



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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__ = None					,			):

				"""simple docstring"""


				lowercase__					:   List[str]													=					{}

				if train_file is not None:
								lowercase__					:   str													=					[train_file]
				if eval_file is not None:
								lowercase__					:   int													=					[eval_file]
				if test_file is not None:
								lowercase__					:   str													=					[test_file]

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

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

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

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

				lowercase__					:   int													=					(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase__					,			({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__					:   Optional[int]													=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

				lowercase__					:   Any													=					(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase__					,			({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__					:   Tuple													=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

				lowercase__					:   Optional[int]													=					(
				    tf.data.Dataset.from_generator(
				        lowerCamelCase__					,			({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__					:   Tuple													=					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

				return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase__					  =  logging.getLogger(__name__)

@dataclass
class 				snake_case__:

					"""simple docstring"""






					lowercase_								=		field(metadata={"""help""": """Which column contains the label"""}      )
					lowercase_								=		field(default=_UpperCamelCase    ,      metadata={"""help""": """The path of the training file"""}      )
					lowercase_								=		field(default=_UpperCamelCase    ,      metadata={"""help""": """The path of the development file"""}      )
					lowercase_								=		field(default=_UpperCamelCase    ,      metadata={"""help""": """The path of the test file"""}      )
					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"""}      )






@dataclass
class 				snake_case__:

					"""simple docstring"""






					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""": """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.
					lowercase_								=		field(
					    default=_UpperCamelCase    ,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}    ,      )






def  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
				lowercase__					:   List[str]													=					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__					:   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					,			)

				lowercase__					:   Optional[int]													=					get_tfds(
				    train_file=data_args.train_file					,			eval_file=data_args.dev_file					,			test_file=data_args.test_file					,			tokenizer=lowerCamelCase__					,			label_column_id=data_args.label_column_id					,			max_seq_length=data_args.max_seq_length					,			)

				lowercase__					:   List[str]													=					AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,			num_labels=len(lowerCamelCase__						)					,			labelaid=lowerCamelCase__					,			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__					:   Union[str, Any]													=					TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path					,			from_pt=bool(".bin" in model_args.model_name_or_path						)					,			config=lowerCamelCase__					,			cache_dir=model_args.cache_dir					,			)

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

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

				# Initialize our Trainer
				lowercase__					:   List[str]													=					TFTrainer(
				    model=lowerCamelCase__					,			args=lowerCamelCase__					,			train_dataset=lowerCamelCase__					,			eval_dataset=lowerCamelCase__					,			compute_metrics=lowerCamelCase__					,			)

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

				# Evaluation
				lowercase__					:   Optional[Any]													=					{}
				if training_args.do_eval:
								logger.info("*** Evaluate ***"						)
								lowercase__					:   List[Any]													=					trainer.evaluate()
								lowercase__					:   int													=					os.path.join(training_args.output_dir					,			"eval_results.txt"						)

								with open(lowerCamelCase__					,			"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(lowerCamelCase__						)

				return results


if __name__ == "__main__":
				main()









from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class 				snake_case__:

					"""simple docstring"""






					def __init__(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Collection[float] | None = None					):
									if components is None:
													lowercase__					:   List[Any]													=					[]
									lowercase__					:   str													=					list(SCREAMING_SNAKE_CASE					)







					def __len__(							self :     Tuple					):
									return len(self.__components					)







					def __str__(							self :     int					):
									return "(" + ",".join(map(SCREAMING_SNAKE_CASE				,       self.__components					)					) + ")"







					def __add__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Vector					):
									lowercase__					:   Optional[Any]													=					len(self					)
									if size == len(SCREAMING_SNAKE_CASE					):
													lowercase__					:   int													=					[self.__components[i] + other.component(SCREAMING_SNAKE_CASE					) for i in range(SCREAMING_SNAKE_CASE					)]
													return Vector(SCREAMING_SNAKE_CASE					)
									else:
													raise Exception("must have the same size"					)







					def __sub__(							self :     Any				,       SCREAMING_SNAKE_CASE :     Vector					):
									lowercase__					:   Any													=					len(self					)
									if size == len(SCREAMING_SNAKE_CASE					):
													lowercase__					:   Any													=					[self.__components[i] - other.component(SCREAMING_SNAKE_CASE					) for i in range(SCREAMING_SNAKE_CASE					)]
													return Vector(SCREAMING_SNAKE_CASE					)
									else:  # error case
													raise Exception("must have the same size"					)







					@overload
					def __mul__(							self :     int				,       SCREAMING_SNAKE_CASE :     float					):
									...







					@overload
					def __mul__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Vector					):
									...







