

"""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,
)


A_							:			str											= logging.getLogger(__name__)





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





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



def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''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'''


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

			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(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase    ):
									assert hasattr(_lowerCamelCase		,				_lowerCamelCase    ), f"""({config.__class__.__name__}) doesn\'t have a `{p}` attribute"""
									setattr(_lowerCamelCase		,				_lowerCamelCase		,				getattr(_lowerCamelCase		,				_lowerCamelCase    )    )

			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=_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:
						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(_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    ):
									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(_lowerCamelCase    )
			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(
			        _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
			)
			SCREAMING_SNAKE_CASE__								=      (
			    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
			)
			SCREAMING_SNAKE_CASE__								=      (
			    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
			SCREAMING_SNAKE_CASE__								=      (
			    build_compute_metrics_fn(data_args.task		,				_lowerCamelCase    ) if training_args.predict_with_generate else None
			)
			SCREAMING_SNAKE_CASE__								=      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		,				)

			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"""		,				_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 ***"""    )

						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"""		,				_lowerCamelCase		,				training_args.output_dir    )
									all_metrics.update(_lowerCamelCase    )

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

						SCREAMING_SNAKE_CASE__								=      trainer.predict(test_dataset=_lowerCamelCase		,				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"""		,				_lowerCamelCase		,				training_args.output_dir    )
									all_metrics.update(_lowerCamelCase    )

									if training_args.predict_with_generate:
												SCREAMING_SNAKE_CASE__								=      tokenizer.batch_decode(
												    test_output.predictions		,				skip_special_tokens=_lowerCamelCase		,				clean_up_tokenization_spaces=_lowerCamelCase    )
												SCREAMING_SNAKE_CASE__								=      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       (     snake_case__    ):




			'''simple docstring'''


			main()


if __name__ == "__main__":
						main()





"""simple docstring"""




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A_							:			Optional[Any]											= pytest.mark.integration


A_							:			Union[str, Any]											= {"comet"}
A_							:			str											= importlib.util.find_spec("fairseq") is not None

A_							:			Any											= {"code_eval"}
A_							:			Tuple											= os.name == "nt"

A_							:			List[Any]											= {"bertscore", "frugalscore", "perplexity"}
A_							:			Any											= importlib.util.find_spec("transformers") is not None

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     snake_case__    ):




			'''simple docstring'''


			@wraps(snake_case__    )
			def wrapper(self		,				snake_case__    ):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""    )
						else:
									test_case(self		,				snake_case__    )

			return wrapper

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      [metric_dir.split(os.sep    )[-2] for metric_dir in glob.glob("""./metrics/*/"""    )]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()			)
@for_all_test_methods(
    A__			,A__			,A__			)
@local
class    lowerCamelCase						(parameterized.TestCase			):
			lowerCamelCase__   :		int							=							{}
			lowerCamelCase__   :		Dict							=							None

			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						SCREAMING_SNAKE_CASE__								=      datasets.load.import_main_class(metric_module.__name__    ,				dataset=__UpperCAmelCase       )
						# check parameters
						SCREAMING_SNAKE_CASE__								=      inspect.signature(metric._compute       ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__UpperCAmelCase    ,				metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
												except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      """[...]"""
						SCREAMING_SNAKE_CASE__								=      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""    ,				__UpperCAmelCase       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									SCREAMING_SNAKE_CASE__								=      doctest.testmod(__UpperCAmelCase    ,				verbose=__UpperCAmelCase    ,				raise_on_error=__UpperCAmelCase       )
						self.assertEqual(results.failed    ,				0       )
						self.assertGreater(results.attempted    ,				1       )

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    str:
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](__UpperCAmelCase       ):
												yield
						else:
									yield

			@contextmanager
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						def load_local_metric(__UpperCAmelCase				:						int    ,				*__UpperCAmelCase				:						int    ,				**__UpperCAmelCase				:						Optional[int]       ):
									return load_metric(os.path.join("""metrics"""    ,				__UpperCAmelCase       )    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase       )

						with patch("""datasets.load_metric"""       ) as mock_load_metric:
									SCREAMING_SNAKE_CASE__								=      load_local_metric
									yield





			@classmethod
			def 					SCREAMING_SNAKE_CASE				(					cls				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[Any]:
						def wrapper(__UpperCAmelCase				:						List[str]       ):
									SCREAMING_SNAKE_CASE__								=      contextmanager(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      patcher
									return patcher

						return wrapper

@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv"""		,				""""""		,				""""""    )  # handle pytest cli flags





			class    lowerCamelCase						(A__			):

						def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    List[str]:
									assert len(input_dict["""input_ids"""]       ) == 2
									return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""    ) as mock_create_predictor:
						SCREAMING_SNAKE_CASE__								=      MockedPredictor()
						yield

@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			import torch

			def bert_cos_score_idf(snake_case__		,				snake_case__		,				*snake_case__		,				**snake_case__    ):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case__    )    )

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""    ), patch(
			    """bert_score.scorer.bert_cos_score_idf"""    ) as mock_bert_cos_score_idf:
						SCREAMING_SNAKE_CASE__								=      bert_cos_score_idf
						yield

@LocalMetricTest.register_intensive_calls_patcher("""comet"""    )
def 	A       (     snake_case__    ):




			'''simple docstring'''


			def load_from_checkpoint(snake_case__    ):





						class    lowerCamelCase						:

									def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Any    ,				*__UpperCAmelCase				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    Optional[int]:
												assert len(__UpperCAmelCase       ) == 2
												SCREAMING_SNAKE_CASE__								=      [0.19, 0.92]
												return scores, sum(__UpperCAmelCase       ) / len(__UpperCAmelCase       )
						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""    ) as mock_download_model:
						SCREAMING_SNAKE_CASE__								=      None
						with patch("""comet.load_from_checkpoint"""    ) as mock_load_from_checkpoint:
									SCREAMING_SNAKE_CASE__								=      load_from_checkpoint
									yield




def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      load_metric(os.path.join("""metrics"""		,				"""seqeval"""    )    )
			SCREAMING_SNAKE_CASE__								=      """ERROR"""
			SCREAMING_SNAKE_CASE__								=      f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
			with pytest.raises(snake_case__		,				match=re.escape(snake_case__    )    ):
						metric.compute(predictions=[]		,				references=[]		,				scheme=snake_case__    )


