





import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
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("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

UpperCAmelCase_							=				logging.getLogger(__name__)
@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       Optional[int]       =	field(
		    default=128				, 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=lowerCamelCase__				, metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}				)
		__a       :       bool       =	field(
		    default=lowerCamelCase__				, 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."""
		        )
		    }				, )
		__a       :       Optional[int]       =	field(
		    default=lowerCamelCase__				, metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of training examples to this """
		            """value if set."""
		        )
		    }				, )
		__a       :       Optional[int]       =	field(
		    default=lowerCamelCase__				, metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
		            """value if set."""
		        )
		    }				, )
		__a       :       Optional[int]       =	field(
		    default=lowerCamelCase__				, metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
		            """value if set."""
		        )
		    }				, )




@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       str       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
		__a       :       str       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Train language if it is different from the evaluation language."""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
		__a       :       Optional[str]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				, )
		__a       :       Optional[bool]       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}				, )
		__a       :       bool       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}				, )
		__a       :       str       =	field(
		    default="""main"""				, metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}				, )
		__a       :       bool       =	field(
		    default=lowerCamelCase__				, metadata={
		        """help""": (
		            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
		            """with private models)."""
		        )
		    }				, )
		__a       :       bool       =	field(
		    default=lowerCamelCase__				, metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}				, )





def  __magic_name__  (				)				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_	:   List[Any]  				=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
							lowercase_				,		lowercase_				,		lowercase_	:   Optional[int]  				=     parser.parse_args_into_dataclasses()

							# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
							# information sent is the one passed as arguments along with your Python/PyTorch versions.
							send_example_telemetry("""run_xnli"""				,       lowercase				)

							# 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				)]				,       )

							if training_args.should_log:
														# The default of training_args.log_level is passive, so we set log level at info here to have that default.
														transformers.utils.logging.set_verbosity_info()

							lowercase_	:   Optional[int]  				=     training_args.get_process_log_level()
							logger.setLevel(lowercase				)
							datasets.utils.logging.set_verbosity(lowercase				)
							transformers.utils.logging.set_verbosity(lowercase				)
							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_	:   int  				=     None
							if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
														lowercase_	:   Optional[int]  				=     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:
																					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				)

							# In distributed training, the load_dataset function guarantees that only one local process can concurrently
							# download the dataset.
							# Downloading and loading xnli dataset from the hub.
							if training_args.do_train:
														if model_args.train_language is None:
																					lowercase_	:   Tuple  				=     load_dataset(
																					    """xnli"""				,       model_args.language				,       split="""train"""				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
														else:
																					lowercase_	:   str  				=     load_dataset(
																					    """xnli"""				,       model_args.train_language				,       split="""train"""				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
														lowercase_	:   str  				=     train_dataset.features["""label"""].names

							if training_args.do_eval:
														lowercase_	:   Any  				=     load_dataset(
														    """xnli"""				,       model_args.language				,       split="""validation"""				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
														lowercase_	:   Dict  				=     eval_dataset.features["""label"""].names

							if training_args.do_predict:
														lowercase_	:   List[Any]  				=     load_dataset(
														    """xnli"""				,       model_args.language				,       split="""test"""				,       cache_dir=model_args.cache_dir				,       use_auth_token=True if model_args.use_auth_token else None				,       )
														lowercase_	:   Optional[int]  				=     predict_dataset.features["""label"""].names

							# Labels
							lowercase_	:   Optional[int]  				=     len(lowercase				)

							# Load pretrained model and tokenizer
							# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							lowercase_	:   str  				=     AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,       num_labels=lowercase				,       idalabel={str(lowercase				): label for i, label in enumerate(lowercase				)}				,       labelaid={label: i for i, label in enumerate(lowercase				)}				,       finetuning_task="""xnli"""				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       )
							lowercase_	:   Any  				=     AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,       do_lower_case=model_args.do_lower_case				,       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				,       )
							lowercase_	:   Optional[Any]  				=     AutoModelForSequenceClassification.from_pretrained(
							    model_args.model_name_or_path				,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)				,       config=lowercase				,       cache_dir=model_args.cache_dir				,       revision=model_args.model_revision				,       use_auth_token=True if model_args.use_auth_token else None				,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes				,       )

							# Preprocessing the datasets
							# Padding strategy
							if data_args.pad_to_max_length:
														lowercase_	:   Any  				=     """max_length"""
							else:
														# We will pad later, dynamically at batch creation, to the max sequence length in each batch
														lowercase_	:   Union[str, Any]  				=     False

							def preprocess_function(lowercase				):
														# Tokenize the texts
														return tokenizer(
														    examples["""premise"""]				,       examples["""hypothesis"""]				,       padding=lowercase				,       max_length=data_args.max_seq_length				,       truncation=lowercase				,       )

