






"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__ = None			,):
			UpperCAmelCase_  :				List[str]      =      {}

			if train_file is not None:
						UpperCAmelCase_  :				List[str]      =      [train_file]
			if eval_file is not None:
						UpperCAmelCase_  :				int      =      [eval_file]
			if test_file is not None:
						UpperCAmelCase_  :				Dict      =      [test_file]

			UpperCAmelCase_  :				Tuple      =      datasets.load_dataset("csv"			,data_files=A__		)
			UpperCAmelCase_  :				Tuple      =      list(ds[list(files.keys()		)[0]].features.keys()		)
			UpperCAmelCase_  :				Dict      =      features_name.pop(A__		)
			UpperCAmelCase_  :				Any      =      list(set(ds[list(files.keys()		)[0]][label_name]		)		)
			UpperCAmelCase_  :				List[Any]      =      {label: i for i, label in enumerate(A__		)}
			UpperCAmelCase_  :				List[Any]      =      tokenizer.model_input_names
			UpperCAmelCase_  :				int      =      {}

			if len(A__		) == 1:
						for k in files.keys():
									UpperCAmelCase_  :				List[str]      =      ds[k].map(
									    lambda A__		: tokenizer.batch_encode_plus(
									        example[features_name[0]]			,truncation=A__			,max_length=A__			,padding="max_length"		)			,batched=A__			,)
			elif len(A__		) == 2:
						for k in files.keys():
									UpperCAmelCase_  :				Dict      =      ds[k].map(
									    lambda A__		: tokenizer.batch_encode_plus(
									        (example[features_name[0]], example[features_name[1]])			,truncation=A__			,max_length=A__			,padding="max_length"			,)			,batched=A__			,)

			def gen_train():
						for ex in transformed_ds[datasets.Split.TRAIN]:
									UpperCAmelCase_  :				List[Any]      =      {k: v for k, v in ex.items() if k in input_names}
									UpperCAmelCase_  :				int      =      labelaid[ex[label_name]]
									yield (d, label)

			def gen_val():
						for ex in transformed_ds[datasets.Split.VALIDATION]:
									UpperCAmelCase_  :				Optional[Any]      =      {k: v for k, v in ex.items() if k in input_names}
									UpperCAmelCase_  :				List[Any]      =      labelaid[ex[label_name]]
									yield (d, label)

			def gen_test():
						for ex in transformed_ds[datasets.Split.TEST]:
									UpperCAmelCase_  :				Any      =      {k: v for k, v in ex.items() if k in input_names}
									UpperCAmelCase_  :				Union[str, Any]      =      labelaid[ex[label_name]]
									yield (d, label)

			UpperCAmelCase_  :				Dict      =      (
			    tf.data.Dataset.from_generator(
			        A__			,({k: tf.intaa for k in input_names}, tf.intaa)			,({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))			,)
			    if datasets.Split.TRAIN in transformed_ds
			    else None
			)

			if train_ds is not None:
						UpperCAmelCase_  :				Any      =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]		)		)		)

			UpperCAmelCase_  :				Tuple      =      (
			    tf.data.Dataset.from_generator(
			        A__			,({k: tf.intaa for k in input_names}, tf.intaa)			,({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))			,)
			    if datasets.Split.VALIDATION in transformed_ds
			    else None
			)

			if val_ds is not None:
						UpperCAmelCase_  :				str      =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]		)		)		)

			UpperCAmelCase_  :				Dict      =      (
			    tf.data.Dataset.from_generator(
			        A__			,({k: tf.intaa for k in input_names}, tf.intaa)			,({k: tf.TensorShape([None]		) for k in input_names}, tf.TensorShape([]		))			,)
			    if datasets.Split.TEST in transformed_ds
			    else None
			)

			if test_ds is not None:
						UpperCAmelCase_  :				Union[str, Any]      =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]		)		)		)

			return train_ds, val_ds, test_ds, labelaid


lowerCamelCase_       			=   logging.getLogger(__name__)







@dataclass
class     UpperCamelCase_ :
	__magic_name__			   =       field(metadata={'''help''': '''Which column contains the label'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''The path of the training file'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''The path of the development file'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''The path of the test file'''}  )
	__magic_name__			   =       field(
	    default=1_28       ,		metadata={
	        '''help''': (
	            '''The maximum total input sequence length after tokenization. Sequences longer '''
	            '''than this will be truncated, sequences shorter will be padded.'''
	        )
	    }       ,		)
	__magic_name__			   =       field(
	    default=__A       ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )







@dataclass
class     UpperCamelCase_ :
	__magic_name__			   =       field(
	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  )
	__magic_name__			   =       field(
	    default=__A       ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
	__magic_name__			   =       field(
	    default=__A       ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
	__magic_name__			   =       field(default=__A       ,		metadata={'''help''': '''Set this flag to use fast tokenization.'''}  )
	# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
	# or just modify its tokenizer_config.json.
	__magic_name__			   =       field(
	    default=__A       ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,		)





def        snake_case   (     ):
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.
			UpperCAmelCase_  :				Tuple      =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
			UpperCAmelCase_  :				Any      =      parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir		)
			    and os.listdir(training_args.output_dir		)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
						    " --overwrite_output_dir to overcome."		)

			# Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,datefmt="%m/%d/%Y %H:%M:%S"			,level=logging.INFO			,)
			logger.info(
			    F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1		)}, """
			    F"""16-bits training: {training_args.fpaa}"""		)
			logger.info(F"""Training/evaluation parameters {training_args}"""		)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			UpperCAmelCase_  :				Union[str, 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			,)

