






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      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							str     ,       __snake_case    :							PreTrainedTokenizer     ,       __snake_case    :							int     ,       __snake_case    :							Optional[int] = None     ,       ):


					'''simple docstring'''


					UpperCAmelCase_    :      Any            =						{}

					if train_file is not None:
										UpperCAmelCase_    :      Any            =						[train_file]
					if eval_file is not None:
										UpperCAmelCase_    :      int            =						[eval_file]
					if test_file is not None:
										UpperCAmelCase_    :      str            =						[test_file]

					UpperCAmelCase_    :      int            =						datasets.load_dataset('csv'     ,       data_files=__snake_case  )
					UpperCAmelCase_    :      List[Any]            =						list(ds[list(files.keys()  )[0]].features.keys()  )
					UpperCAmelCase_    :      List[str]            =						features_name.pop(__snake_case  )
					UpperCAmelCase_    :      Any            =						list(set(ds[list(files.keys()  )[0]][label_name]  )  )
					UpperCAmelCase_    :      List[str]            =						{label: i for i, label in enumerate(__snake_case  )}
					UpperCAmelCase_    :      Dict            =						tokenizer.model_input_names
					UpperCAmelCase_    :      Optional[int]            =						{}

					if len(__snake_case  ) == 1:
										for k in files.keys():
															UpperCAmelCase_    :      List[Any]            =						ds[k].map(
															    lambda __snake_case  : tokenizer.batch_encode_plus(
															        example[features_name[0]]     ,       truncation=__snake_case     ,       max_length=__snake_case     ,       padding='max_length'  )     ,       batched=__snake_case     ,       )
					elif len(__snake_case  ) == 2:
										for k in files.keys():
															UpperCAmelCase_    :      Union[str, Any]            =						ds[k].map(
															    lambda __snake_case  : tokenizer.batch_encode_plus(
															        (example[features_name[0]], example[features_name[1]])     ,       truncation=__snake_case     ,       max_length=__snake_case     ,       padding='max_length'     ,       )     ,       batched=__snake_case     ,       )

					def gen_train():
										for ex in transformed_ds[datasets.Split.TRAIN]:
															UpperCAmelCase_    :      Tuple            =						{k: v for k, v in ex.items() if k in input_names}
															UpperCAmelCase_    :      str            =						labelaid[ex[label_name]]
															yield (d, label)

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

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

					UpperCAmelCase_    :      str            =						(
					    tf.data.Dataset.from_generator(
					        __snake_case     ,       ({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_    :      Tuple            =						train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

					UpperCAmelCase_    :      str            =						(
					    tf.data.Dataset.from_generator(
					        __snake_case     ,       ({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_    :      int            =						val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

					UpperCAmelCase_    :      List[str]            =						(
					    tf.data.Dataset.from_generator(
					        __snake_case     ,       ({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_    :      List[Any]            =						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   lowerCamelCase      :







		'''simple docstring'''



		_snake_case    :    int       						=  field(metadata={'''help''': '''Which column contains the label'''}			)
		_snake_case    :    str       						=  field(default=_snake_case ,  metadata={'''help''': '''The path of the training file'''}			)
		_snake_case    :    Optional[str]       						=  field(default=_snake_case ,  metadata={'''help''': '''The path of the development file'''}			)
		_snake_case    :    Optional[str]       						=  field(default=_snake_case ,  metadata={'''help''': '''The path of the test file'''}			)
		_snake_case    :    int       						=  field(
		    default=1_2_8 ,  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=_snake_case ,  metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)





@dataclass
class   lowerCamelCase      :







		'''simple docstring'''



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





def      lowercase__      (     ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
					UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						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_    :      List[str]            =						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_    :      Dict            =						get_tfds(
					    train_file=data_args.train_file     ,       eval_file=data_args.dev_file     ,       test_file=data_args.test_file     ,       tokenizer=__snake_case     ,       label_column_id=data_args.label_column_id     ,       max_seq_length=data_args.max_seq_length     ,       )

					UpperCAmelCase_    :      Any            =						AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,       num_labels=len(__snake_case  )     ,       labelaid=__snake_case     ,       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_    :      Dict            =						TFAutoModelForSequenceClassification.from_pretrained(
										    model_args.model_name_or_path     ,       from_pt=bool('.bin' in model_args.model_name_or_path  )     ,       config=__snake_case     ,       cache_dir=model_args.cache_dir     ,       )

					def compute_metrics(__snake_case    :							EvalPrediction  ) -> Dict:
										UpperCAmelCase_    :      Tuple            =						np.argmax(p.predictions     ,       axis=1  )

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

					# Initialize our Trainer
					UpperCAmelCase_    :      List[str]            =						TFTrainer(
					    model=__snake_case     ,       args=__snake_case     ,       train_dataset=__snake_case     ,       eval_dataset=__snake_case     ,       compute_metrics=__snake_case     ,       )

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir  )

					# Evaluation
					UpperCAmelCase_    :      List[Any]            =						{}
					if training_args.do_eval:
										logger.info('*** Evaluate ***'  )
										UpperCAmelCase_    :      Dict            =						trainer.evaluate()
										UpperCAmelCase_    :      Dict            =						os.path.join(training_args.output_dir     ,       'eval_results.txt'  )

										with open(__snake_case     ,       '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(__snake_case  )

					return results


if __name__ == "__main__":
		main()













from math import factorial

lowerCAmelCase_    =				{str(digit): factorial(digit) for digit in range(1_0)}






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameter number must be int''' )

				if number < 0:
								raise ValueError('''Parameter number must be greater than or equal to 0''' )

				# Converts number in string to iterate on its digits and adds its factorial.
				return sum(DIGIT_FACTORIAL[digit] for digit in str(_UpperCamelCase ) )






def      lowerCamelCase_						(				_UpperCamelCase = 60 ,				_UpperCamelCase = 1_000_000 )		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase ,				_UpperCamelCase ) or not isinstance(_UpperCamelCase ,				_UpperCamelCase ):
								raise TypeError('''Parameters chain_length and number_limit must be int''' )

				if chain_length <= 0 or number_limit <= 0:
								raise ValueError(
								    '''Parameters chain_length and number_limit must be greater than 0''' )

				# the counter for the chains with the exact desired length
				snake_case_						:    Optional[Any]				=  0
				# the cached sizes of the previous chains
				snake_case_						:    dict[int, int]				=  {}

				for start_chain_element in range(1 ,				_UpperCamelCase ):
								# The temporary set will contain the elements of the chain
								snake_case_						:    List[str]				=  set()
								snake_case_						:    List[Any]				=  0

								# Stop computing the chain when you find a cached size, a repeating item or the
								# length is greater then the desired one.
								snake_case_						:    Any				=  start_chain_element
								while (
								    chain_element not in chain_sets_lengths
								    and chain_element not in chain_set
								    and chain_set_length <= chain_length
								):
												chain_set.add(_UpperCamelCase )
												chain_set_length += 1
												snake_case_						:    List[Any]				=  digit_factorial_sum(_UpperCamelCase )

								if chain_element in chain_sets_lengths:
												chain_set_length += chain_sets_lengths[chain_element]

								snake_case_						:    List[str]				=  chain_set_length

								# If chain contains the exact amount of elements increase the counter
								if chain_set_length == chain_length:
												chains_counter += 1

				return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
