



'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase							:Dict       = logging.get_logger(__name__)

lowerCamelCase							:Optional[int]       = '''▁'''

lowerCamelCase							:str       = {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCamelCase							:Any       = {
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCamelCase							:Optional[int]       = {
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
lowerCamelCase							:List[str]       = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE       :		List[str]		      = PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE       :		int		      = ['input_ids', 'attention_mask']

	__SCREAMING_SNAKE_CASE       :		List[int]		      = []
	__SCREAMING_SNAKE_CASE       :		List[int]		      = []


	def __init__(self	,				lowercase	,				lowercase="<s>"	,				lowercase="</s>"	,				lowercase="</s>"	,				lowercase="<s>"	,				lowercase="<unk>"	,				lowercase="<pad>"	,				lowercase="<mask>"	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				lowercase = None	,				lowercase=None	,				**lowercase	,				):
				# Mask token behave like a normal word, i.e. include the space before it
				A_     :						str     =					AddedToken(lowercase	,				lstrip=lowercase	,				rstrip=lowercase							) if isinstance(lowercase	,				lowercase							) else mask_token

				A_     :						int     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=lowercase	,				eos_token=lowercase	,				unk_token=lowercase	,				sep_token=lowercase	,				cls_token=lowercase	,				pad_token=lowercase	,				mask_token=lowercase	,				tokenizer_file=lowercase	,				src_lang=lowercase	,				tgt_lang=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				**lowercase	,				)

				A_     :						Optional[Any]     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(str(lowercase							)							)
				A_     :						Any     =					vocab_file

				# Original fairseq vocab and spm vocab must be "aligned":
				# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
				# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
				# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
				# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

				# Mimic fairseq token-to-id alignment for the first 4 token
				A_     :						str     =					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				A_     :						Dict     =					1

				A_     :						Dict     =					len(self.sp_model							)
				A_     :						str     =					{
				    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase							)
				}
				A_     :						List[Any]     =					{v: k for k, v in self.lang_code_to_id.items()}
				A_     :						List[Any]     =					len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

				self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
				A_     :						Dict     =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
				A_     :						Optional[int]     =					list(self.lang_code_to_id.keys()							)

				if additional_special_tokens is not None:
							# Only add those special tokens if they are not already there.
							self._additional_special_tokens.extend(
							    [t for t in additional_special_tokens if t not in self._additional_special_tokens]							)

				A_     :						Tuple     =					src_lang if src_lang is not None else """en_XX"""
				A_     :						List[str]     =					self.lang_code_to_id[self._src_lang]
				A_     :						int     =					tgt_lang
				self.set_src_lang_special_tokens(self._src_lang							)


	def __getstate__(self							):
				A_     :						Union[str, Any]     =					self.__dict__.copy()
				A_     :						Optional[Any]     =					None
				A_     :						Dict     =					self.sp_model.serialized_model_proto()
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Optional[int]     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						Tuple     =					{}

				A_     :						List[str]     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


	@property
	def    _a     (self							):
				return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token


	@property
	def    _a     (self							):
				return self._src_lang


	@src_lang.setter
	def    _a     (self	,				lowercase							):
				A_     :						List[Any]     =					new_src_lang
				self.set_src_lang_special_tokens(self._src_lang							)


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase	,				token_ids_a=lowercase	,				already_has_special_tokens=lowercase							)

				A_     :						List[Any]     =					[1] * len(self.prefix_tokens							)
				A_     :						Dict     =					[1] * len(self.suffix_tokens							)
				if token_ids_a is None:
							return prefix_ones + ([0] * len(lowercase							)) + suffix_ones
				return prefix_ones + ([0] * len(lowercase							)) + ([0] * len(lowercase							)) + suffix_ones


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Dict     =					[self.sep_token_id]
				A_     :						List[Any]     =					[self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep							) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase	,				lowercase	,				**lowercase							):
				if src_lang is None or tgt_lang is None:
							raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""							)
				A_     :						Any     =					src_lang
				A_     :						Optional[int]     =					self(lowercase	,				add_special_tokens=lowercase	,				return_tensors=lowercase	,				**lowercase							)
				A_     :						int     =					self.convert_tokens_to_ids(lowercase							)
				A_     :						Optional[int]     =					tgt_lang_id
				return inputs


	def    _a     (self							):
				A_     :						List[Any]     =					{self.convert_ids_to_tokens(lowercase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def    _a     (self	,				lowercase							):
				return self.sp_model.encode(lowercase	,				out_type=lowercase							)


	def    _a     (self	,				lowercase							):
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				A_     :						Dict     =					self.sp_model.PieceToId(lowercase							)

