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


__SCREAMING_SNAKE_CASE						:    Dict     = logging.getLogger(__name__)



@dataclass
class    lowercase_    :
							_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    lowercase_    :
							_lowerCamelCase						=		field(
							    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()		)}		)
							_lowerCamelCase						=		field(
							    metadata={'help': 'The input data dir. Should contain the .tsv files (or other 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 					snake_case						(__lowercase						)       ->       List[Any]:

						'''simple docstring'''



						_snake_case						:     str				=    default_data_collator(UpperCamelCase_						)
						_snake_case						:     Union[str, Any]				=    batch.pop("pairID"					,			UpperCamelCase_						)
						return batch
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						_snake_case						:     Any				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
						_snake_case						:     Tuple				=    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"					,			UpperCamelCase_						)

						# Set seed
						set_seed(training_args.seed						)

						try:
												_snake_case						:     List[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.

						_snake_case						:     Union[str, Any]				=    AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,			num_labels=UpperCamelCase_					,			finetuning_task=data_args.task_name					,			cache_dir=model_args.cache_dir					,			)
						_snake_case						:     Union[str, 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					,			)
						_snake_case						:     Any				=    AutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path					,			from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,			config=UpperCamelCase_					,			cache_dir=model_args.cache_dir					,			)

						# Get datasets
						_snake_case						:     Optional[Any]				=    (
						    HansDataset(
						        data_dir=data_args.data_dir					,			tokenizer=UpperCamelCase_					,			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
						)
						_snake_case						:     Tuple				=    (
						    HansDataset(
						        data_dir=data_args.data_dir					,			tokenizer=UpperCamelCase_					,			task=data_args.task_name					,			max_seq_length=data_args.max_seq_length					,			overwrite_cache=data_args.overwrite_cache					,			evaluate=UpperCamelCase_					,			)
						    if training_args.do_eval
						    else None
						)

						# Initialize our Trainer
						_snake_case						:     List[Any]				=    Trainer(
						    model=UpperCamelCase_					,			args=UpperCamelCase_					,			train_dataset=UpperCamelCase_					,			eval_dataset=UpperCamelCase_					,			data_collator=UpperCamelCase_					,			)

						# 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 ***"						)

												_snake_case						:     Dict				=    trainer.predict(UpperCamelCase_						)
												_snake_case						:     str				=    output.predictions
												_snake_case						:     List[str]				=    np.argmax(UpperCamelCase_					,			axis=1						)

												_snake_case						:     str				=    [ex.pairID for ex in eval_dataset]
												_snake_case						:     Tuple				=    os.path.join(training_args.output_dir					,			"hans_predictions.txt"						)
												_snake_case						:     Dict				=    eval_dataset.get_labels()
												if trainer.is_world_master():
																		with open(UpperCamelCase_					,			"w"						) as writer:
																								writer.write("pairID,gold_label\n"						)
																								for pid, pred in zip(UpperCamelCase_					,			UpperCamelCase_						):
																														writer.write("ex" + str(UpperCamelCase_						) + "," + label_list[int(UpperCamelCase_						)] + "\n"						)

												trainer._log(output.metrics						)
def 					snake_case						(__lowercase						)       ->       Optional[Any]:

						'''simple docstring'''



						main()


if __name__ == "__main__":
				main()





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


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

_lowerCamelCase					:     List[str]					  =  {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "tvlt"



				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						Any=224						,		_UpperCAmelCase					:						Any=2048						,		_UpperCAmelCase					:						Optional[int]=128						,		_UpperCAmelCase					:						Tuple=[16, 16]						,		_UpperCAmelCase					:						Optional[int]=[16, 16]						,		_UpperCAmelCase					:						Tuple=3						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Dict=8						,		_UpperCAmelCase					:						Any=768						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						Any=12						,		_UpperCAmelCase					:						Optional[Any]=3072						,		_UpperCAmelCase					:						Tuple="gelu"						,		_UpperCAmelCase					:						List[str]=0.0						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Optional[int]=0.02						,		_UpperCAmelCase					:						int=1E-6						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						Dict=False						,		_UpperCAmelCase					:						str=16						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Optional[Any]=8						,		_UpperCAmelCase					:						Optional[Any]=2048						,		_UpperCAmelCase					:						Optional[int]=0.75						,		_UpperCAmelCase					:						List[Any]=0.15						,		_UpperCAmelCase					:						int="frame-level"						,		_UpperCAmelCase					:						Union[str, Any]=True						,		_UpperCAmelCase					:						Optional[int]=True						,		_UpperCAmelCase					:						Any="classification"						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase					)

								if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    f"got {audio_mask_type}"					)

								_lowerCAmelCase			:			Tuple        =					image_size
								_lowerCAmelCase			:			List[str]        =					spectrogram_length
								_lowerCAmelCase			:			str        =					frequency_length
								_lowerCAmelCase			:			Dict        =					image_patch_size
								_lowerCAmelCase			:			Tuple        =					audio_patch_size
								_lowerCAmelCase			:			str        =					num_image_channels
								_lowerCAmelCase			:			Dict        =					num_audio_channels
								_lowerCAmelCase			:			str        =					num_frames

								_lowerCAmelCase			:			Any        =					hidden_size
								_lowerCAmelCase			:			List[str]        =					num_hidden_layers
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			Any        =					hidden_dropout_prob
								_lowerCAmelCase			:			Union[str, Any]        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			Tuple        =					layer_norm_eps
								_lowerCAmelCase			:			str        =					qkv_bias
								_lowerCAmelCase			:			Tuple        =					use_mean_pooling

								_lowerCAmelCase			:			Union[str, Any]        =					decoder_num_attention_heads
								_lowerCAmelCase			:			int        =					decoder_hidden_size
								_lowerCAmelCase			:			List[str]        =					decoder_num_hidden_layers
								_lowerCAmelCase			:			Optional[int]        =					decoder_intermediate_size
								_lowerCAmelCase			:			Any        =					pixel_mask_ratio
								_lowerCAmelCase			:			int        =					audio_mask_ratio
								_lowerCAmelCase			:			Optional[int]        =					audio_mask_type

								_lowerCAmelCase			:			Dict        =					task_matching
								_lowerCAmelCase			:			Any        =					task_mae
								_lowerCAmelCase			:			int        =					loss_type

