






'''simple docstring'''







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

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


UpperCamelCase    :      List[str]  			=		logging.getLogger(__name__)




@dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





		_UpperCamelCase				  =					field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
		_UpperCamelCase				  =					field(
		    default=a   ,metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
		_UpperCamelCase				  =					field(
		    default=a   ,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
		_UpperCamelCase				  =					field(
		    default=a   ,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   ,)
		_UpperCamelCase				  =					field(default=a   ,metadata={'help': 'Whether tp freeze the encoder.'}			)
		_UpperCamelCase				  =					field(default=a   ,metadata={'help': 'Whether  to freeze the embeddings.'}			)




@dataclass
class 		UpperCamelCase__    :




		'''simple docstring'''





		_UpperCamelCase				  =					field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
		_UpperCamelCase				  =					field(
		    default='summarization'   ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}   ,)
		_UpperCamelCase				  =					field(
		    default=1024   ,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=128   ,metadata={
		        'help': (
		            'The maximum total sequence length for target text after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }   ,)
		_UpperCamelCase				  =					field(
		    default=142   ,metadata={
		        'help': (
		            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded. '
		            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
		            'during ``evaluate`` and ``predict``.'
		        )
		    }   ,)
		_UpperCamelCase				  =					field(
		    default=142   ,metadata={
		        'help': (
		            'The maximum total sequence length for test target text after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }   ,)
		_UpperCamelCase				  =					field(default=-1   ,metadata={'help': '# training examples. -1 means use all.'}			)
		_UpperCamelCase				  =					field(default=-1   ,metadata={'help': '# validation examples. -1 means use all.'}			)
		_UpperCamelCase				  =					field(default=-1   ,metadata={'help': '# test examples. -1 means use all.'}			)
		_UpperCamelCase				  =					field(default=a   ,metadata={'help': 'Source language id for translation.'}			)
		_UpperCamelCase				  =					field(default=a   ,metadata={'help': 'Target language id for translation.'}			)
		_UpperCamelCase				  =					field(default=a   ,metadata={'help': '# num_beams to use for evaluation.'}			)
		_UpperCamelCase				  =					field(
		    default=a   ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}   ,)



def 						A__			(				__lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Tuple						):
						logger.info(F'''***** {split} metrics *****'''						)
						for key in sorted(metrics.keys()						):
												logger.info(F'''  {key} = {metrics[key]}'''						)
						save_json(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    F'''{split}_results.json'''						)						)
def 						A__			(				):
						# 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.

						lowerCamelCase__         =  HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)						)

						if len(sys.argv						) == 2 and sys.argv[1].endswith(""".json"""						):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
						else:
												lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  parser.parse_args_into_dataclasses()

						check_output_dir(__lowerCAmelCase						)

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,    datefmt="""%m/%d/%Y %H:%M:%S"""   ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,    )
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""   ,    training_args.local_rank   ,    training_args.device   ,    training_args.n_gpu   ,    bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED						)   ,    training_args.fpaa   ,    )
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank						):
												transformers.utils.logging.set_verbosity_info()
						logger.info("""Training/evaluation parameters %s"""   ,    __lowerCAmelCase						)

						# Set seed
						set_seed(training_args.seed						)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowerCamelCase__         =  AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,    cache_dir=model_args.cache_dir   ,    )

						lowerCamelCase__         =  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
						for p in extra_model_params:
												if getattr(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						):
																		assert hasattr(__lowerCAmelCase   ,    __lowerCAmelCase						), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
																		setattr(__lowerCAmelCase   ,    __lowerCAmelCase   ,    getattr(__lowerCAmelCase   ,    __lowerCAmelCase						)						)

						lowerCamelCase__         =  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   ,    )
						lowerCamelCase__         =  AutoModelForSeqaSeqLM.from_pretrained(
						    model_args.model_name_or_path   ,    from_tf=""".ckpt""" in model_args.model_name_or_path   ,    config=__lowerCAmelCase   ,    cache_dir=model_args.cache_dir   ,    )

						# use task specific params
						use_task_specific_params(__lowerCAmelCase   ,    data_args.task						)

						# set num_beams for evaluation
						if data_args.eval_beams is None:
												lowerCamelCase__         =  model.config.num_beams

						# set decoder_start_token_id for MBart
						if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase   ,    (MBartTokenizer, MBartTokenizerFast)						):
												assert (
												    data_args.tgt_lang is not None and data_args.src_lang is not None
												), "mBart requires --tgt_lang and --src_lang"
												if isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
																		lowerCamelCase__         =  tokenizer.lang_code_to_id[data_args.tgt_lang]
												else:
																		lowerCamelCase__         =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

						if model_args.freeze_embeds:
												freeze_embeds(__lowerCAmelCase						)
						if model_args.freeze_encoder:
												freeze_params(model.get_encoder()						)
												assert_all_frozen(model.get_encoder()						)