				# Need to return unknown token if the SP model returned 0
				return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


	def    _a     (self	,				lowercase							):
				if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
				return self.sp_model.IdToPiece(index - self.fairseq_offset							)


	def    _a     (self	,				lowercase							):
				A_     :						Any     =					"""""".join(lowercase							).replace(lowercase	,				""" """							).strip()
				return out_string


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if not os.path.isdir(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Any     =					os.path.join(
				    lowercase	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

				if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file	,				lowercase							)
				elif not os.path.isfile(self.vocab_file							):
							with open(lowercase	,				"""wb"""							) as fi:
										A_     :						int     =					self.sp_model.serialized_model_proto()
										fi.write(lowercase							)

				return (out_vocab_file,)


	def    _a     (self	,				lowercase	,				lowercase = "en_XX"	,				lowercase = None	,				lowercase = "ro_RO"	,				**lowercase	,				):
				A_     :						Any     =					src_lang
				A_     :						Tuple     =					tgt_lang
				return super().prepare_seqaseq_batch(lowercase	,				lowercase	,				**lowercase							)


	def    _a     (self							):
				return self.set_src_lang_special_tokens(self.src_lang							)


	def    _a     (self							):
				return self.set_tgt_lang_special_tokens(self.tgt_lang							)


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.lang_code_to_id[src_lang]
				A_     :						str     =					[]
				A_     :						Dict     =					[self.eos_token_id, self.cur_lang_code]


	def    _a     (self	,				lowercase							):
				A_     :						Optional[Any]     =					self.lang_code_to_id[lang]
				A_     :						int     =					[]
				A_     :						Union[str, Any]     =					[self.eos_token_id, self.cur_lang_code]




'''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							:Union[str, Any]       = logging.getLogger(__name__)







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







@dataclass
class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		str		      = field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(
	    default='summarization'				,      metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 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.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 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.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 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``.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 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.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=-1				,      metadata={'help': '# training examples. -1 means use all.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=-1				,      metadata={'help': '# validation examples. -1 means use all.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=-1				,      metadata={'help': '# test examples. -1 means use all.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(default=__UpperCAmelCase				,      metadata={'help': 'Source language id for translation.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(default=__UpperCAmelCase				,      metadata={'help': 'Target language id for translation.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=__UpperCAmelCase				,      metadata={'help': '# num_beams to use for evaluation.'}			)
	__SCREAMING_SNAKE_CASE       :		bool		      = 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'''





			A_     :						Union[str, 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.
						A_,   A_,   A_     :						str     =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						A_,   A_,   A_     :						List[str]     =					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.

			A_     :						List[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_     :						int     =					("""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__   )   )

			A_     :						Optional[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[Any]     =					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:
						A_     :						int     =					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__   ):
									A_     :						Optional[Any]     =					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(lowerCamelCase__   )
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder()   )
						assert_all_frozen(model.get_encoder()   )

			A_     :						Union[str, Any]     =					SeqaSeqDataset

			# Get datasets
			A_     :						Union[str, Any]     =					(
			    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
			)
			A_     :						int     =					(
			    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
			)
			A_     :						Tuple     =					(
			    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
			A_     :						Optional[Any]     =					(
			    build_compute_metrics_fn(data_args.task						,				lowerCamelCase__   ) if training_args.predict_with_generate else None
			)
			A_     :						List[str]     =					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__						,				)

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

						A_     :						List[Any]     =					trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
						A_     :						Any     =					train_result.metrics
						A_     :						str     =					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 ***"""   )

						A_     :						Tuple     =					trainer.evaluate(metric_key_prefix="""val"""   )
						A_     :						str     =					data_args.n_val
						A_     :						Optional[Any]     =					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 ***"""   )

						A_     :						Any     =					trainer.predict(test_dataset=lowerCamelCase__						,				metric_key_prefix="""test"""   )
						A_     :						int     =					test_output.metrics
						A_     :						Tuple     =					data_args.n_test

						if trainer.is_world_process_zero():
									A_     :						List[Any]     =					round(metrics["""test_loss"""]						,				4   )
									handle_metrics("""test"""						,				lowerCamelCase__						,				training_args.output_dir   )
									all_metrics.update(lowerCamelCase__   )

									if training_args.predict_with_generate:
												A_     :						List[Any]     =					tokenizer.batch_decode(
												    test_output.predictions						,				skip_special_tokens=lowerCamelCase__						,				clean_up_tokenization_spaces=lowerCamelCase__   )
												A_     :						Tuple     =					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()