7			,     __UpperCamelCase=7			,     __UpperCamelCase=3			,     __UpperCamelCase=2			,     __UpperCamelCase=True			,     __UpperCamelCase="reflect"			,     __UpperCamelCase=2			,     __UpperCamelCase=2			,     __UpperCamelCase=1.0			,     __UpperCamelCase=1_024			,     __UpperCamelCase=None			,     __UpperCamelCase=True			,     **__UpperCamelCase			,     )				->				Dict:
												UpperCamelCase_		:							str  =		target_bandwidths
												UpperCamelCase_		:							Tuple  =		sampling_rate
												UpperCamelCase_		:							str  =		audio_channels
												UpperCamelCase_		:							Any  =		normalize
												UpperCamelCase_		:							Union[str, Any]  =		chunk_length_s
												UpperCamelCase_		:							Optional[Any]  =		overlap
												UpperCamelCase_		:							Any  =		hidden_size
												UpperCamelCase_		:							Dict  =		num_filters
												UpperCamelCase_		:							List[str]  =		num_residual_layers
												UpperCamelCase_		:							Optional[int]  =		upsampling_ratios
												UpperCamelCase_		:							str  =		norm_type
												UpperCamelCase_		:							str  =		kernel_size
												UpperCamelCase_		:							Tuple  =		last_kernel_size
												UpperCamelCase_		:							int  =		residual_kernel_size
												UpperCamelCase_		:							str  =		dilation_growth_rate
												UpperCamelCase_		:							str  =		use_causal_conv
												UpperCamelCase_		:							str  =		pad_mode
												UpperCamelCase_		:							Union[str, Any]  =		compress
												UpperCamelCase_		:							Dict  =		num_lstm_layers
												UpperCamelCase_		:							Optional[int]  =		trim_right_ratio
												UpperCamelCase_		:							int  =		codebook_size
												UpperCamelCase_		:							str  =		codebook_dim if codebook_dim is not None else hidden_size
												UpperCamelCase_		:							Optional[int]  =		use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																		raise ValueError(
																		    f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'''						)

												super().__init__(**__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate						)





						@property
						def        A_							(self						)				->				Optional[int]:
												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1			,     int((1.0 - self.overlap) * self.chunk_length						)						)





						@property
						def        A_							(self						)				->				int:
												UpperCamelCase_		:							int  =		np.prod(self.upsampling_ratios						)
												return math.ceil(self.sampling_rate / hop_length						)





						@property
						def        A_							(self						)				->				int:
												return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)