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







					def 			snake_case					(							self :     Any					):
									return Vector(self.__components					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     int					):
									if isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_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 :     Optional[int]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     float					):
									assert -len(self.__components					) <= pos < len(self.__components					)
									lowercase__					:   int													=					value







					def 			snake_case					(							self :     Any					):
									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(SCREAMING_SNAKE_CASE					)					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Vector				,       SCREAMING_SNAKE_CASE :     bool = False					):
									lowercase__					:   Optional[Any]													=					self * other
									lowercase__					:   Union[str, Any]													=					self.euclidean_length() * other.euclidean_length()
									if deg:
													return math.degrees(math.acos(num / den					)					)
									else:
													return math.acos(num / den					)






def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						)
				return Vector([0] * dimension						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						) and (isinstance(lowerCamelCase__					,			lowerCamelCase__						))
				lowercase__					:   Tuple													=					[0] * dimension
				lowercase__					:   Union[str, Any]													=					1
				return Vector(lowerCamelCase__						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				assert (
				    isinstance(lowerCamelCase__					,			lowerCamelCase__						)
				    and isinstance(lowerCamelCase__					,			lowerCamelCase__						)
				    and (isinstance(lowerCamelCase__					,			(int, float)						))
				)
				return x * scalar + y


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				random.seed(lowerCamelCase__						)
				lowercase__					:   Optional[Any]													=					[random.randint(lowerCamelCase__					,			lowerCamelCase__						) for _ in range(lowerCamelCase__						)]
				return Vector(lowerCamelCase__						)

class 				snake_case__:

					"""simple docstring"""






					def __init__(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     list[list[float]]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     int					):
									lowercase__					:   List[str]													=					matrix
									lowercase__					:   int													=					w
									lowercase__					:   str													=					h







					def __str__(							self :     int					):
									lowercase__					:   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 :     str				,       SCREAMING_SNAKE_CASE :     Matrix					):
									if self.__width == other.width() and self.__height == other.height():
													lowercase__					:   Union[str, Any]													=					[]
													for i in range(self.__height					):
																	lowercase__					:   int													=					[
																	    self.__matrix[i][j] + other.component(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	    for j in range(self.__width					)
																	]
																	matrix.append(SCREAMING_SNAKE_CASE					)
													return Matrix(SCREAMING_SNAKE_CASE				,       self.__width				,       self.__height					)
									else:
													raise Exception("matrix must have the same dimension!"					)







					def __sub__(							self :     Dict				,       SCREAMING_SNAKE_CASE :     Matrix					):
									if self.__width == other.width() and self.__height == other.height():
													lowercase__					:   Dict													=					[]
													for i in range(self.__height					):
																	lowercase__					:   Optional[Any]													=					[
																	    self.__matrix[i][j] - other.component(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	    for j in range(self.__width					)
																	]
																	matrix.append(SCREAMING_SNAKE_CASE					)
													return Matrix(SCREAMING_SNAKE_CASE				,       self.__width				,       self.__height					)
									else:
													raise Exception("matrices must have the same dimension!"					)







					@overload
					def __mul__(							self :     Any				,       SCREAMING_SNAKE_CASE :     float					):
									...







					@overload
					def __mul__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Vector					):
									...







					def __mul__(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     float | Vector					):
									if isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):  # matrix-vector
													if len(SCREAMING_SNAKE_CASE					) == self.__width:
																	lowercase__					:   Any													=					zero_vector(self.__height					)
																	for i in range(self.__height					):
																					lowercase__					:   Union[str, Any]													=					[
																					    self.__matrix[i][j] * other.component(SCREAMING_SNAKE_CASE					)
																					    for j in range(self.__width					)
																					]
																					ans.change_component(SCREAMING_SNAKE_CASE				,       sum(SCREAMING_SNAKE_CASE					)					)
																	return ans
													else:
																	raise Exception(
																	    "vector must have the same size as the "
																	    "number of columns of the matrix!"					)
									elif isinstance(SCREAMING_SNAKE_CASE				,       (int, float)					):  # matrix-scalar
													lowercase__					:   str													=					[
													    [self.__matrix[i][j] * other for j in range(self.__width					)]
													    for i in range(self.__height					)
													]
													return Matrix(SCREAMING_SNAKE_CASE				,       self.__width				,       self.__height					)
									return None







					def 			snake_case					(							self :     Union[str, Any]					):
									return self.__height







					def 			snake_case					(							self :     Optional[Any]					):
									return self.__width







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     int					):
									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 :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     float					):
									if 0 <= x < self.__height and 0 <= y < self.__width:
													lowercase__					:   Union[str, Any]													=					value
									else:
													raise Exception("change_component: indices out of bounds"					)







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     int					):
									if self.__height != self.__width:
													raise Exception("Matrix is not square"					)
									lowercase__					:   str													=					self.__matrix[:x] + self.__matrix[x + 1 :]
									for i in range(len(SCREAMING_SNAKE_CASE					)					):
													lowercase__					:   Optional[int]													=					minor[i][:y] + minor[i][y + 1 :]
									return Matrix(SCREAMING_SNAKE_CASE				,       self.__width - 1				,       self.__height - 1					).determinant()







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     int					):
									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(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
									else:
													raise Exception("Indices out of bounds"					)







					def 			snake_case					(							self :     List[str]					):
									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__					:   Dict													=					[
													    self.__matrix[0][y] * self.cofactor(0				,       SCREAMING_SNAKE_CASE					) for y in range(self.__width					)
													]
													return sum(SCREAMING_SNAKE_CASE					)






def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   list[list[float]]													=					[[0] * n for _ in range(lowerCamelCase__						)]
				return Matrix(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				random.seed(lowerCamelCase__						)
				lowercase__					:   list[list[float]]													=					[
				    [random.randint(lowerCamelCase__					,			lowerCamelCase__						) for _ in range(lowerCamelCase__						)] for _ in range(lowerCamelCase__						)
				]
				return Matrix(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						)




