



'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)


lowerCamelCase							:Union[str, Any]       = {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase							:Any       = {
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCamelCase							:List[str]       = {'''facebook/blenderbot-3B''': 1_2_8}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      a      (			):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					(
			    list(range(ord("""!"""   )						,				ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )						,				ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )						,				ord("""ÿ"""   ) + 1   )   )
			)
			A_     :						List[str]     =					bs[:]
			A_     :						Any     =					0
			for b in range(2**8   ):
						if b not in bs:
									bs.append(A_   )
									cs.append(2**8 + n   )
									n += 1
			A_     :						Tuple     =					[chr(A_   ) for n in cs]
			return dict(zip(A_						,				A_   )   )



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						Any     =					set()
			A_     :						Dict     =					word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)   )
						A_     :						str     =					char
			return pairs







class      _lowerCAmelCase							(      a_			):
	__SCREAMING_SNAKE_CASE       :		Any		      = VOCAB_FILES_NAMES
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = PRETRAINED_VOCAB_FILES_MAP
	__SCREAMING_SNAKE_CASE       :		Tuple		      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = ['input_ids', 'attention_mask']


	def __init__(self	,				lowercase	,				lowercase	,				lowercase="replace"	,				lowercase="<s>"	,				lowercase="</s>"	,				lowercase="</s>"	,				lowercase="<s>"	,				lowercase="<unk>"	,				lowercase="<pad>"	,				lowercase="<mask>"	,				lowercase=False	,				**lowercase	,				):
				A_     :						Optional[Any]     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else bos_token
				A_     :						List[Any]     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else eos_token
				A_     :						Optional[int]     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else sep_token
				A_     :						Optional[int]     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else cls_token
				A_     :						List[str]     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else unk_token
				A_     :						Tuple     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				A_     :						Dict     =					AddedToken(lowercase_	,				lstrip=lowercase_	,				rstrip=lowercase_							) if isinstance(lowercase_	,				lowercase_							) else mask_token

				super().__init__(
				    errors=lowercase_	,				bos_token=lowercase_	,				eos_token=lowercase_	,				unk_token=lowercase_	,				sep_token=lowercase_	,				cls_token=lowercase_	,				pad_token=lowercase_	,				mask_token=lowercase_	,				add_prefix_space=lowercase_	,				**lowercase_	,				)

				with open(lowercase_	,				encoding="""utf-8"""							) as vocab_handle:
							A_     :						Optional[int]     =					json.load(lowercase_							)
				A_     :						Union[str, Any]     =					{v: k for k, v in self.encoder.items()}
				A_     :						str     =					errors  # how to handle errors in decoding
				A_     :						Tuple     =					bytes_to_unicode()
				A_     :						Any     =					{v: k for k, v in self.byte_encoder.items()}
				with open(lowercase_	,				encoding="""utf-8"""							) as merges_handle:
							A_     :						List[str]     =					merges_handle.read().split("""\n"""							)[1:-1]
				A_     :						Union[str, Any]     =					[tuple(merge.split()							) for merge in bpe_merges]
				A_     :						Dict     =					dict(zip(lowercase_	,				range(len(lowercase_							)							)							)							)
				A_     :						str     =					{}
				A_     :						str     =					add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				A_     :						str     =					re.compile(R"""\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


	@property
	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
	def    _a     (self							):
				return len(self.encoder							)


	def    _a     (self							):
				return dict(self.encoder	,				**self.added_tokens_encoder							)


	def    _a     (self	,				lowercase							):
				if token in self.cache:
							return self.cache[token]
				A_     :						Optional[Any]     =					tuple(lowercase_							)
				A_     :						Optional[Any]     =					get_pairs(lowercase_							)

