





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    (       _lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Tuple					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			Dict					,					_lowerCAmelCase			:			Optional[int]					,					_lowerCAmelCase			:			Any = None					,					)			->    List[str]:
   A_       :					str        =      {}

   if train_file is not None:
      A_       :					int        =      [train_file]
   if eval_file is not None:
      A_       :					Union[str, Any]        =      [eval_file]
   if test_file is not None:
      A_       :					int        =      [test_file]

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

   if len(A_						) == 1:
      for k in files.keys():
         A_       :					Dict        =      ds[k].map(
             lambda _lowerCAmelCase						: 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():
         A_       :					Any        =      ds[k].map(
             lambda _lowerCAmelCase						: 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]:
         A_       :					List[Any]        =      {k: v for k, v in ex.items() if k in input_names}
         A_       :					Optional[Any]        =      labelaid[ex[label_name]]
         yield (d, label)

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

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

   A_       :					Union[str, Any]        =      (
       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:
      A_       :					Optional[Any]        =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]						)						)						)

   A_       :					List[str]        =      (
       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:
      A_       :					List[str]        =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]						)						)						)

   A_       :					int        =      (
       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:
      A_       :					Dict        =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]						)						)						)

   return train_ds, val_ds, test_ds, labelaid


_lowerCAmelCase				:   Optional[Any]						       =						logging.getLogger(__name__)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(metadata={'''help''': '''Which column contains the label'''}				)
     __UpperCamelCase            =  field(default=a_     ,					metadata={'''help''': '''The path of the training file'''}				)
     __UpperCamelCase            =  field(default=a_     ,					metadata={'''help''': '''The path of the development file'''}				)
     __UpperCamelCase            =  field(default=a_     ,					metadata={'''help''': '''The path of the test file'''}				)
     __UpperCamelCase            =  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.'''
             )
         }     ,					)
     __UpperCamelCase            =  field(
         default=a_     ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)




@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}				)
     __UpperCamelCase            =  field(
         default=a_     ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}				)
     __UpperCamelCase            =  field(
         default=a_     ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}				)
     __UpperCamelCase            =  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.
     __UpperCamelCase            =  field(
         default=a_     ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,					)




def   __snake_case    (       )			->    Union[str, Any]:
   # 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.
   A_       :					Dict        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)						)
   A_       :					Union[str, 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.

   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_       :					str        =      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					,					)

   A_       :					Optional[Any]        =      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():
      A_       :					Dict        =      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(_lowerCAmelCase			:			Optional[Any]						) -> Dict:
      A_       :					List[Any]        =      np.argmax(p.predictions					,					axis=1						)

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

   # Initialize our Trainer
   A_       :					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
   A_       :					List[Any]        =      {}
   if training_args.do_eval:
      logger.info("*** Evaluate ***"						)
      A_       :					str        =      trainer.evaluate()
      A_       :					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"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Any				 =						{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"t5"
					lowercase__												=			["past_key_values"]
					lowercase__												=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(			self     :							Tuple  ,lowercase_     :							Tuple=3_2_1_2_8  ,lowercase_     :							Union[str, Any]=5_1_2  ,lowercase_     :							Dict=6_4  ,lowercase_     :							Any=2_0_4_8  ,lowercase_     :							Dict=6  ,lowercase_     :							Dict=None  ,lowercase_     :							Dict=8  ,lowercase_     :							Optional[int]=3_2  ,lowercase_     :							List[Any]=1_2_8  ,lowercase_     :							Any=0.1  ,lowercase_     :							Optional[Any]=1E-6  ,lowercase_     :							Any=1.0  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Tuple=True  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Tuple=0  ,lowercase_     :							Optional[Any]=1  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Tuple    							=      vocab_size
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Union[str, Any]    							=      d_kv
						lowerCAmelCase__      :		List[Any]    							=      d_ff
						lowerCAmelCase__      :		int    							=      num_layers
						lowerCAmelCase__      :		Union[str, Any]    							=      (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowerCAmelCase__      :		Any    							=      num_heads
						lowerCAmelCase__      :		Optional[int]    							=      relative_attention_num_buckets
						lowerCAmelCase__      :		Union[str, Any]    							=      relative_attention_max_distance
						lowerCAmelCase__      :		List[Any]    							=      dropout_rate
						lowerCAmelCase__      :		str    							=      layer_norm_epsilon
						lowerCAmelCase__      :		int    							=      initializer_factor
						lowerCAmelCase__      :		Dict    							=      feed_forward_proj
						lowerCAmelCase__      :		int    							=      use_cache

						lowerCAmelCase__      :		int    							=      self.feed_forward_proj.split('''-'''						)
						lowerCAmelCase__      :		str    							=      act_info[-1]
						lowerCAmelCase__      :		Tuple    							=      act_info[0] == '''gated'''

						if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
							    '''\'gated-gelu\' or \'relu\''''						)

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__      :		Tuple    							=      '''gelu_new'''

						super().__init__(
						    pad_token_id=lowercase_  ,eos_token_id=lowercase_  ,is_encoder_decoder=lowercase_  ,**lowercase_  ,)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		int    							=      {
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
							lowerCAmelCase__      :		Union[str, Any]    							=      '''past_encoder_sequence + sequence'''
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch'''}
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''decoder_sequence'''}
							lowerCAmelCase__      :		Any    							=      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
							self.fill_with_past_key_values_(lowercase_  ,direction='''inputs'''						)

						return common_inputs







					@property
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						return 1_3




