

SCREAMING_SNAKE_CASE_            = [
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.17.0.dev0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

SCREAMING_SNAKE_CASE_            = logging.getLogger(__name__)

@dataclass
class 			a					:
						_lowercase        =     field(
						    default="tab_fact"							,	metadata={"help": "The name of the dataset to use (via the datasets library)."}			)
						_lowercase        =     field(
						    default="tab_fact"							,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							,	)
						_lowercase        =     field(
						    default=1_0_2_4							,	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 preprocessed datasets or not."}			)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={
						        "help": (
						            "Whether to pad all samples to `max_seq_length`. "
						            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
						            "value if set."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
						            "value if set."
						        )
						    }							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={"help": "A csv or a json file containing the training data."}			)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={"help": "A csv or a json file containing the validation data."}			)
						_lowercase        =     field(default=UpperCAmelCase							,	metadata={"help": "A csv or a json file containing the test data."}			)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if self.dataset_name is not None:
														pass
										elif self.train_file is None or self.validation_file is None:
														raise ValueError("Need either a GLUE task, a training/validation file or a dataset name."		)
										else:
														_UpperCAmelCase    :						int							=      self.train_file.split("."		)[-1]
														assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
														_UpperCAmelCase    :						Optional[int]							=      self.validation_file.split("."		)[-1]
														assert (
														    validation_extension == train_extension
														), "`validation_file` should have the same extension (csv or json) as `train_file`."







@dataclass
class 			a					:
						_lowercase        =     field(
						    default=UpperCAmelCase							,	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"}							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}							,	)
						_lowercase        =     field(
						    default="main"							,	metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}							,	)
						_lowercase        =     field(
						    default=UpperCAmelCase							,	metadata={
						        "help": (
						            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
						            "with private models)."
						        )
						    }							,	)







def 							__SCREAMING_SNAKE_CASE							(			)				->    List[str]:
				# 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    :						List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
				if len(sys.argv	) == 2 and sys.argv[1].endswith(".json"	):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
				else:
								_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[Any]							=      parser.parse_args_into_dataclasses()

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					, datefmt="%m/%d/%Y %H:%M:%S"					, handlers=[logging.StreamHandler(sys.stdout	)]					, )

				_UpperCAmelCase    :						Tuple							=      training_args.get_process_log_level()
				logger.setLevel(lowerCAmelCase	)
				datasets.utils.logging.set_verbosity(lowerCAmelCase	)
				transformers.utils.logging.set_verbosity(lowerCAmelCase	)
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + F'distributed training: {bool(training_args.local_rank != -1	)}, 16-bits training: {training_args.fpaa}'	)
				logger.info(F'Training/evaluation parameters {training_args}'	)

				# Detecting last checkpoint.
				_UpperCAmelCase    :						List[str]							=      None
				if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase    :						Tuple							=      get_last_checkpoint(training_args.output_dir	)
								if last_checkpoint is None and len(os.listdir(training_args.output_dir	)	) > 0:
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome."	)
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."	)

    # Set seed before initializing model.
				set_seed(training_args.seed	)

				# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
				# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
				#
				# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
				#
				# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
				# single column. You can easily tweak this behavior (see below)
				#
				# In distributed training, the load_dataset function guarantee that only one local process can concurrently
				# download the dataset.
				if data_args.dataset_name is not None:
								# Downloading and loading a dataset from the hub.
								_UpperCAmelCase    :						Tuple							=      load_dataset(
								    data_args.dataset_name					, data_args.dataset_config_name					, cache_dir=model_args.cache_dir	)
				else:
								# Loading a dataset from your local files.
								# CSV/JSON training and evaluation files are needed.
								_UpperCAmelCase    :						int							=      {"train": data_args.train_file, "validation": data_args.validation_file}

