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





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



def    a      ( a		,						a		,						a	)     ->List[str]:




			'''simple docstring'''


			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    a      ( )     ->List[str]:




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE     						= 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		,						)

			SCREAMING_SNAKE_CASE     						= ('''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	)	)

			SCREAMING_SNAKE_CASE     						= 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		,						)
			SCREAMING_SNAKE_CASE     						= 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:
						SCREAMING_SNAKE_CASE     						= 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	):
									SCREAMING_SNAKE_CASE     						= tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									SCREAMING_SNAKE_CASE     						= 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()	)

			SCREAMING_SNAKE_CASE     						= SeqaSeqDataset

			# Get datasets
			SCREAMING_SNAKE_CASE     						= (
			    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
			)
			SCREAMING_SNAKE_CASE     						= (
			    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
			)
			SCREAMING_SNAKE_CASE     						= (
			    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
			SCREAMING_SNAKE_CASE     						= (
			    build_compute_metrics_fn(data_args.task		,						a	) if training_args.predict_with_generate else None
			)
			SCREAMING_SNAKE_CASE     						= 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		,						)

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

						SCREAMING_SNAKE_CASE     						= trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
						SCREAMING_SNAKE_CASE     						= train_result.metrics
						SCREAMING_SNAKE_CASE     						= 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 ***'''	)

						SCREAMING_SNAKE_CASE     						= trainer.evaluate(metric_key_prefix='''val'''	)
						SCREAMING_SNAKE_CASE     						= data_args.n_val
						SCREAMING_SNAKE_CASE     						= 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 ***'''	)

						SCREAMING_SNAKE_CASE     						= trainer.predict(test_dataset=a		,						metric_key_prefix='''test'''	)
						SCREAMING_SNAKE_CASE     						= test_output.metrics
						SCREAMING_SNAKE_CASE     						= data_args.n_test

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

									if training_args.predict_with_generate:
												SCREAMING_SNAKE_CASE     						= tokenizer.batch_decode(
												    test_output.predictions		,						skip_special_tokens=a		,						clean_up_tokenization_spaces=a	)
												SCREAMING_SNAKE_CASE     						= 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    a      ( a	)     ->Union[str, Any]:




			'''simple docstring'''


			main()


if __name__ == "__main__":
							main()

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      str:







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()