


"""simple docstring"""



from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 					__magic_name__		:






       def __init__(   self  :    Tuple  ,     snake_case__  :    Collection[float] | None = None				):

              '''simple docstring'''






              if components is None:
                     lowercase		:str       							=				[]
              lowercase		:Dict       							=				list(snake_case__				)






       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return len(self.__components				)






       def __str__(   self  :    Any				):

              '''simple docstring'''






              return "(" + ",".join(map(snake_case__  ,     self.__components				)				) + ")"






       def __add__(   self  :    List[Any]  ,     snake_case__  :    Vector				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				len(self				)
              if size == len(snake_case__				):
                     lowercase		:List[Any]       							=				[self.__components[i] + other.component(snake_case__				) for i in range(snake_case__				)]
                     return Vector(snake_case__				)
              else:
                     raise Exception('''must have the same size'''				)






       def __sub__(   self  :    Optional[int]  ,     snake_case__  :    Vector				):

              '''simple docstring'''






              lowercase		:List[str]       							=				len(self				)
              if size == len(snake_case__				):
                     lowercase		:str       							=				[self.__components[i] - other.component(snake_case__				) for i in range(snake_case__				)]
                     return Vector(snake_case__				)
              else:  # error case
                     raise Exception('''must have the same size'''				)






       @overload
       def __mul__(   self  :    Any  ,     snake_case__  :    float				):

              '''simple docstring'''






              ...






       @overload
       def __mul__(   self  :    Optional[int]  ,     snake_case__  :    Vector				):

              '''simple docstring'''






              ...






       def __mul__(   self  :    Tuple  ,     snake_case__  :    float | Vector				):

              '''simple docstring'''






              if isinstance(snake_case__  ,     (float, int)				):
                     lowercase		:Dict       							=				[c * other for c in self.__components]
                     return Vector(snake_case__				)
              elif isinstance(snake_case__  ,     snake_case__				) and len(self				) == len(snake_case__				):
                     lowercase		:Union[str, Any]       							=				len(self				)
                     lowercase		:List[str]       							=				[self.__components[i] * other.component(snake_case__				) for i in range(snake_case__				)]
                     return sum(snake_case__				)
              else:  # error case
                     raise Exception('''invalid operand!'''				)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              return Vector(self.__components				)






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int				):

              '''simple docstring'''






              if isinstance(snake_case__  ,     snake_case__				) and -len(self.__components				) <= i < len(self.__components				):
                     return self.__components[i]
              else:
                     raise Exception('''index out of range'''				)






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    int  ,     snake_case__  :    float				):

              '''simple docstring'''






              assert -len(self.__components				) <= pos < len(self.__components				)
              lowercase		:List[Any]       							=				value






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              if len(self.__components				) == 0:
                     raise Exception('''Vector is empty'''				)
              lowercase		:Union[str, Any]       							=				[c**2 for c in self.__components]
              return math.sqrt(sum(snake_case__				)				)







       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    Vector  ,     snake_case__  :    bool = False				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				self * other
              lowercase		:List[str]       							=				self.euclidean_length() * other.euclidean_length()
              if deg:
                     return math.degrees(math.acos(num / den				)				)
              else:
                     return math.acos(num / den				)







def       lowerCamelCase					(a_						:int)     ->							Vector:
       assert isinstance(a_				,				a_)
       return Vector([0] * dimension)






def       lowerCamelCase					(a_						:int				,				a_						:int)     ->							Vector:
       assert isinstance(a_				,				a_) and (isinstance(a_				,				a_))
       lowercase		:str       							=				[0] * dimension
       lowercase		:Optional[Any]       							=				1
       return Vector(a_)






def       lowerCamelCase					(a_						:float				,				a_						:Vector				,				a_						:Vector)     ->							Vector:
       assert (
           isinstance(a_				,				a_)
           and isinstance(a_				,				a_)
           and (isinstance(a_				,				(int, float)))
       )
       return x * scalar + y






def       lowerCamelCase					(a_						:int				,				a_						:int				,				a_						:int)     ->							Vector:
       random.seed(a_)
       lowercase		:Tuple       							=				[random.randint(a_				,				a_) for _ in range(a_)]
       return Vector(a_)
class 					__magic_name__		:






