






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')

A__      :     Any       =					logging.getLogger(__name__)
@dataclass
class  _UpperCAmelCase   :

				"""simple docstring"""




				lowercase__											=			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."""
				        )
				    }						,)
				lowercase__											=			field(
				    default=A__						,metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}      )
				lowercase__											=			field(
				    default=A__						,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."""
				        )
				    }						,)
				lowercase__											=			field(
				    default=A__						,metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }						,)
				lowercase__											=			field(
				    default=A__						,metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }						,)
				lowercase__											=			field(
				    default=A__						,metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of prediction examples to this """
				            """value if set."""
				        )
				    }						,)



@dataclass
class  _UpperCAmelCase   :

				"""simple docstring"""




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






def     a     (		):






				'''simple docstring'''
				# 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__  					=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
				lowercase__					,		lowercase__					,		lowercase__  					=     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'''						,  lowerCamelCase_							)

				# 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__  					=     training_args.get_process_log_level()
				logger.setLevel(lowerCamelCase_							)
				datasets.utils.logging.set_verbosity(lowerCamelCase_							)
				transformers.utils.logging.set_verbosity(lowerCamelCase_							)
				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__  					=     None
				if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase__  					=     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__  					=     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__  					=     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__  					=     train_dataset.features['''label'''].names

				if training_args.do_eval:
								lowercase__  					=     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__  					=     eval_dataset.features['''label'''].names

				if training_args.do_predict:
								lowercase__  					=     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__  					=     predict_dataset.features['''label'''].names

				# Labels
				lowercase__  					=     len(lowerCamelCase_							)

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				lowercase__  					=     AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,  num_labels=lowerCamelCase_						,  idalabel={str(lowerCamelCase_							): label for i, label in enumerate(lowerCamelCase_							)}						,  labelaid={label: i for i, label in enumerate(lowerCamelCase_							)}						,  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__  					=     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__  					=     AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path						,  from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)						,  config=lowerCamelCase_						,  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__  					=     '''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								lowercase__  					=     False

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

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												lowercase__  					=     min(len(lowerCamelCase_							)						,  data_args.max_train_samples							)
												lowercase__  					=     train_dataset.select(range(lowerCamelCase_							)							)
								with training_args.main_process_first(desc='''train dataset map pre-processing'''							):
												lowercase__  					=     train_dataset.map(
												    lowerCamelCase_						,  batched=lowerCamelCase_						,  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(lowerCamelCase_							)							)						,  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__  					=     min(len(lowerCamelCase_							)						,  data_args.max_eval_samples							)
												lowercase__  					=     eval_dataset.select(range(lowerCamelCase_							)							)
								with training_args.main_process_first(desc='''validation dataset map pre-processing'''							):
												lowercase__  					=     eval_dataset.map(
												    lowerCamelCase_						,  batched=lowerCamelCase_						,  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__  					=     min(len(lowerCamelCase_							)						,  data_args.max_predict_samples							)
												lowercase__  					=     predict_dataset.select(range(lowerCamelCase_							)							)
								with training_args.main_process_first(desc='''prediction dataset map pre-processing'''							):
												lowercase__  					=     predict_dataset.map(
												    lowerCamelCase_						,  batched=lowerCamelCase_						,  load_from_cache_file=not data_args.overwrite_cache						,  desc='''Running tokenizer on prediction dataset'''						,  )

    # Get the metric function
				lowercase__  					=     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(lowerCamelCase_							):
								lowercase__  					=     p.predictions[0] if isinstance(p.predictions						,  lowerCamelCase_							) else p.predictions
								lowercase__  					=     np.argmax(lowerCamelCase_						,  axis=1							)
								return metric.compute(predictions=lowerCamelCase_						,  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__  					=     default_data_collator
				elif training_args.fpaa:
								lowercase__  					=     DataCollatorWithPadding(lowerCamelCase_						,  pad_to_multiple_of=8							)
				else:
								lowercase__  					=     None

				# Initialize our Trainer
				lowercase__  					=     Trainer(
				    model=lowerCamelCase_						,  args=lowerCamelCase_						,  train_dataset=train_dataset if training_args.do_train else None						,  eval_dataset=eval_dataset if training_args.do_eval else None						,  compute_metrics=lowerCamelCase_						,  tokenizer=lowerCamelCase_						,  data_collator=lowerCamelCase_						,  )

				# Training
				if training_args.do_train:
								lowercase__  					=     None
								if training_args.resume_from_checkpoint is not None:
												lowercase__  					=     training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase__  					=     last_checkpoint
								lowercase__  					=     trainer.train(resume_from_checkpoint=lowerCamelCase_							)
								lowercase__  					=     train_result.metrics
								lowercase__  					=     (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_							)
								)
								lowercase__  					=     min(lowerCamelCase_						,  len(lowerCamelCase_							)							)

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

								trainer.log_metrics('''train'''						,  lowerCamelCase_							)
								trainer.save_metrics('''train'''						,  lowerCamelCase_							)
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''							)
								lowercase__  					=     trainer.evaluate(eval_dataset=lowerCamelCase_							)

								lowercase__  					=     data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_							)
								lowercase__  					=     min(lowerCamelCase_						,  len(lowerCamelCase_							)							)

								trainer.log_metrics('''eval'''						,  lowerCamelCase_							)
								trainer.save_metrics('''eval'''						,  lowerCamelCase_							)

				# Prediction
				if training_args.do_predict:
								logger.info('''*** Predict ***'''							)
								lowercase__					,		lowercase__					,		lowercase__  					=     trainer.predict(lowerCamelCase_						,  metric_key_prefix='''predict'''							)

								lowercase__  					=     (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase_							)
								)
								lowercase__  					=     min(lowerCamelCase_						,  len(lowerCamelCase_							)							)

								trainer.log_metrics('''predict'''						,  lowerCamelCase_							)
								trainer.save_metrics('''predict'''						,  lowerCamelCase_							)

								lowercase__  					=     np.argmax(lowerCamelCase_						,  axis=1							)
								lowercase__  					=     os.path.join(training_args.output_dir						,  '''predictions.txt'''							)
								if trainer.is_world_process_zero():
												with open(lowerCamelCase_						,  '''w'''							) as writer:
																writer.write('''index\tprediction\n'''							)
																for index, item in enumerate(lowerCamelCase_							):
																				lowercase__  					=     label_list[item]
																				writer.write(F"""{index}\t{item}\n"""							)


if __name__ == "__main__":
							main()












def     a     (		lowerCamelCase_							):






				'''simple docstring'''

				if p < 2:
								raise ValueError('''p should not be less than 2!'''							)
				elif p == 2:
								return True

				lowercase__  					=     4
				lowercase__  					=     (1 << p) - 1
				for _ in range(p - 2							):
								lowercase__  					=     ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
							print(lucas_lehmer_test(7))
							print(lucas_lehmer_test(11))




