

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_            = logging.getLogger(__name__)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]					, lowerCAmelCase:					Optional[int]	)				->    int:
				return (preds == labels).mean()

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







@dataclass
class 			a					:
						_lowercase        =     field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()			)}			)
						_lowercase        =     field(metadata={"help": "Should contain the data files for the task."}			)
						_lowercase        =     field(
						    default=1_2_8							,	metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={"help": "Overwrite the cached training and evaluation sets"}			)







def 							__SCREAMING_SNAKE_CASE							(			)				->    List[Any]:
				# 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    :						Tuple							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
				_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Union[str, 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:
								_UpperCAmelCase    :						Optional[int]							=      processors[data_args.task_name]()
								_UpperCAmelCase    :						List[str]							=      processor.get_labels()
								_UpperCAmelCase    :						str							=      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.

				_UpperCAmelCase    :						Tuple							=      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					, )
				_UpperCAmelCase    :						Tuple							=      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    :						int							=      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
				_UpperCAmelCase    :						List[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
				)
				_UpperCAmelCase    :						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.dev					, )
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(lowerCAmelCase:					EvalPrediction	) -> Dict:
								_UpperCAmelCase    :						int							=      np.argmax(p.predictions					, axis=1	)
								return {"acc": simple_accuracy(lowerCAmelCase					, p.label_ids	)}

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

				# Initialize our Trainer
				_UpperCAmelCase    :						Optional[Any]							=      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
				_UpperCAmelCase    :						List[str]							=      {}
				if training_args.do_eval:
								logger.info("*** Evaluate ***"	)

								_UpperCAmelCase    :						Dict							=      trainer.evaluate()

								_UpperCAmelCase    :						Union[str, 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 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]	)				->    Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()





from typing import List, Optional

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(    self  ,   A_ = None  ,   A_ = None  ,   A_ = "student_t"  ,   A_ = "nll"  ,   A_ = 1  ,   A_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

										# Transformer architecture configuration
										_UpperCAmelCase    :						Union[str, Any]							=      input_size * len(self.lags_sequence		) + self._number_of_features
										_UpperCAmelCase    :						int							=      d_model
										_UpperCAmelCase    :						Any							=      encoder_attention_heads
										_UpperCAmelCase    :						str							=      decoder_attention_heads
										_UpperCAmelCase    :						Union[str, Any]							=      encoder_ffn_dim
										_UpperCAmelCase    :						Any							=      decoder_ffn_dim
										_UpperCAmelCase    :						Any							=      encoder_layers
										_UpperCAmelCase    :						Optional[int]							=      decoder_layers

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

										super().__init__(is_encoder_decoder=A_  ,   **A_		)





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    sum(self.embedding_dimension		)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)


