"""simple docstring"""


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")

lowerCAmelCase_:					int     =					logging.getLogger(__name__)






@dataclass
class    a__    :
   snake_case_							=      field(
       default="tab_fact"					,					metadata={"help": "The name of the dataset to use (via the datasets library)."}	)
   snake_case_							=      field(
       default="tab_fact"					,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}					,					)
   snake_case_							=      field(
       default=1024					,					metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }					,					)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "Overwrite the cached preprocessed datasets or not."}	)
   snake_case_							=      field(
       default=_a					,					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."
           )
       }					,					)
   snake_case_							=      field(
       default=_a					,					metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }					,					)
   snake_case_							=      field(
       default=_a					,					metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }					,					)
   snake_case_							=      field(
       default=_a					,					metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of prediction examples to this "
               "value if set."
           )
       }					,					)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "A csv or a json file containing the training data."}	)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "A csv or a json file containing the validation data."}	)
   snake_case_							=      field(default=_a					,					metadata={"help": "A csv or a json file containing the test data."}	)


   def 	snake_case__	(     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:
             lowercase__				 =							self.train_file.split("."  )[-1]
             assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
             lowercase__				 =							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__    :
   snake_case_							=      field(
       default=_a					,					metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,					)
   snake_case_							=      field(
       default=_a					,					metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}					,					)
   snake_case_							=      field(
       default="main"					,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}					,					)
   snake_case_							=      field(
       default=_a					,					metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }					,					)







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							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.
          lowercase__ ,						lowercase__ ,						lowercase__				 =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
     else:
          lowercase__ ,						lowercase__ ,						lowercase__				 =							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      )]    ,				)

     lowercase__				 =							training_args.get_process_log_level()
     logger.setLevel(A      )
     datasets.utils.logging.set_verbosity(A      )
     transformers.utils.logging.set_verbosity(A      )
     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.
     lowercase__				 =							None
     if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
          lowercase__				 =							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.
          lowercase__				 =							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.
          lowercase__				 =							{"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:
                    lowercase__				 =							data_args.train_file.split("."      )[-1]
                    lowercase__				 =							data_args.test_file.split("."      )[-1]
                    assert (
                        test_extension == train_extension
                    ), "`test_file` should have the same extension (csv or json) as `train_file`."
                    lowercase__				 =							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
               lowercase__				 =							load_dataset("csv"    ,				data_files=A    ,				cache_dir=model_args.cache_dir      )
          else:
               # Loading a dataset from local json files
               lowercase__				 =							load_dataset("json"    ,				data_files=A    ,				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
     lowercase__				 =							raw_datasets["train"].features["label"].names
     lowercase__				 =							len(A      )

     # Load pretrained model and tokenizer
     #
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     lowercase__				 =							AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,				num_labels=A    ,				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
     lowercase__				 =							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=A    ,				)
     lowercase__				 =							BartForSequenceClassification.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    ,				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:
          lowercase__				 =							"max_length"
     else:
          # We will pad later, dynamically at batch creation, to the max sequence length in each batch
          lowercase__				 =							False

     # Some models have set the order of the labels to use, so let's make sure we do use it.
     lowercase__				 =							{"Refused": 0, "Entailed": 1}
     lowercase__				 =							{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}.'''      )
     lowercase__				 =							min(data_args.max_seq_length    ,				tokenizer.model_max_length      )

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

          lowercase__				 =							examples["statement"]
          lowercase__				 =							list(map(_convert_table_text_to_pandas    ,				examples["table_text"]      )      )
          lowercase__				 =							tokenizer(A    ,				A    ,				padding=A    ,				max_length=A    ,				truncation=A      )

          lowercase__				 =							examples["label"]
          return result

     with training_args.main_process_first(desc="dataset map pre-processing"      ):
          lowercase__				 =							raw_datasets.map(
              A    ,				batched=A    ,				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"      )
          lowercase__				 =							raw_datasets["train"]
          if data_args.max_train_samples is not None:
               lowercase__				 =							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"      )
          lowercase__				 =							raw_datasets["validation"]
          if data_args.max_eval_samples is not None:
               lowercase__				 =							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"      )
          lowercase__				 =							raw_datasets["test"]
          if data_args.max_predict_samples is not None:
               lowercase__				 =							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(A      )      )    ,				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(A      ):
          lowercase__				 =							p.predictions[0] if isinstance(p.predictions    ,				A      ) else p.predictions
          lowercase__				 =							np.argmax(A    ,				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:
          lowercase__				 =							default_data_collator
     elif training_args.fpaa:
          lowercase__				 =							DataCollatorWithPadding(A    ,				pad_to_multiple_of=8      )
     else:
          lowercase__				 =							None

     # Initialize our Trainer
     lowercase__				 =							Trainer(
         model=A    ,				args=A    ,				train_dataset=train_dataset if training_args.do_train else None    ,				eval_dataset=eval_dataset if training_args.do_eval else None    ,				compute_metrics=A    ,				tokenizer=A    ,				data_collator=A    ,				)

     # Training
     if training_args.do_train:
          lowercase__				 =							None
          if training_args.resume_from_checkpoint is not None:
               lowercase__				 =							training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               lowercase__				 =							last_checkpoint
          lowercase__				 =							trainer.train(resume_from_checkpoint=A      )
          lowercase__				 =							train_result.metrics
          lowercase__				 =							(
              data_args.max_train_samples if data_args.max_train_samples is not None else len(A      )
          )
          lowercase__				 =							min(A    ,				len(A      )      )

