
from __future__ import annotations

from typing import Any







class   lowerCamelCase	:





     def __init__(self   :					int       ,					_A   :					int       ,					_A   :					int       ,					_A   :					float = 0     )		->		None:
         snake_case  ,							snake_case				  =      row, column
         snake_case				  =      [[default_value for c in range(_A     )] for r in range(_A     )]





     def __str__(self   :					List[str]     )		->		str:
         snake_case				  =      f'Matrix consist of {self.row} rows and {self.column} columns\n'

         # Make string identifier
         snake_case				  =      0
         for row_vector in self.array:
             for obj in row_vector:
                 snake_case				  =      max(_A       ,					len(str(_A     )     )     )
         snake_case				  =      f'%{max_element_length}s'

         # Make string and return
         def single_line(_A   :					list[float]     ) -> str:
             nonlocal string_format_identifier
             snake_case				  =      "["
             line += ", ".join(string_format_identifier % (obj,) for obj in row_vector     )
             line += "]"
             return line

         s += "\n".join(single_line(_A     ) for row_vector in self.array     )
         return s





     def __repr__(self   :					str     )		->		str:
         return str(self     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					tuple[int, int]     )		->		bool:
         if not (isinstance(_A       ,					(list, tuple)     ) and len(_A     ) == 2):
             return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
         else:
             return True





     def __getitem__(self   :					Union[str, Any]       ,					_A   :					tuple[int, int]     )		->		Any:
         assert self.validate_indicies(_A     )
         return self.array[loc[0]][loc[1]]





     def __setitem__(self   :					Union[str, Any]       ,					_A   :					tuple[int, int]       ,					_A   :					float     )		->		None:
         assert self.validate_indicies(_A     )
         snake_case				  =      value





     def __add__(self   :					Dict       ,					_A   :					Matrix     )		->		Matrix:
         assert isinstance(_A       ,					_A     )
         assert self.row == another.row and self.column == another.column

         # Add
         snake_case				  =      Matrix(self.row       ,					self.column     )
         for r in range(self.row     ):
             for c in range(self.column     ):
                 snake_case				  =      self[r, c] + another[r, c]
         return result





     def __neg__(self   :					str     )		->		Matrix:
         snake_case				  =      Matrix(self.row       ,					self.column     )
         for r in range(self.row     ):
             for c in range(self.column     ):
                 snake_case				  =      -self[r, c]
         return result





     def __sub__(self   :					Tuple       ,					_A   :					Matrix     )		->		Matrix:
         return self + (-another)





     def __mul__(self   :					Dict       ,					_A   :					int | float | Matrix     )		->		Matrix:

         if isinstance(_A       ,					(int, float)     ):  # Scalar multiplication
             snake_case				  =      Matrix(self.row       ,					self.column     )
             for r in range(self.row     ):
                 for c in range(self.column     ):
                     snake_case				  =      self[r, c] * another
             return result
         elif isinstance(_A       ,					_A     ):  # Matrix multiplication
             assert self.column == another.row
             snake_case				  =      Matrix(self.row       ,					another.column     )
             for r in range(self.row     ):
                 for c in range(another.column     ):
                     for i in range(self.column     ):
                         result[r, c] += self[r, i] * another[i, c]
             return result
         else:
             snake_case				  =      f'Unsupported type given for another ({type(_A     )})'
             raise TypeError(_A     )





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Matrix:
         snake_case				  =      Matrix(self.column       ,					self.row     )
         for r in range(self.row     ):
             for c in range(self.column     ):
                 snake_case				  =      self[r, c]
         return result





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					Matrix       ,					_A   :					Matrix     )		->		Any:
         assert isinstance(_A       ,					_A     ) and isinstance(_A       ,					_A     )
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         snake_case				  =      v.transpose()
         snake_case				  =      (v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
             return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



   def 		lowercase_    (     )							->							None:
       """simple docstring"""

       snake_case				  =      Matrix(3				,	3				,	0	)
       for i in range(3	):
           snake_case				  =      1
       print(F'a^(-1) is {ainv}'	)
       # u, v
       snake_case				  =      Matrix(3				,	1				,	0	)
       snake_case  ,							snake_case  ,							snake_case				  =      1, 2, -3
       snake_case				  =      Matrix(3				,	1				,	0	)
       snake_case  ,							snake_case  ,							snake_case				  =      4, -2, 5
       print(F'u is {u}'	)
       print(F'v is {v}'	)
       print(F'uv^T is {u * v.transpose()}'	)
       # Sherman Morrison
       print(F'(a + uv^T)^(-1) is {ainv.sherman_morrison(A__				,	A__	)}'	)



   def 		lowercase_    (     )							->							None:
       """simple docstring"""

       import doctest

       doctest.testmod()





   testa()