							if training_args.do_train:
														if data_args.max_train_samples is not None:
																					lowercase_	:   Tuple  				=     min(len(lowercase				)				,       data_args.max_train_samples				)
																					lowercase_	:   Optional[int]  				=     train_dataset.select(range(lowercase				)				)
														with training_args.main_process_first(desc="""train dataset map pre-processing"""				):
																					lowercase_	:   str  				=     train_dataset.map(
																					    lowercase				,       batched=lowercase				,       load_from_cache_file=not data_args.overwrite_cache				,       desc="""Running tokenizer on train dataset"""				,       )
														# Log a few random samples from the training set:
														for index in random.sample(range(len(lowercase				)				)				,       3				):
																					logger.info(f"""Sample {index} of the training set: {train_dataset[index]}."""				)

							if training_args.do_eval:
														if data_args.max_eval_samples is not None:
																					lowercase_	:   List[Any]  				=     min(len(lowercase				)				,       data_args.max_eval_samples				)
																					lowercase_	:   Optional[Any]  				=     eval_dataset.select(range(lowercase				)				)
														with training_args.main_process_first(desc="""validation dataset map pre-processing"""				):
																					lowercase_	:   Optional[int]  				=     eval_dataset.map(
																					    lowercase				,       batched=lowercase				,       load_from_cache_file=not data_args.overwrite_cache				,       desc="""Running tokenizer on validation dataset"""				,       )

							if training_args.do_predict:
														if data_args.max_predict_samples is not None:
																					lowercase_	:   Union[str, Any]  				=     min(len(lowercase				)				,       data_args.max_predict_samples				)
																					lowercase_	:   Any  				=     predict_dataset.select(range(lowercase				)				)
														with training_args.main_process_first(desc="""prediction dataset map pre-processing"""				):
																					lowercase_	:   Tuple  				=     predict_dataset.map(
																					    lowercase				,       batched=lowercase				,       load_from_cache_file=not data_args.overwrite_cache				,       desc="""Running tokenizer on prediction dataset"""				,       )

    # Get the metric function
							lowercase_	:   List[Any]  				=     evaluate.load("""xnli"""				)

							# 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(lowercase				):
														lowercase_	:   Dict  				=     p.predictions[0] if isinstance(p.predictions				,       lowercase				) else p.predictions
														lowercase_	:   Union[str, Any]  				=     np.argmax(lowercase				,       axis=1				)
														return metric.compute(predictions=lowercase				,       references=p.label_ids				)

							# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
							if data_args.pad_to_max_length:
														lowercase_	:   Dict  				=     default_data_collator
							elif training_args.fpaa:
														lowercase_	:   List[str]  				=     DataCollatorWithPadding(lowercase				,       pad_to_multiple_of=8				)
							else:
														lowercase_	:   Optional[int]  				=     None

							# Initialize our Trainer
							lowercase_	:   List[str]  				=     Trainer(
							    model=lowercase				,       args=lowercase				,       train_dataset=train_dataset if training_args.do_train else None				,       eval_dataset=eval_dataset if training_args.do_eval else None				,       compute_metrics=lowercase				,       tokenizer=lowercase				,       data_collator=lowercase				,       )

							# Training
							if training_args.do_train:
														lowercase_	:   Dict  				=     None
														if training_args.resume_from_checkpoint is not None:
																					lowercase_	:   Dict  				=     training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					lowercase_	:   Union[str, Any]  				=     last_checkpoint
														lowercase_	:   Union[str, Any]  				=     trainer.train(resume_from_checkpoint=lowercase				)
														lowercase_	:   Optional[int]  				=     train_result.metrics
														lowercase_	:   List[str]  				=     (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase				)
														)
														lowercase_	:   List[Any]  				=     min(lowercase				,       len(lowercase				)				)

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

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

							# Evaluation
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""				)
														lowercase_	:   Dict  				=     trainer.evaluate(eval_dataset=lowercase				)

														lowercase_	:   List[str]  				=     data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase				)
														lowercase_	:   Optional[Any]  				=     min(lowercase				,       len(lowercase				)				)

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

							# Prediction
							if training_args.do_predict:
														logger.info("""*** Predict ***"""				)
														lowercase_				,		lowercase_				,		lowercase_	:   Any  				=     trainer.predict(lowercase				,       metric_key_prefix="""predict"""				)

														lowercase_	:   List[Any]  				=     (
														    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase				)
														)
														lowercase_	:   List[str]  				=     min(lowercase				,       len(lowercase				)				)

														trainer.log_metrics("""predict"""				,       lowercase				)
														trainer.save_metrics("""predict"""				,       lowercase				)

														lowercase_	:   Optional[Any]  				=     np.argmax(lowercase				,       axis=1				)
														lowercase_	:   Optional[Any]  				=     os.path.join(training_args.output_dir				,       """predictions.txt"""				)
														if trainer.is_world_process_zero():
																					with open(lowercase				,       """w"""				) as writer:
																												writer.write("""index\tprediction\n"""				)
																												for index, item in enumerate(lowercase				):
																																			lowercase_	:   Tuple  				=     label_list[item]
																																			writer.write(f"""{index}\t{item}\n"""				)


if __name__ == "__main__":
							main()






def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if n_term == "":
														return []
							lowercase_	:   list  				=     []
							for temp in range(int(lowercase				)				):
														series.append(f"""1/{temp + 1}""" if series else """1"""				)
							return series


if __name__ == "__main__":
							UpperCAmelCase_							=				input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))