								# Get the test dataset: you can provide your own CSV/JSON test file (see below)
								# when you use `do_predict` without specifying a GLUE benchmark task.
								if training_args.do_predict:
												if data_args.test_file is not None:
																_UpperCAmelCase    :						Tuple							=      data_args.train_file.split("."	)[-1]
																_UpperCAmelCase    :						Optional[Any]							=      data_args.test_file.split("."	)[-1]
																assert (
																    test_extension == train_extension
																), "`test_file` should have the same extension (csv or json) as `train_file`."
																_UpperCAmelCase    :						Union[str, Any]							=      data_args.test_file
												else:
																raise ValueError("Need either a GLUE task or a test file for `do_predict`."	)

								for key in data_files.keys():
												logger.info(F'load a local file for {key}: {data_files[key]}'	)

								if data_args.train_file.endswith(".csv"	):
												# Loading a dataset from local csv files
												_UpperCAmelCase    :						List[str]							=      load_dataset("csv"					, data_files=lowerCAmelCase					, cache_dir=model_args.cache_dir	)
								else:
												# Loading a dataset from local json files
												_UpperCAmelCase    :						Dict							=      load_dataset("json"					, data_files=lowerCAmelCase					, cache_dir=model_args.cache_dir	)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
				_UpperCAmelCase    :						List[str]							=      raw_datasets["train"].features["label"].names
				_UpperCAmelCase    :						Tuple							=      len(lowerCAmelCase	)

				# Load pretrained model and tokenizer
				#
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				_UpperCAmelCase    :						Union[str, Any]							=      AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path					, num_labels=lowerCAmelCase					, cache_dir=model_args.cache_dir					, revision=model_args.model_revision					, use_auth_token=True if model_args.use_auth_token else None					, )
				# load tapex tokenizer
				_UpperCAmelCase    :						Tuple							=      TapexTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					, cache_dir=model_args.cache_dir					, use_fast=model_args.use_fast_tokenizer					, revision=model_args.model_revision					, use_auth_token=True if model_args.use_auth_token else None					, add_prefix_space=lowerCAmelCase					, )
				_UpperCAmelCase    :						Union[str, Any]							=      BartForSequenceClassification.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					, revision=model_args.model_revision					, use_auth_token=True if model_args.use_auth_token else None					, )

				# Padding strategy
				if data_args.pad_to_max_length:
								_UpperCAmelCase    :						Tuple							=      "max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								_UpperCAmelCase    :						List[Any]							=      False

				# Some models have set the order of the labels to use, so let's make sure we do use it.
				_UpperCAmelCase    :						List[Any]							=      {"Refused": 0, "Entailed": 1}
				_UpperCAmelCase    :						Optional[int]							=      {0: "Refused", 1: "Entailed"}

				if data_args.max_seq_length > tokenizer.model_max_length:
								logger.warning(
								    F'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
								    F'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'	)
				_UpperCAmelCase    :						int							=      min(data_args.max_seq_length					, tokenizer.model_max_length	)

				def preprocess_tabfact_function(lowerCAmelCase:					str	):
								# Tokenize the texts
								def _convert_table_text_to_pandas(lowerCAmelCase:					List[Any]	):
												_UpperCAmelCase    :						Any							=      [_table_row.split("#"	) for _table_row in _table_text.strip("\n"	).split("\n"	)]
												_UpperCAmelCase    :						List[str]							=      pd.DataFrame.from_records(_table_content[1:]					, columns=_table_content[0]	)
												return _table_pd

								_UpperCAmelCase    :						Tuple							=      examples["statement"]
								_UpperCAmelCase    :						List[Any]							=      list(map(_convert_table_text_to_pandas					, examples["table_text"]	)	)
								_UpperCAmelCase    :						Dict							=      tokenizer(lowerCAmelCase					, lowerCAmelCase					, padding=lowerCAmelCase					, max_length=lowerCAmelCase					, truncation=lowerCAmelCase	)

								_UpperCAmelCase    :						List[str]							=      examples["label"]
								return result

				with training_args.main_process_first(desc="dataset map pre-processing"	):
								_UpperCAmelCase    :						List[Any]							=      raw_datasets.map(
								    lowerCAmelCase					, batched=lowerCAmelCase					, load_from_cache_file=not data_args.overwrite_cache					, desc="Running tokenizer on dataset"					, )
				if training_args.do_train:
								if "train" not in raw_datasets:
												raise ValueError("--do_train requires a train dataset"	)
								_UpperCAmelCase    :						Dict							=      raw_datasets["train"]
								if data_args.max_train_samples is not None:
												_UpperCAmelCase    :						List[Any]							=      train_dataset.select(range(data_args.max_train_samples	)	)

				if training_args.do_eval:
								if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
												raise ValueError("--do_eval requires a validation dataset"	)
								_UpperCAmelCase    :						Union[str, Any]							=      raw_datasets["validation"]
								if data_args.max_eval_samples is not None:
												_UpperCAmelCase    :						Any							=      eval_dataset.select(range(data_args.max_eval_samples	)	)

				if training_args.do_predict or data_args.test_file is not None:
								if "test" not in raw_datasets and "test_matched" not in raw_datasets:
												raise ValueError("--do_predict requires a test dataset"	)
								_UpperCAmelCase    :						Dict							=      raw_datasets["test"]
								if data_args.max_predict_samples is not None:
												_UpperCAmelCase    :						Any							=      predict_dataset.select(range(data_args.max_predict_samples	)	)