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

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

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

          lowercase__				 =							trainer.evaluate(eval_dataset=A      )
          lowercase__				 =							data_args.max_eval_samples if data_args.max_eval_samples is not None else len(A      )
          lowercase__				 =							min(A    ,				len(A      )      )

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

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

          # Removing the `label` columns because it contains -1 and Trainer won't like that.
          lowercase__				 =							predict_dataset.remove_columns("label"      )
          lowercase__				 =							trainer.predict(A    ,				metric_key_prefix="predict"      ).predictions
          lowercase__				 =							np.argmax(A    ,				axis=1      )

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

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

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







def 					__a   (		A      ):






     '''simple docstring'''



     main()


if __name__ == "__main__":
 main()





"""simple docstring"""


from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


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






class    a__    (   _a	):
   snake_case_							=      ["audio_values", "audio_mask"]


   def __init__(     self,				_UpperCAmelCase=2048,				_UpperCAmelCase=1,				_UpperCAmelCase=[16, 16],				_UpperCAmelCase=128,				_UpperCAmelCase=4_4100,				_UpperCAmelCase=86,				_UpperCAmelCase=2048,				_UpperCAmelCase=0.0,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            feature_size=_UpperCAmelCase,				sampling_rate=_UpperCAmelCase,				padding_value=_UpperCAmelCase,				**_UpperCAmelCase,				)

        lowercase__				 =							spectrogram_length
        lowercase__				 =							num_channels
        lowercase__				 =							patch_size
        lowercase__				 =							feature_size // self.patch_size[1]
        lowercase__				 =							n_fft
        lowercase__				 =							sampling_rate // hop_length_to_sampling_rate
        lowercase__				 =							sampling_rate
        lowercase__				 =							padding_value
        lowercase__				 =							mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2,				num_mel_filters=_UpperCAmelCase,				min_frequency=0.0,				max_frequency=22_050.0,				sampling_rate=_UpperCAmelCase,				norm="slaney",				mel_scale="slaney",				).T


   def 	snake_case__	(     self,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							spectrogram(
            _UpperCAmelCase,				window_function(self.n_fft,				"hann"  ),				frame_length=self.n_fft,				hop_length=self.hop_length,				power=2.0,				mel_filters=self.mel_filters.T,				log_mel="dB",				db_range=80.0,				)
        lowercase__				 =							log_spec[:, :-1]
        lowercase__				 =							log_spec - 20.0
        lowercase__				 =							np.clip(log_spec / 40.0,				-2.0,				0.0  ) + 1.0
        return log_spec


   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = True,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				**_UpperCAmelCase,				):
        '''simple docstring'''



        if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      "This feature extractor is set to support sampling rate"
                      F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
                      F''' with {self.sampling_rate} and not {sampling_rate}.'''  )
        else:
             logger.warning(
                 "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                 "Failing to do so can result in silent errors that might be hard to debug."  )

        lowercase__				 =							isinstance(_UpperCAmelCase,				np.ndarray  ) and len(raw_speech.shape  ) > 1
        if is_batched_numpy and len(raw_speech.shape  ) > 2:
             raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''  )
        lowercase__				 =							is_batched_numpy or (
            isinstance(_UpperCAmelCase,				(list, tuple)  ) and (isinstance(raw_speech[0],				(np.ndarray, tuple, list)  ))
        )
        if is_batched:
             lowercase__				 =							[np.asarray([speech],				dtype=np.floataa  ).T for speech in raw_speech]
        elif not is_batched and not isinstance(_UpperCAmelCase,				np.ndarray  ):
             lowercase__				 =							np.asarray(_UpperCAmelCase,				dtype=np.floataa  )
        elif isinstance(_UpperCAmelCase,				np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
             lowercase__				 =							raw_speech.astype(np.floataa  )
        # always return batch
        if not is_batched:
             lowercase__				 =							[np.asarray([raw_speech]  ).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        lowercase__				 =							[
            self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0],				_UpperCAmelCase  ):
             lowercase__				 =							[np.asarray(_UpperCAmelCase,				dtype=np.floataa  ) for feature in audio_features]

        # Create audio attention mask
        lowercase__				 =							max(
            [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
        if return_attention_mask:
             lowercase__				 =							[
                 (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowercase__				 =							np.array(_UpperCAmelCase  ).astype(np.floataa  )

        # convert into correct format for padding
        lowercase__				 =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        lowercase__				 =							np.ones([len(_UpperCAmelCase  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
        lowercase__				 =							padded_audio_features * self.padding_value
        for i in range(len(_UpperCAmelCase  )  ):
             lowercase__				 =							audio_features[i]
             lowercase__				 =							feature

        # return as BatchFeature
        if return_attention_mask:
             lowercase__				 =							{"audio_values": padded_audio_features, "audio_mask": audio_mask}
        else:
             lowercase__				 =							{"audio_values": padded_audio_features}

        lowercase__				 =							BatchFeature(data=_UpperCAmelCase,				tensor_type=_UpperCAmelCase  )
        return encoded_inputs




