






'''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,
)


__lowerCAmelCase       		=      logging.getLogger(__name__)





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





@dataclass
class 	SCREAMING_SNAKE_CASE					:
			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=1024	,   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=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."
			        )
			    }	,   )
			snake_case__  =				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``."
			        )
			    }	,   )
			snake_case__  =				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."
			        )
			    }	,   )
			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=SCREAMING_SNAKE_CASE_	,   metadata={"help": "Source language id for translation."}       )
			snake_case__  =				field(default=SCREAMING_SNAKE_CASE_	,   metadata={"help": "Target language id for translation."}       )
			snake_case__  =				field(default=SCREAMING_SNAKE_CASE_	,   metadata={"help": "# num_beams to use for evaluation."}       )
			snake_case__  =				field(
			    default=SCREAMING_SNAKE_CASE_	,   metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}	,   )





def 		_UpperCAmelCase   (							__A							:   int						,		__A							:   Dict						,		__A							:   Tuple      ):
				logger.info(f'***** {split} metrics *****'      )
				for key in sorted(metrics.keys()      ):
								logger.info(f'  {key} = {metrics[key]}'      )
				save_json(__A						,		os.path.join(__A						,		f'{split}_results.json'      )      )





def 		_UpperCAmelCase   (							):
				# 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.

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

				check_output_dir(__A      )

				# 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'''						,		__A      )

				# 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_		:  Union[str, Any]											=      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_		:  List[Any]											=      ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
				for p in extra_model_params:
								if getattr(__A						,		__A						,		__A      ):
												assert hasattr(__A						,		__A      ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
												setattr(__A						,		__A						,		getattr(__A						,		__A      )      )

				a_		:  Union[str, Any]											=      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_		:  Optional[int]											=      AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path						,		from_tf='''.ckpt''' in model_args.model_name_or_path						,		config=__A						,		cache_dir=model_args.cache_dir						,		)

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

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

				# set decoder_start_token_id for MBart
				if model.config.decoder_start_token_id is None and isinstance(__A						,		(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(__A						,		__A      ):
												a_		:  int											=      tokenizer.lang_code_to_id[data_args.tgt_lang]
								else:
												a_		:  List[str]											=      tokenizer.convert_tokens_to_ids(data_args.tgt_lang      )

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

				a_		:  Any											=      SeqaSeqDataset

				# Get datasets
				a_		:  str											=      (
				    dataset_class(
				        __A						,		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_		:  Union[str, Any]											=      (
				    dataset_class(
				        __A						,		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_		:  Union[str, Any]											=      (
				    dataset_class(
				        __A						,		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_		:  List[Any]											=      (
				    build_compute_metrics_fn(data_args.task						,		__A      ) if training_args.predict_with_generate else None
				)
				a_		:  int											=      SeqaSeqTrainer(
				    model=__A						,		args=__A						,		data_args=__A						,		train_dataset=__A						,		eval_dataset=__A						,		data_collator=SeqaSeqDataCollator(
				        __A						,		__A						,		model.config.decoder_start_token_id						,		training_args.tpu_num_cores      )						,		compute_metrics=__A						,		tokenizer=__A						,		)

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

								a_		:  List[str]											=      trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								a_		:  str											=      train_result.metrics
								a_		:  List[Any]											=      data_args.n_train

								trainer.save_model()  # this also saves the tokenizer

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

												# 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_		:  Any											=      trainer.evaluate(metric_key_prefix='''val'''      )
								a_		:  List[Any]											=      data_args.n_val
								a_		:  List[Any]											=      round(metrics['''val_loss''']						,		4      )

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

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

								a_		:  Union[str, Any]											=      trainer.predict(test_dataset=__A						,		metric_key_prefix='''test'''      )
								a_		:  List[Any]											=      test_output.metrics
								a_		:  int											=      data_args.n_test

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

												if training_args.predict_with_generate:
																a_		:  List[Any]											=      tokenizer.batch_decode(
																    test_output.predictions						,		skip_special_tokens=__A						,		clean_up_tokenization_spaces=__A      )
																a_		:  Dict											=      lmap(str.strip						,		__A      )
																write_txt_file(__A						,		os.path.join(training_args.output_dir						,		'''test_generations.txt'''      )      )

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

				return all_metrics





def 		_UpperCAmelCase   (							__A							:   List[str]      ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()












'''simple docstring'''







import requests
from bsa import BeautifulSoup





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   dict      ):
				a_		:  Tuple											=      BeautifulSoup(requests.get(__A						,		params=__A      ).content						,		'''html.parser'''      )
				a_		:  List[str]											=      soup.find('''div'''						,		attrs={'''class''': '''gs_ri'''}      )
				a_		:  List[str]											=      div.find('''div'''						,		attrs={'''class''': '''gs_fl'''}      ).find_all('''a'''      )
				return anchors[2].get_text()


if __name__ == "__main__":
		__lowerCAmelCase       		=      {
		    'title': (
		        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
		        'capacitance, volumetric capacitance, and energy density'
		    ),
		    'journal': 'Chem. Mater.',
		    'volume': 30,
		    'pages': '3979-3990',
		    'year': 2_018,
		    'hl': 'en',
		}
		print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




