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""")

SCREAMING_SNAKE_CASE :	Tuple							     =		logging.getLogger(__name__)
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			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."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
  _SCREAMING_SNAKE_CASE    				=			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."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of prediction examples to this """
              """value if set."""
          )
      }			,       )
@dataclass
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Train language if it is different from the evaluation language."""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default="""main"""			,       metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }			,       )
  _SCREAMING_SNAKE_CASE    				=			field(
      default=a_			,       metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}			,       )



def   __A		(			):



 """simple docstring"""


 __a       =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
 __a       ,     __a       ,     __a       =  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"							,				_A					)

 # 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()

 __a       =  training_args.get_process_log_level()
 logger.setLevel(_A					)
 datasets.utils.logging.set_verbosity(_A					)
 transformers.utils.logging.set_verbosity(_A					)
 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.
 __a       =  None
 if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
  __a       =  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:
   __a       =  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:
   __a       =  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							,				)
  __a       =  train_dataset.features["label"].names

 if training_args.do_eval:
  __a       =  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							,				)
  __a       =  eval_dataset.features["label"].names

 if training_args.do_predict:
  __a       =  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							,				)
  __a       =  predict_dataset.features["label"].names

 # Labels
 __a       =  len(_A					)

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

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

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

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

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

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

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

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

 # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"					)
  __a       =  trainer.evaluate(eval_dataset=_A					)

  __a       =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A					)
  __a       =  min(_A							,				len(_A					)					)

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

 # Prediction
 if training_args.do_predict:
  logger.info("*** Predict ***"					)
  __a       ,     __a       ,     __a       =  trainer.predict(_A							,				metric_key_prefix="predict"					)

  __a       =  (
      data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_A					)
  )
  __a       =  min(_A							,				len(_A					)					)

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

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


if __name__ == "__main__":
  main()



from __future__ import annotations



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [True] * limit
 __a       =  False
 __a       =  False
 __a       =  True

 for i in range(3							,				int(limit**0.5 + 1					)							,				2					):
  __a       =  i * 2
  while index < limit:
   __a       =  False
   __a       =  index + i

 __a       =  [2]

 for i in range(3							,				_A							,				2					):
  if is_prime[i]:
   primes.append(_A					)

 return primes



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  prime_sieve(_A					)
 __a       =  0
 __a       =  0

 for i in range(len(_A					)					):
  for j in range(i + length							,				len(_A					)					):
   __a       =  sum(primes[i:j]					)
   if sol >= ceiling:
    break

   if sol in primes:
    __a       =  j - i
    __a       =  sol

 return largest


if __name__ == "__main__":
  print(f'''{solution() = }''')