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__ = None				,	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      {}

    if train_file is not None:
        snake_case				  =      [train_file]
    if eval_file is not None:
        snake_case				  =      [eval_file]
    if test_file is not None:
        snake_case				  =      [test_file]

    snake_case				  =      datasets.load_dataset("csv"				,	data_files=A__	)
    snake_case				  =      list(ds[list(files.keys()	)[0]].features.keys()	)
    snake_case				  =      features_name.pop(A__	)
    snake_case				  =      list(set(ds[list(files.keys()	)[0]][label_name]	)	)
    snake_case				  =      {label: i for i, label in enumerate(A__	)}
    snake_case				  =      tokenizer.model_input_names
    snake_case				  =      {}

    if len(A__	) == 1:
        for k in files.keys():
            snake_case				  =      ds[k].map(
                lambda A__	: tokenizer.batch_encode_plus(
                    example[features_name[0]]				,	truncation=A__				,	max_length=A__				,	padding="max_length"	)				,	batched=A__				,	)
    elif len(A__	) == 2:
        for k in files.keys():
            snake_case				  =      ds[k].map(
                lambda A__	: tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])				,	truncation=A__				,	max_length=A__				,	padding="max_length"				,	)				,	batched=A__				,	)

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            snake_case				  =      {k: v for k, v in ex.items() if k in input_names}
            snake_case				  =      labelaid[ex[label_name]]
            yield (d, label)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        snake_case				  =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        snake_case				  =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]	)	)	)

    snake_case				  =      (
        tf.data.Dataset.from_generator(
            A__				,	({k: tf.intaa for k in input_names}, tf.intaa)				,	({k: tf.TensorShape([None]	) for k in input_names}, tf.TensorShape([]	))				,	)
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        snake_case				  =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]	)	)	)

    return train_ds, val_ds, test_ds, labelaid


_A						      =				logging.getLogger(__name__)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							int     =	field(metadata={"help": "Which column contains the label"}						)
     UpperCAmelCase__       :							str     =	field(default=A_							,				metadata={"help": "The path of the training file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the development file"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(default=A_							,				metadata={"help": "The path of the test file"}						)
     UpperCAmelCase__       :							int     =	field(
         default=1_28							,				metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }							,				)
     UpperCAmelCase__       :							bool     =	field(
         default=A_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							str     =	field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
     UpperCAmelCase__       :							bool     =	field(default=A_							,				metadata={"help": "Set this flag to use fast tokenization."}						)
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__       :							Optional[str]     =	field(
         default=A_							,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,				)



def 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)	)
    snake_case  ,							snake_case  ,							snake_case				  =      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				,	)
    logger.info(
        F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1	)}, '
        F'16-bits training: {training_args.fpaa}'	)
    logger.info(F'Training/evaluation parameters {training_args}'	)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case				  =      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  ,							snake_case  ,							snake_case  ,							snake_case				  =      get_tfds(
        train_file=data_args.train_file				,	eval_file=data_args.dev_file				,	test_file=data_args.test_file				,	tokenizer=A__				,	label_column_id=data_args.label_column_id				,	max_seq_length=data_args.max_seq_length				,	)

    snake_case				  =      AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,	num_labels=len(A__	)				,	labelaid=A__				,	idalabel={id: label for label, id in labelaid.items()}				,	finetuning_task="text-classification"				,	cache_dir=model_args.cache_dir				,	)

    with training_args.strategy.scope():
        snake_case				  =      TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path				,	from_pt=bool(".bin" in model_args.model_name_or_path	)				,	config=A__				,	cache_dir=model_args.cache_dir				,	)

    def compute_metrics(A__	) -> Dict:
        snake_case				  =      np.argmax(p.predictions				,	axis=1	)

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    snake_case				  =      TFTrainer(
        model=A__				,	args=A__				,	train_dataset=A__				,	eval_dataset=A__				,	compute_metrics=A__				,	)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir	)

    # Evaluation
    snake_case				  =      {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"	)
        snake_case				  =      trainer.evaluate()
        snake_case				  =      os.path.join(training_args.output_dir				,	"eval_results.txt"	)

        with open(A__				,	"w"	) as writer:
            logger.info("***** Eval results *****"	)

            for key, value in result.items():
                logger.info(F'  {key} = {value}'	)
                writer.write(F'{key} = {value}\n'	)

            results.update(A__	)

    return results


if __name__ == "__main__":
   main()

