


'''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,
)


_SCREAMING_SNAKE_CASE							: str										=			logging.getLogger(__name__)






@dataclass
class 		_snake_case     :






						'''simple docstring'''







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






@dataclass
class 		_snake_case     :






						'''simple docstring'''







						__snake_case				=   field(
						    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}		)
						__snake_case				=   field(
						    default="summarization"	,	metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}	,	)
						__snake_case				=   field(
						    default=1_0_2_4	,	metadata={
						        "help": (
						            "The maximum total input sequence length after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }	,	)
						__snake_case				=   field(
						    default=1_2_8	,	metadata={
						        "help": (
						            "The maximum total sequence length for target text after tokenization. Sequences longer "
						            "than this will be truncated, sequences shorter will be padded."
						        )
						    }	,	)
						__snake_case				=   field(
						    default=1_4_2	,	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``."
						        )
						    }	,	)
						__snake_case				=   field(
						    default=1_4_2	,	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."
						        )
						    }	,	)
						__snake_case				=   field(default=-1	,	metadata={"help": "# training examples. -1 means use all."}		)
						__snake_case				=   field(default=-1	,	metadata={"help": "# validation examples. -1 means use all."}		)
						__snake_case				=   field(default=-1	,	metadata={"help": "# test examples. -1 means use all."}		)
						__snake_case				=   field(default=__lowerCamelCase	,	metadata={"help": "Source language id for translation."}		)
						__snake_case				=   field(default=__lowerCamelCase	,	metadata={"help": "Target language id for translation."}		)
						__snake_case				=   field(default=__lowerCamelCase	,	metadata={"help": "# num_beams to use for evaluation."}		)
						__snake_case				=   field(
						    default=__lowerCamelCase	,	metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}	,	)






def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				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     _UpperCamelCase				(			):






				"""simple docstring"""



				__magic_name__					:       Any    							=							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.
								__magic_name__							,  __magic_name__							,  __magic_name__					:       int    							=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
				else:
								__magic_name__							,  __magic_name__							,  __magic_name__					:       Tuple    							=							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.

				__magic_name__					:       int    							=							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  , )

				__magic_name__					:       int    							=							("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__		)		)

				__magic_name__					:       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  , )
				__magic_name__					:       int    							=							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:
								__magic_name__					:       int    							=							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__		):
												__magic_name__					:       Tuple    							=							tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												__magic_name__					:       List[Any]    							=							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()		)

				__magic_name__					:       List[Any]    							=							SeqaSeqDataset

				# Get datasets
				__magic_name__					:       str    							=							(
				    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
				)
				__magic_name__					:       int    							=							(
				    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
				)
				__magic_name__					:       Optional[int]    							=							(
				    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
				__magic_name__					:       Tuple    							=							(
				    build_compute_metrics_fn(data_args.task  , UpperCamelCase__		) if training_args.predict_with_generate else None
				)
				__magic_name__					:       Tuple    							=							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__  , )

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

								__magic_name__					:       int    							=							trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
								__magic_name__					:       str    							=							train_result.metrics
								__magic_name__					:       int    							=							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 ***"		)

								__magic_name__					:       int    							=							trainer.evaluate(metric_key_prefix="val"		)
								__magic_name__					:       Dict    							=							data_args.n_val
								__magic_name__					:       Optional[int]    							=							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 ***"		)

								__magic_name__					:       str    							=							trainer.predict(test_dataset=UpperCamelCase__  , metric_key_prefix="test"		)
								__magic_name__					:       Tuple    							=							test_output.metrics
								__magic_name__					:       Union[str, Any]    							=							data_args.n_test

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

												if training_args.predict_with_generate:
																__magic_name__					:       Dict    							=							tokenizer.batch_decode(
																    test_output.predictions  , skip_special_tokens=UpperCamelCase__  , clean_up_tokenization_spaces=UpperCamelCase__		)
																__magic_name__					:       List[str]    							=							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     _UpperCamelCase				(			UpperCamelCase__		):






				"""simple docstring"""



				main()


if __name__ == "__main__":
		main()

from __future__ import annotations


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						if resistor <= 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative or zero value!"""
									raise ValueError(a	)
						first_sum += 1 / float(a	)
						index += 1
			return 1 / first_sum


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative value!"""
									raise ValueError(a	)
						index += 1
			return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()