


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''')

snake_case_						:			Optional[int]													=							logging.getLogger(__name__)

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

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







def 						lowerCamelCase(   ):
				# 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.

				_SCREAMING_SNAKE_CASE			  =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =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()

				_SCREAMING_SNAKE_CASE			  =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.
				_SCREAMING_SNAKE_CASE			  =None
				if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
								_SCREAMING_SNAKE_CASE			  =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:
												_SCREAMING_SNAKE_CASE			  =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:
												_SCREAMING_SNAKE_CASE			  =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	,)
								_SCREAMING_SNAKE_CASE			  =train_dataset.features['''label'''].names

				if training_args.do_eval:
								_SCREAMING_SNAKE_CASE			  =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	,)
								_SCREAMING_SNAKE_CASE			  =eval_dataset.features['''label'''].names

				if training_args.do_predict:
								_SCREAMING_SNAKE_CASE			  =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	,)
								_SCREAMING_SNAKE_CASE			  =predict_dataset.features['''label'''].names

				# Labels
				_SCREAMING_SNAKE_CASE			  =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.
				_SCREAMING_SNAKE_CASE			  =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	,)
				_SCREAMING_SNAKE_CASE			  =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	,)
				_SCREAMING_SNAKE_CASE			  =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:
								_SCREAMING_SNAKE_CASE			  ='''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								_SCREAMING_SNAKE_CASE			  =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:
												_SCREAMING_SNAKE_CASE			  =min(len(a__)	,data_args.max_train_samples)
												_SCREAMING_SNAKE_CASE			  =train_dataset.select(range(a__))
								with training_args.main_process_first(desc='''train dataset map pre-processing'''):
												_SCREAMING_SNAKE_CASE			  =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:
												_SCREAMING_SNAKE_CASE			  =min(len(a__)	,data_args.max_eval_samples)
												_SCREAMING_SNAKE_CASE			  =eval_dataset.select(range(a__))
								with training_args.main_process_first(desc='''validation dataset map pre-processing'''):
												_SCREAMING_SNAKE_CASE			  =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:
												_SCREAMING_SNAKE_CASE			  =min(len(a__)	,data_args.max_predict_samples)
												_SCREAMING_SNAKE_CASE			  =predict_dataset.select(range(a__))
								with training_args.main_process_first(desc='''prediction dataset map pre-processing'''):
												_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =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__):
								_SCREAMING_SNAKE_CASE			  =p.predictions[0] if isinstance(p.predictions	,a__) else p.predictions
								_SCREAMING_SNAKE_CASE			  =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:
								_SCREAMING_SNAKE_CASE			  =default_data_collator
				elif training_args.fpaa:
								_SCREAMING_SNAKE_CASE			  =DataCollatorWithPadding(a__	,pad_to_multiple_of=8)
				else:
								_SCREAMING_SNAKE_CASE			  =None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE			  =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:
								_SCREAMING_SNAKE_CASE			  =None
								if training_args.resume_from_checkpoint is not None:
												_SCREAMING_SNAKE_CASE			  =training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_SCREAMING_SNAKE_CASE			  =last_checkpoint
								_SCREAMING_SNAKE_CASE			  =trainer.train(resume_from_checkpoint=a__)
								_SCREAMING_SNAKE_CASE			  =train_result.metrics
								_SCREAMING_SNAKE_CASE			  =(
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(a__)
								)
								_SCREAMING_SNAKE_CASE			  =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 ***''')
								_SCREAMING_SNAKE_CASE			  =trainer.evaluate(eval_dataset=a__)

								_SCREAMING_SNAKE_CASE			  =data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a__)
								_SCREAMING_SNAKE_CASE			  =min(a__	,len(a__))

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

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

								_SCREAMING_SNAKE_CASE			  =(
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(a__)
								)
								_SCREAMING_SNAKE_CASE			  =min(a__	,len(a__))

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

								_SCREAMING_SNAKE_CASE			  =np.argmax(a__	,axis=1)
								_SCREAMING_SNAKE_CASE			  =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__):
																				_SCREAMING_SNAKE_CASE			  =label_list[item]
																				writer.write(f"{index}\t{item}\n")


if __name__ == "__main__":
							main()



snake_case_						:			dict[tuple[int, int, int], int]													=							{}







def 						lowerCamelCase(   a__	,a__	,a__):

				# if we are absent twice, or late 3 consecutive days,
				# no further prize strings are possible
				if late == 3 or absent == 2:
								return 0

				# if we have no days left, and have not failed any other rules,
				# we have a prize string
				if days == 0:
								return 1

				# No easy solution, so now we need to do the recursive calculation

				# First, check if the combination is already in the cache, and
				# if yes, return the stored value from there since we already
				# know the number of possible prize strings from this point on
				_SCREAMING_SNAKE_CASE			  =(days, absent, late)
				if key in cache:
								return cache[key]

				# now we calculate the three possible ways that can unfold from
				# this point on, depending on our attendance today

				# 1) if we are late (but not absent), the "absent" counter stays as
				# it is, but the "late" counter increases by one
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,late + 1)

				# 2) if we are absent, the "absent" counter increases by 1, and the
				# "late" counter resets to 0
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,absent + 1	,0)

				# 3) if we are on time, this resets the "late" counter and keeps the
				# absent counter
				_SCREAMING_SNAKE_CASE			  =_calculate(days - 1	,a__	,0)

				_SCREAMING_SNAKE_CASE			  =state_late + state_absent + state_ontime

				_SCREAMING_SNAKE_CASE			  =prizestrings
				return prizestrings







def 						lowerCamelCase(   a__ = 30):
				return _calculate(a__	,absent=0	,late=0)


if __name__ == "__main__":
							print(solution())