





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     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str   ,			a__   :PreTrainedTokenizer   ,			a__   :int   ,			a__   :Optional[int] = None   ,			):






							"""simple docstring"""





							UpperCamelCase__				     =			{}

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

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

							if len(a__       ) == 1:
														for k in files.keys():
																					UpperCamelCase__				     =			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():
																					UpperCamelCase__				     =			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]:
																					UpperCamelCase__				     =			{k: v for k, v in ex.items() if k in input_names}
																					UpperCamelCase__				     =			labelaid[ex[label_name]]
																					yield (d, label)

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

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

							UpperCamelCase__				     =			(
							    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:
														UpperCamelCase__				     =			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

							UpperCamelCase__				     =			(
							    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:
														UpperCamelCase__				     =			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

							UpperCamelCase__				     =			(
							    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:
														UpperCamelCase__				     =			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        __SCREAMING_SNAKE_CASE					:
							snake_case     :      int						     =       field(metadata={"""help""": """Which column contains the label"""}       )
							snake_case     :      str						     =       field(default=_a   ,		metadata={"""help""": """The path of the training file"""}       )
							snake_case     :      Optional[str]						     =       field(default=_a   ,		metadata={"""help""": """The path of the development file"""}       )
							snake_case     :      Optional[str]						     =       field(default=_a   ,		metadata={"""help""": """The path of the test file"""}       )
							snake_case     :      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."""
							        )
							    }   ,		)
							snake_case     :      bool						     =       field(
							    default=_a   ,		metadata={"""help""": """Overwrite the cached training and evaluation sets"""}       )






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






def     _UpperCamelCase     ():






							"""simple docstring"""





							UpperCamelCase__				     =			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
							UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			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.

							UpperCamelCase__				     =			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   ,			)

							UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			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   ,			)

							UpperCamelCase__				     =			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():
														UpperCamelCase__				     =			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:
														UpperCamelCase__				     =			np.argmax(p.predictions   ,			axis=1       )

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

							# Initialize our Trainer
							UpperCamelCase__				     =			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
							UpperCamelCase__				     =			{}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""       )
														UpperCamelCase__				     =			trainer.evaluate()
														UpperCamelCase__				     =			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()











from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403





def     _UpperCamelCase     (a__   :str   ,			a__   :complex   ,			a__   :str = "x"   ,			a__   :float = 10**-10   ,			a__   :int = 1   ,			):






							"""simple docstring"""





							UpperCamelCase__				     =			symbols(a__       )
							UpperCamelCase__				     =			lambdify(a__   ,			a__       )
							UpperCamelCase__				     =			lambdify(a__   ,			diff(a__   ,			a__       )       )

							UpperCamelCase__				     =			starting_point

							while True:
														if diff_function(a__       ) != 0:
																					UpperCamelCase__				     =			prev_guess - multiplicity * func(a__       ) / diff_function(
																					    a__       )
														else:
																					raise ZeroDivisionError("""Could not find root"""       ) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess       ) < precision:
																					return next_guess

														UpperCamelCase__				     =			next_guess


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")

					# Find root of polynomial
					# Find fourth Root of 5
					print(f"""The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}""")

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    f"""{newton_raphson('log(y) - 1', 2, variable='y')}""",
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    f"""{newton_raphson('exp(x) - 1', 10, precision=0.0_05)}""",
					)

					# Find root of cos(x)
					print(f"""The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}""")




