

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,
)


lowerCAmelCase__	   = logging.getLogger(__name__)

@dataclass
class 					a__						:
			"""simple docstring"""







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

@dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       field(
			    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
			__lowerCamelCase        =       field(
			    default='summarization'							,	metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}							,	)
			__lowerCamelCase        =       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.'
			        )
			    }							,	)
			__lowerCamelCase        =       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.'
			        )
			    }							,	)
			__lowerCamelCase        =       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``.'
			        )
			    }							,	)
			__lowerCamelCase        =       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.'
			        )
			    }							,	)
			__lowerCamelCase        =       field(default=-1							,	metadata={'help': '# training examples. -1 means use all.'}			)
			__lowerCamelCase        =       field(default=-1							,	metadata={'help': '# validation examples. -1 means use all.'}			)
			__lowerCamelCase        =       field(default=-1							,	metadata={'help': '# test examples. -1 means use all.'}			)
			__lowerCamelCase        =       field(default=snake_case							,	metadata={'help': 'Source language id for translation.'}			)
			__lowerCamelCase        =       field(default=snake_case							,	metadata={'help': 'Target language id for translation.'}			)
			__lowerCamelCase        =       field(default=snake_case							,	metadata={'help': '# num_beams to use for evaluation.'}			)
			__lowerCamelCase        =       field(
			    default=snake_case							,	metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}							,	)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							str      )			->      Any:




				'''simple docstring'''





				logger.info(F'***** {split} metrics *****'      )
				for key in sorted(metrics.keys()      ):
								logger.info(F'  {key} = {metrics[key]}'      )
				save_json(SCREAMING_SNAKE_CASE_	,       os.path.join(SCREAMING_SNAKE_CASE_	,       F'{split}_results.json'      )      )



def 					lowerCAmelCase__							(	)			->      str:




				'''simple docstring'''





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

				check_output_dir(SCREAMING_SNAKE_CASE_      )

				# 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"	,       SCREAMING_SNAKE_CASE_      )

				# 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.

				A__        =			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	,       )

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

				A__        =			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	,       )
				A__        =			AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path	,       from_tf=".ckpt" in model_args.model_name_or_path	,       config=SCREAMING_SNAKE_CASE_	,       cache_dir=model_args.cache_dir	,       )

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

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

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

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

				A__        =			SeqaSeqDataset

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

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

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

								trainer.save_model()  # this also saves the tokenizer

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

												# 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 ***"      )

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

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

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

								A__        =			trainer.predict(test_dataset=SCREAMING_SNAKE_CASE_	,       metric_key_prefix="test"      )
								A__        =			test_output.metrics
								A__        =			data_args.n_test

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

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

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

				return all_metrics



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str      )			->      Tuple:




				'''simple docstring'''





				main()


if __name__ == "__main__":
	main()




import math

lowerCAmelCase__	   = 1_0
lowerCAmelCase__	   = 7
lowerCAmelCase__	   = BALLS_PER_COLOUR * NUM_COLOURS



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 2_0      )			->      str:




				'''simple docstring'''





				A__        =			math.comb(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				A__        =			math.comb(NUM_BALLS - BALLS_PER_COLOUR	,       SCREAMING_SNAKE_CASE_      )

				A__        =			NUM_COLOURS * (1 - missing_colour / total)

				return F'{result:.9f}'


if __name__ == "__main__":
	print(solution(2_0))

