





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,
)


lowercase    : int    	=  logging.getLogger(__name__)

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






@dataclass
class  __A:
					__A	  =       field(
					    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."} )
					__A	  =       field(
					    default="summarization"						,			metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}						,			)
					__A	  =       field(
					    default=10_24						,			metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }						,			)
					__A	  =       field(
					    default=1_28						,			metadata={
					        "help": (
					            "The maximum total sequence length for target text after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }						,			)
					__A	  =       field(
					    default=1_42						,			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``."
					        )
					    }						,			)
					__A	  =       field(
					    default=1_42						,			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."
					        )
					    }						,			)
					__A	  =       field(default=-1						,			metadata={"help": "# training examples. -1 means use all."} )
					__A	  =       field(default=-1						,			metadata={"help": "# validation examples. -1 means use all."} )
					__A	  =       field(default=-1						,			metadata={"help": "# test examples. -1 means use all."} )
					__A	  =       field(default=UpperCAmelCase__						,			metadata={"help": "Source language id for translation."} )
					__A	  =       field(default=UpperCAmelCase__						,			metadata={"help": "Target language id for translation."} )
					__A	  =       field(default=UpperCAmelCase__						,			metadata={"help": "# num_beams to use for evaluation."} )
					__A	  =       field(
					    default=UpperCAmelCase__						,			metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}						,			)






def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase					):
							logger.info(f'''***** {split} metrics *****'''					)
							for key in sorted(metrics.keys()					):
														logger.info(f'''  {key} = {metrics[key]}'''					)
							save_json(UpperCAmelCase__    ,       os.path.join(UpperCAmelCase__    ,       f'''{split}_results.json'''					)					)


def     SCREAMING_SNAKE_CASE			(      ):
							_UpperCamelCase              =						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.
														_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
							else:
														_UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						parser.parse_args_into_dataclasses()

							check_output_dir(UpperCAmelCase__					)

							# 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'''    ,       UpperCAmelCase__					)

							# 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.

							_UpperCamelCase              =						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    ,       )

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

							_UpperCamelCase              =						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              =						AutoModelForSeqaSeqLM.from_pretrained(
							    model_args.model_name_or_path    ,       from_tf='''.ckpt''' in model_args.model_name_or_path    ,       config=UpperCAmelCase__    ,       cache_dir=model_args.cache_dir    ,       )

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

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

							# set decoder_start_token_id for MBart
							if model.config.decoder_start_token_id is None and isinstance(UpperCAmelCase__    ,       (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(UpperCAmelCase__    ,       UpperCAmelCase__					):
																					_UpperCamelCase              =						tokenizer.lang_code_to_id[data_args.tgt_lang]
														else:
																					_UpperCamelCase              =						tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

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

							_UpperCamelCase              =						SeqaSeqDataset

							# Get datasets
							_UpperCamelCase              =						(
							    dataset_class(
							        UpperCAmelCase__    ,       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
							)
							_UpperCamelCase              =						(
							    dataset_class(
							        UpperCAmelCase__    ,       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
							)
							_UpperCamelCase              =						(
							    dataset_class(
							        UpperCAmelCase__    ,       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
							_UpperCamelCase              =						(
							    build_compute_metrics_fn(data_args.task    ,       UpperCAmelCase__					) if training_args.predict_with_generate else None
							)
							_UpperCamelCase              =						SeqaSeqTrainer(
							    model=UpperCAmelCase__    ,       args=UpperCAmelCase__    ,       data_args=UpperCAmelCase__    ,       train_dataset=UpperCAmelCase__    ,       eval_dataset=UpperCAmelCase__    ,       data_collator=SeqaSeqDataCollator(
							        UpperCAmelCase__    ,       UpperCAmelCase__    ,       model.config.decoder_start_token_id    ,       training_args.tpu_num_cores					)    ,       compute_metrics=UpperCAmelCase__    ,       tokenizer=UpperCAmelCase__    ,       )

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

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

														trainer.save_model()  # this also saves the tokenizer

														if trainer.is_world_process_zero():
																					handle_metrics('''train'''    ,       UpperCAmelCase__    ,       training_args.output_dir					)
																					all_metrics.update(UpperCAmelCase__					)

																					# 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 ***'''					)

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

														if trainer.is_world_process_zero():
																					handle_metrics('''val'''    ,       UpperCAmelCase__    ,       training_args.output_dir					)
																					all_metrics.update(UpperCAmelCase__					)

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

														_UpperCamelCase              =						trainer.predict(test_dataset=UpperCAmelCase__    ,       metric_key_prefix='''test'''					)
														_UpperCamelCase              =						test_output.metrics
														_UpperCamelCase              =						data_args.n_test

														if trainer.is_world_process_zero():
																					_UpperCamelCase              =						round(metrics['''test_loss''']    ,       4					)
																					handle_metrics('''test'''    ,       UpperCAmelCase__    ,       training_args.output_dir					)
																					all_metrics.update(UpperCAmelCase__					)

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

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

							return all_metrics


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
							main()


if __name__ == "__main__":
							main()













from math import factorial


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 20					):
       _UpperCamelCase              =						2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
       # 2, 3,...
       _UpperCamelCase              =						n // 2

       return int(factorial(lowerCAmelCase					) / (factorial(lowerCAmelCase					) * factorial(n - k					))					)


if __name__ == "__main__":
       import sys

       if len(sys.argv) == 1:
              print(solution(20))
       else:
              try:
                     lowercase    : Dict    	=  int(sys.argv[1])
                     print(solution(n))
              except ValueError:
                     print("""Invalid entry - please enter a number.""")