			UpperCAmelCase_  :				Optional[Any]      =      get_tfds(
			    train_file=data_args.train_file			,eval_file=data_args.dev_file			,test_file=data_args.test_file			,tokenizer=A__			,label_column_id=data_args.label_column_id			,max_seq_length=data_args.max_seq_length			,)

			UpperCAmelCase_  :				Dict      =      AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,num_labels=len(A__		)			,labelaid=A__			,idalabel={id: label for label, id in labelaid.items()}			,finetuning_task="text-classification"			,cache_dir=model_args.cache_dir			,)

			with training_args.strategy.scope():
						UpperCAmelCase_  :				int      =      TFAutoModelForSequenceClassification.from_pretrained(
						    model_args.model_name_or_path			,from_pt=bool(".bin" in model_args.model_name_or_path		)			,config=A__			,cache_dir=model_args.cache_dir			,)

			def compute_metrics(A__		) -> Dict:
						UpperCAmelCase_  :				int      =      np.argmax(p.predictions			,axis=1		)

						return {"acc": (preds == p.label_ids).mean()}

			# Initialize our Trainer
			UpperCAmelCase_  :				Tuple      =      TFTrainer(
			    model=A__			,args=A__			,train_dataset=A__			,eval_dataset=A__			,compute_metrics=A__			,)

			# Training
			if training_args.do_train:
						trainer.train()
						trainer.save_model()
						tokenizer.save_pretrained(training_args.output_dir		)

			# Evaluation
			UpperCAmelCase_  :				Optional[int]      =      {}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"		)
						UpperCAmelCase_  :				List[str]      =      trainer.evaluate()
						UpperCAmelCase_  :				Union[str, Any]      =      os.path.join(training_args.output_dir			,"eval_results.txt"		)

						with open(A__			,"w"		) as writer:
									logger.info("***** Eval results *****"		)

									for key, value in result.items():
												logger.info(F"""  {key} = {value}"""		)
												writer.write(F"""{key} = {value}\n"""		)

									results.update(A__		)

			return results


if __name__ == "__main__":
			main()











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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   '''▁'''

lowerCamelCase_       			=   {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCamelCase_       			=   {
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

lowerCamelCase_       			=   {
    '''facebook/xglm-564M''': 2048,
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       VOCAB_FILES_NAMES
	__magic_name__			   =       PRETRAINED_VOCAB_FILES_MAP
	__magic_name__			   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__			   =       ['''input_ids''', '''attention_mask''']





	def __init__(   self      :				int			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Union[str, Any]="<s>"			,			lowerCAmelCase_      :				Any="</s>"			,			lowerCAmelCase_      :				Tuple="</s>"			,			lowerCAmelCase_      :				List[str]="<s>"			,			lowerCAmelCase_      :				Union[str, Any]="<unk>"			,			lowerCAmelCase_      :				Any="<pad>"			,			lowerCAmelCase_      :				Optional[Dict[str, Any]] = None			,			**lowerCAmelCase_      :				List[str]			,			)		->    None:
				UpperCAmelCase_  :				Any      =      {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				UpperCAmelCase_  :				Any      =      7
				UpperCAmelCase_  :				Dict      =      [f"""<madeupword{i}>""" for i in range(self.num_madeup_words		)]

				UpperCAmelCase_  :				Optional[int]      =      kwargs.get("additional_special_tokens"			,			[]		)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=lowerCAmelCase_			,			eos_token=lowerCAmelCase_			,			unk_token=lowerCAmelCase_			,			sep_token=lowerCAmelCase_			,			cls_token=lowerCAmelCase_			,			pad_token=lowerCAmelCase_			,			sp_model_kwargs=self.sp_model_kwargs			,			**lowerCAmelCase_			,			)

				UpperCAmelCase_  :				int      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				Union[str, 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'

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

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

				UpperCAmelCase_  :				List[str]      =      len(self.sp_model		)
				UpperCAmelCase_  :				List[Any]      =      {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
				self.fairseq_tokens_to_ids.update(lowerCAmelCase_		)

				UpperCAmelCase_  :				Tuple      =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}





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





	def __setstate__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				Any		)		->    Any:
				UpperCAmelCase_  :				Tuple      =      d

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

				UpperCAmelCase_  :				int      =      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None		)		->    List[int]:

				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				UpperCAmelCase_  :				Optional[Any]      =      [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None			,			lowerCAmelCase_      :				bool = False		)		->    List[int]:

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

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				List[int]			,			lowerCAmelCase_      :				Optional[List[int]] = None		)		->    List[int]:
				UpperCAmelCase_  :				str      =      [self.sep_token_id]

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





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Any:
				return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      {self.convert_ids_to_tokens(lowerCAmelCase_		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				str		)		->    List[str]:
				return self.sp_model.encode(lowerCAmelCase_			,			out_type=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Optional[Any]		)		->    Optional[int]:
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				UpperCAmelCase_  :				Optional[int]      =      self.sp_model.PieceToId(lowerCAmelCase_		)

				# 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   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Union[str, Any]		)		->    Any:
				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   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Tuple		)		->    Optional[int]:
				UpperCAmelCase_  :				Any      =      "".join(lowerCAmelCase_		).replace(lowerCAmelCase_			,			" "		).strip()
				return out_string





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Optional[str] = None		)		->    Tuple[str]:
				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_  :				List[Any]      =      os.path.join(
				    lowerCAmelCase_			,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

				return (out_vocab_file,)