				if not pairs:
							return token

				while True:
							A_     :						Any     =					min(lowercase_	,				key=lambda lowercase							: self.bpe_ranks.get(lowercase_	,				float("""inf"""							)							)							)
							if bigram not in self.bpe_ranks:
										break
							A_     :						Optional[int]     =					bigram
							A_     :						Union[str, Any]     =					[]
							A_     :						Dict     =					0
							while i < len(lowercase_							):
										try:
													A_     :						List[str]     =					word.index(lowercase_	,				lowercase_							)
										except ValueError:
													new_word.extend(word[i:]							)
													break
										else:
													new_word.extend(word[i:j]							)
													A_     :						List[str]     =					j

										if word[i] == first and i < len(lowercase_							) - 1 and word[i + 1] == second:
													new_word.append(first + second							)
													i += 2
										else:
													new_word.append(word[i]							)
													i += 1
							A_     :						str     =					tuple(lowercase_							)
							A_     :						int     =					new_word
							if len(lowercase_							) == 1:
										break
							else:
										A_     :						Any     =					get_pairs(lowercase_							)
				A_     :						Optional[Any]     =					''' '''.join(lowercase_							)
				A_     :						List[Any]     =					word
				return word


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					[]
				for token in re.findall(self.pat	,				lowercase_							):
							A_     :						Optional[Any]     =					''''''.join(
							    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase_							).split(""" """							)							)
				return bpe_tokens


	def    _a     (self	,				lowercase							):
				return self.encoder.get(lowercase_	,				self.encoder.get(self.unk_token							)							)


	def    _a     (self	,				lowercase							):
				return self.decoder.get(lowercase_							)


	def    _a     (self	,				lowercase							):
				A_     :						Tuple     =					''''''.join(lowercase_							)
				A_     :						List[Any]     =					bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""	,				errors=self.errors							)
				return text


	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_     :						str     =					os.path.join(
				    lowercase_	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
				A_     :						List[Any]     =					os.path.join(
				    lowercase_	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

				with open(lowercase_	,				"""w"""	,				encoding="""utf-8"""							) as f:
							f.write(json.dumps(self.encoder	,				indent=2	,				sort_keys=lowercase_	,				ensure_ascii=lowercase_							) + """\n"""							)

				A_     :						str     =					0
				with open(lowercase_	,				"""w"""	,				encoding="""utf-8"""							) as writer:
							writer.write("""#version: 0.2\n"""							)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,				key=lambda lowercase							: kv[1]							):
										if index != token_index:
													logger.warning(
													    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
													    """ Please check that the tokenizer is not corrupted!"""							)
													A_     :						Optional[Any]     =					token_index
										writer.write(""" """.join(lowercase_							) + """\n"""							)
										index += 1

				return vocab_file, merge_file


	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_							)

				if token_ids_a is None:
							return [1] + ([0] * len(lowercase_							)) + [1]
				return [1] + ([0] * len(lowercase_							)) + [1, 1] + ([0] * len(lowercase_							)) + [1]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						Optional[Any]     =					[self.sep_token_id]
				A_     :						Union[str, 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=False	,				**lowercase							):
				A_     :						Union[str, Any]     =					kwargs.pop("""add_prefix_space"""	,				self.add_prefix_space							)
				if (is_split_into_words or add_prefix_space) and (len(lowercase_							) > 0 and not text[0].isspace()):
							A_     :						List[str]     =					''' ''' + text
				return (text, kwargs)


	def    _a     (self	,				lowercase	,				lowercase = None							):
				return token_ids_a + [self.eos_token_id]


	def    _a     (self	,				lowercase							):
				A_     :						List[Any]     =					[]
				for is_user, text in conversation.iter_texts():
							if is_user:
										# We need to space prefix as it's being done within blenderbot
										inputs.append(""" """ + text							)
							else:
										# Generated responses should contain them already.
										inputs.append(lowercase_							)

				A_     :						Optional[Any]     =					'''  '''.join(lowercase_							)
				A_     :						List[Any]     =					self.encode(lowercase_							)
				if len(lowercase_							) > self.model_max_length:
							A_     :						List[Any]     =					input_ids[-self.model_max_length :]
							logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'							)
				return input_ids




'''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()