    # Log a few random samples from the training set:
				if training_args.do_train:
								for index in random.sample(range(len(lowerCAmelCase	)	)					, 3	):
												logger.info(F'Sample {index} of the training set: {train_dataset[index]}.'	)

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(lowerCAmelCase:					EvalPrediction	):
								_UpperCAmelCase    :						Optional[int]							=      p.predictions[0] if isinstance(p.predictions					, lowerCAmelCase	) else p.predictions
								_UpperCAmelCase    :						Optional[Any]							=      np.argmax(lowerCAmelCase					, axis=1	)
								return {"accuracy": (preds == p.label_ids).astype(np.floataa	).mean().item()}

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								_UpperCAmelCase    :						str							=      default_data_collator
				elif training_args.fpaa:
								_UpperCAmelCase    :						int							=      DataCollatorWithPadding(lowerCAmelCase					, pad_to_multiple_of=8	)
				else:
								_UpperCAmelCase    :						List[str]							=      None

				# Initialize our Trainer
				_UpperCAmelCase    :						List[Any]							=      Trainer(
				    model=lowerCAmelCase					, args=lowerCAmelCase					, train_dataset=train_dataset if training_args.do_train else None					, eval_dataset=eval_dataset if training_args.do_eval else None					, compute_metrics=lowerCAmelCase					, tokenizer=lowerCAmelCase					, data_collator=lowerCAmelCase					, )

				# Training
				if training_args.do_train:
								_UpperCAmelCase    :						List[Any]							=      None
								if training_args.resume_from_checkpoint is not None:
												_UpperCAmelCase    :						List[str]							=      training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_UpperCAmelCase    :						Dict							=      last_checkpoint
								_UpperCAmelCase    :						str							=      trainer.train(resume_from_checkpoint=lowerCAmelCase	)
								_UpperCAmelCase    :						Tuple							=      train_result.metrics
								_UpperCAmelCase    :						Optional[int]							=      (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase	)
								)
								_UpperCAmelCase    :						Any							=      min(lowerCAmelCase					, len(lowerCAmelCase	)	)

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"					, lowerCAmelCase	)
								trainer.save_metrics("train"					, lowerCAmelCase	)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***"	)

								_UpperCAmelCase    :						Optional[int]							=      trainer.evaluate(eval_dataset=lowerCAmelCase	)
								_UpperCAmelCase    :						Optional[Any]							=      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCAmelCase	)
								_UpperCAmelCase    :						Any							=      min(lowerCAmelCase					, len(lowerCAmelCase	)	)

								trainer.log_metrics("eval"					, lowerCAmelCase	)
								trainer.save_metrics("eval"					, lowerCAmelCase	)

				if training_args.do_predict:
								logger.info("*** Predict ***"	)

								# Removing the `label` columns because it contains -1 and Trainer won't like that.
								_UpperCAmelCase    :						int							=      predict_dataset.remove_columns("label"	)
								_UpperCAmelCase    :						Any							=      trainer.predict(lowerCAmelCase					, metric_key_prefix="predict"	).predictions
								_UpperCAmelCase    :						List[str]							=      np.argmax(lowerCAmelCase					, axis=1	)

								_UpperCAmelCase    :						int							=      os.path.join(training_args.output_dir					, "predict_results_tabfact.txt"	)
								if trainer.is_world_process_zero():
												with open(lowerCAmelCase					, "w"	) as writer:
																logger.info("***** Predict Results *****"	)
																writer.write("index\tprediction\n"	)
																for index, item in enumerate(lowerCAmelCase	):
																				_UpperCAmelCase    :						List[Any]							=      label_list[item]
																				writer.write(F'{index}\t{item}\n'	)

				_UpperCAmelCase    :						int							=      {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}

				if training_args.push_to_hub:
								trainer.push_to_hub(**lowerCAmelCase	)
				else:
								trainer.create_model_card(**lowerCAmelCase	)

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


if __name__ == "__main__":
					main()