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    list[list[float]]  ,     snake_case__  :    int  ,     snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:int       							=				matrix
              lowercase		:Union[str, Any]       							=				w
              lowercase		:List[Any]       							=				h






       def __str__(   self  :    int				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				''''''
              for i in range(self.__height				):
                     ans += "|"
                     for j in range(self.__width				):
                            if j < self.__width - 1:
                                   ans += str(self.__matrix[i][j]				) + ","
                            else:
                                   ans += str(self.__matrix[i][j]				) + "|\n"
              return ans






       def __add__(   self  :    Tuple  ,     snake_case__  :    Matrix				):

              '''simple docstring'''






              if self.__width == other.width() and self.__height == other.height():
                     lowercase		:List[Any]       							=				[]
                     for i in range(self.__height				):
                            lowercase		:Optional[Any]       							=				[
                                self.__matrix[i][j] + other.component(snake_case__  ,     snake_case__				)
                                for j in range(self.__width				)
                            ]
                            matrix.append(snake_case__				)
                     return Matrix(snake_case__  ,     self.__width  ,     self.__height				)
              else:
                     raise Exception('''matrix must have the same dimension!'''				)






       def __sub__(   self  :    Tuple  ,     snake_case__  :    Matrix				):

              '''simple docstring'''






              if self.__width == other.width() and self.__height == other.height():
                     lowercase		:Tuple       							=				[]
                     for i in range(self.__height				):
                            lowercase		:List[Any]       							=				[
                                self.__matrix[i][j] - other.component(snake_case__  ,     snake_case__				)
                                for j in range(self.__width				)
                            ]
                            matrix.append(snake_case__				)
                     return Matrix(snake_case__  ,     self.__width  ,     self.__height				)
              else:
                     raise Exception('''matrices must have the same dimension!'''				)






       @overload
       def __mul__(   self  :    int  ,     snake_case__  :    float				):

              '''simple docstring'''






              ...






       @overload
       def __mul__(   self  :    Optional[Any]  ,     snake_case__  :    Vector				):

              '''simple docstring'''






              ...






       def __mul__(   self  :    int  ,     snake_case__  :    float | Vector				):

              '''simple docstring'''






              if isinstance(snake_case__  ,     snake_case__				):  # matrix-vector
                     if len(snake_case__				) == self.__width:
                            lowercase		:Any       							=				zero_vector(self.__height				)
                            for i in range(self.__height				):
                                   lowercase		:Dict       							=				[
                                       self.__matrix[i][j] * other.component(snake_case__				)
                                       for j in range(self.__width				)
                                   ]
                                   ans.change_component(snake_case__  ,     sum(snake_case__				)				)
                            return ans
                     else:
                            raise Exception(
                                '''vector must have the same size as the '''
                                '''number of columns of the matrix!'''				)
              elif isinstance(snake_case__  ,     (int, float)				):  # matrix-scalar
                     lowercase		:Tuple       							=				[
                         [self.__matrix[i][j] * other for j in range(self.__width				)]
                         for i in range(self.__height				)
                     ]
                     return Matrix(snake_case__  ,     self.__width  ,     self.__height				)
              return None






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return self.__height






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              return self.__width






       def  __snake_case						(   self  :    str  ,     snake_case__  :    int  ,     snake_case__  :    int				):

              '''simple docstring'''






              if 0 <= x < self.__height and 0 <= y < self.__width:
                     return self.__matrix[x][y]
              else:
                     raise Exception('''change_component: indices out of bounds'''				)






       def  __snake_case						(   self  :    str  ,     snake_case__  :    int  ,     snake_case__  :    int  ,     snake_case__  :    float				):

              '''simple docstring'''






              if 0 <= x < self.__height and 0 <= y < self.__width:
                     lowercase		:Tuple       							=				value
              else:
                     raise Exception('''change_component: indices out of bounds'''				)






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int  ,     snake_case__  :    int				):

              '''simple docstring'''






              if self.__height != self.__width:
                     raise Exception('''Matrix is not square'''				)
              lowercase		:Dict       							=				self.__matrix[:x] + self.__matrix[x + 1 :]
              for i in range(len(snake_case__				)				):
                     lowercase		:Optional[int]       							=				minor[i][:y] + minor[i][y + 1 :]
              return Matrix(snake_case__  ,     self.__width - 1  ,     self.__height - 1				).determinant()