						lowerCamelCase__         =  SeqaSeqDataset

						# Get datasets
						lowerCamelCase__         =  (
						    dataset_class(
						        __lowerCAmelCase   ,    type_path="""train"""   ,    data_dir=data_args.data_dir   ,    n_obs=data_args.n_train   ,    max_target_length=data_args.max_target_length   ,    max_source_length=data_args.max_source_length   ,    prefix=model.config.prefix or """"""   ,    )
						    if training_args.do_train
						    else None
						)
						lowerCamelCase__         =  (
						    dataset_class(
						        __lowerCAmelCase   ,    type_path="""val"""   ,    data_dir=data_args.data_dir   ,    n_obs=data_args.n_val   ,    max_target_length=data_args.val_max_target_length   ,    max_source_length=data_args.max_source_length   ,    prefix=model.config.prefix or """"""   ,    )
						    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
						    else None
						)
						lowerCamelCase__         =  (
						    dataset_class(
						        __lowerCAmelCase   ,    type_path="""test"""   ,    data_dir=data_args.data_dir   ,    n_obs=data_args.n_test   ,    max_target_length=data_args.test_max_target_length   ,    max_source_length=data_args.max_source_length   ,    prefix=model.config.prefix or """"""   ,    )
						    if training_args.do_predict
						    else None
						)

						# Initialize our Trainer
						lowerCamelCase__         =  (
						    build_compute_metrics_fn(data_args.task   ,    __lowerCAmelCase						) if training_args.predict_with_generate else None
						)
						lowerCamelCase__         =  SeqaSeqTrainer(
						    model=__lowerCAmelCase   ,    args=__lowerCAmelCase   ,    data_args=__lowerCAmelCase   ,    train_dataset=__lowerCAmelCase   ,    eval_dataset=__lowerCAmelCase   ,    data_collator=SeqaSeqDataCollator(
						        __lowerCAmelCase   ,    __lowerCAmelCase   ,    model.config.decoder_start_token_id   ,    training_args.tpu_num_cores						)   ,    compute_metrics=__lowerCAmelCase   ,    tokenizer=__lowerCAmelCase   ,    )

						lowerCamelCase__         =  {}
						# Training
						if training_args.do_train:
												logger.info("""*** Train ***"""						)

												lowerCamelCase__         =  trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
												lowerCamelCase__         =  train_result.metrics
												lowerCamelCase__         =  data_args.n_train

												trainer.save_model()  # this also saves the tokenizer

												if trainer.is_world_process_zero():
																		handle_metrics("""train"""   ,    __lowerCAmelCase   ,    training_args.output_dir						)
																		all_metrics.update(__lowerCAmelCase						)

																		# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
																		trainer.state.save_to_json(os.path.join(training_args.output_dir   ,    """trainer_state.json"""						)						)

																		# For convenience, we also re-save the tokenizer to the same directory,
																		# so that you can share your model easily on huggingface.co/models =)
																		tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""						)

												lowerCamelCase__         =  trainer.evaluate(metric_key_prefix="""val"""						)
												lowerCamelCase__         =  data_args.n_val
												lowerCamelCase__         =  round(metrics["""val_loss"""]   ,    4						)

												if trainer.is_world_process_zero():
																		handle_metrics("""val"""   ,    __lowerCAmelCase   ,    training_args.output_dir						)
																		all_metrics.update(__lowerCAmelCase						)

						if training_args.do_predict:
												logger.info("""*** Predict ***"""						)

												lowerCamelCase__         =  trainer.predict(test_dataset=__lowerCAmelCase   ,    metric_key_prefix="""test"""						)
												lowerCamelCase__         =  test_output.metrics
												lowerCamelCase__         =  data_args.n_test

												if trainer.is_world_process_zero():
																		lowerCamelCase__         =  round(metrics["""test_loss"""]   ,    4						)
																		handle_metrics("""test"""   ,    __lowerCAmelCase   ,    training_args.output_dir						)
																		all_metrics.update(__lowerCAmelCase						)

																		if training_args.predict_with_generate:
																								lowerCamelCase__         =  tokenizer.batch_decode(
																								    test_output.predictions   ,    skip_special_tokens=__lowerCAmelCase   ,    clean_up_tokenization_spaces=__lowerCAmelCase						)
																								lowerCamelCase__         =  lmap(str.strip   ,    __lowerCAmelCase						)
																								write_txt_file(__lowerCAmelCase   ,    os.path.join(training_args.output_dir   ,    """test_generations.txt"""						)						)

						if trainer.is_world_process_zero():
												save_json(__lowerCAmelCase   ,    os.path.join(training_args.output_dir   ,    """all_results.json"""						)						)

						return all_metrics
def 						A__			(				__lowerCAmelCase		:			Any						):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()









'''simple docstring'''







from math import factorial

UpperCamelCase    :      dict[str, int]  			=		{str(digit): factorial(digit) for digit in range(10)}
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												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(__lowerCAmelCase						)						)
def 						A__			(				__lowerCAmelCase		:			int = 60   ,    __lowerCAmelCase		:			int = 100_0000						):

						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						) or not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												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
						lowerCamelCase__         =  0
						# the cached sizes of the previous chains
						lowerCamelCase__         =  {}

						for start_chain_element in range(1   ,    __lowerCAmelCase						):
												# The temporary set will contain the elements of the chain
												lowerCamelCase__         =  set()
												lowerCamelCase__         =  0

												# Stop computing the chain when you find a cached size, a repeating item or the
												# length is greater then the desired one.
												lowerCamelCase__         =  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(__lowerCAmelCase						)
																		chain_set_length += 1
																		lowerCamelCase__         =  digit_factorial_sum(__lowerCAmelCase						)

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

												lowerCamelCase__         =  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()}')

