






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,
)


A__			:    Tuple         =				logging.getLogger(__name__)
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




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



@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(
				    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}			)
				lowercase__       							= field(
				    default="""summarization"""	,metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""}	,)
				lowercase__       							= field(
				    default=1_024	,metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }	,)
				lowercase__       							= 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."""
				        )
				    }	,)
				lowercase__       							= 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``."""
				        )
				    }	,)
				lowercase__       							= 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."""
				        )
				    }	,)
				lowercase__       							= field(default=-1	,metadata={"""help""": """# training examples. -1 means use all."""}			)
				lowercase__       							= field(default=-1	,metadata={"""help""": """# validation examples. -1 means use all."""}			)
				lowercase__       							= field(default=-1	,metadata={"""help""": """# test examples. -1 means use all."""}			)
				lowercase__       							= field(default=UpperCAmelCase_	,metadata={"""help""": """Source language id for translation."""}			)
				lowercase__       							= field(default=UpperCAmelCase_	,metadata={"""help""": """Target language id for translation."""}			)
				lowercase__       							= field(default=UpperCAmelCase_	,metadata={"""help""": """# num_beams to use for evaluation."""}			)
				lowercase__       							= field(
				    default=UpperCAmelCase_	,metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""}	,)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_       ):






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






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    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.
								lowercase__     				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
				else:
								lowercase__     				=    parser.parse_args_into_dataclasses()

				check_output_dir(lowerCamelCase_       )

				# 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'''	,     lowerCamelCase_       )

				# 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.

				lowercase__     				=    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	,     )

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

				lowercase__     				=    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	,     )
				lowercase__     				=    AutoModelForSeqaSeqLM.from_pretrained(
				    model_args.model_name_or_path	,     from_tf='''.ckpt''' in model_args.model_name_or_path	,     config=lowerCamelCase_	,     cache_dir=model_args.cache_dir	,     )

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

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

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

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

				lowercase__     				=    SeqaSeqDataset

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

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

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

								trainer.save_model()  # this also saves the tokenizer

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

												# 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 ***'''       )

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

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

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

								lowercase__     				=    trainer.predict(test_dataset=lowerCamelCase_	,     metric_key_prefix='''test'''       )
								lowercase__     				=    test_output.metrics
								lowercase__     				=    data_args.n_test

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

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

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

				return all_metrics






def 						a    (		lowerCamelCase_       ):






				'''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    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    [[] for _ in range(lowerCamelCase						)]
								lowercase__     				=    size




				def __getitem__(  self		:					Optional[Any],						lowerCamelCase		:					int						):

								'''simple docstring'''
								return iter(self._graph[vertex]						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self._size




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''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(lowerCamelCase,						lowerCamelCase						)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    deque([start_vertex]						)
								lowercase__     				=    [None] * self.size
								lowercase__     				=    0

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

												for edge in self[current_vertex]:
																lowercase__     				=    current_distance + edge.weight
																lowercase__     				=    distances[edge.destination_vertex]
																if (
																    isinstance(lowerCamelCase,						lowerCamelCase						)
																    and new_distance >= dest_vertex_distance
																):
																				continue
																lowercase__     				=    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()