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    int  ,     snake_case__  :    int				):

              '''simple docstring'''






              if self.__height != self.__width:
                     raise Exception('''Matrix is not square'''				)
              if 0 <= x < self.__height and 0 <= y < self.__width:
                     return (-1) ** (x + y) * self.minor(snake_case__  ,     snake_case__				)
              else:
                     raise Exception('''Indices out of bounds'''				)







       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              if self.__height != self.__width:
                     raise Exception('''Matrix is not square'''				)
              if self.__height < 1:
                     raise Exception('''Matrix has no element'''				)
              elif self.__height == 1:
                     return self.__matrix[0][0]
              elif self.__height == 2:
                     return (
                         self.__matrix[0][0] * self.__matrix[1][1]
                         - self.__matrix[0][1] * self.__matrix[1][0]
                     )
              else:
                     lowercase		:str       							=				[
                         self.__matrix[0][y] * self.cofactor(0  ,     snake_case__				) for y in range(self.__width				)
                     ]
                     return sum(snake_case__				)







def       lowerCamelCase					(a_						:int)     ->							Matrix:
       lowercase		:list[list[float]]       							=				[[0] * n for _ in range(a_)]
       return Matrix(a_				,				a_				,				a_)






def       lowerCamelCase					(a_						:int				,				a_						:int				,				a_						:int				,				a_						:int)     ->							Matrix:
       random.seed(a_)
       lowercase		:list[list[float]]       							=				[
           [random.randint(a_				,				a_) for _ in range(a_)] for _ in range(a_)
       ]
       return Matrix(a_				,				a_				,				a_)





"""simple docstring"""



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       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:str				,				a_						:PreTrainedTokenizer				,				a_						:int				,				a_						:Optional[int] = None				,				)     ->							int:
       lowercase		:Dict       							=				{}

       if train_file is not None:
              lowercase		:Union[str, Any]       							=				[train_file]
       if eval_file is not None:
              lowercase		:str       							=				[eval_file]
       if test_file is not None:
              lowercase		:Dict       							=				[test_file]

       lowercase		:Optional[Any]       							=				datasets.load_dataset('''csv'''				,				data_files=a_)
       lowercase		:int       							=				list(ds[list(files.keys())[0]].features.keys())
       lowercase		:Dict       							=				features_name.pop(a_)
       lowercase		:str       							=				list(set(ds[list(files.keys())[0]][label_name]))
       lowercase		:List[str]       							=				{label: i for i, label in enumerate(a_)}
       lowercase		:Any       							=				tokenizer.model_input_names
       lowercase		:Optional[Any]       							=				{}

       if len(a_) == 1:
              for k in files.keys():
                     lowercase		:Dict       							=				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():
                     lowercase		:Optional[Any]       							=				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]:
                     lowercase		:Any       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:List[str]       							=				labelaid[ex[label_name]]
                     yield (d, label)

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

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

       lowercase		:List[str]       							=				(
           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:
              lowercase		:Union[str, Any]       							=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

       lowercase		:Optional[int]       							=				(
           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:
              lowercase		:List[Any]       							=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

       lowercase		:Optional[Any]       							=				(
           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:
              lowercase		:int       							=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

       return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase      		=			logging.getLogger(__name__)
@dataclass
class 					__magic_name__		:
       __A			:      int        =   field(metadata={"help": "Which column contains the label"}     )
       __A			:      str        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the training file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the development file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the test file"}     )
       __A			:      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."
               )
           }			,						)
       __A			:      bool        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}     )







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







def       lowerCamelCase					()     ->							Dict:
       # 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.
       lowercase		:Dict       							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
       lowercase							,  lowercase							,  lowercase		: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				,				)
       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.

       lowercase		:Optional[int]       							=				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				,				)

       lowercase							,  lowercase							,  lowercase							,  lowercase		:int       							=				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				,				)

       lowercase		:Optional[int]       							=				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():
              lowercase		:int       							=				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_						:EvalPrediction) -> Dict:
              lowercase		:Tuple       							=				np.argmax(p.predictions				,				axis=1)

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

       # Initialize our Trainer
       lowercase		:Any       							=				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
       lowercase		:Union[str, Any]       							=				{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***''')
              lowercase		:str       							=				trainer.evaluate()
              lowercase		:int       							=				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()

