






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
					    FlaxRobertaPreLayerNormForCausalLM,
					    FlaxRobertaPreLayerNormForMaskedLM,
					    FlaxRobertaPreLayerNormForMultipleChoice,
					    FlaxRobertaPreLayerNormForQuestionAnswering,
					    FlaxRobertaPreLayerNormForSequenceClassification,
					    FlaxRobertaPreLayerNormForTokenClassification,
					    FlaxRobertaPreLayerNormModel,
					)
class 						lowerCamelCase_				(      unittest.TestCase ):







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=7       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=9_9       ,					__lowerCAmelCase=3_2       ,					__lowerCAmelCase=5       ,					__lowerCAmelCase=4       ,					__lowerCAmelCase=3_7       ,					__lowerCAmelCase="gelu"       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=0.1       ,					__lowerCAmelCase=5_1_2       ,					__lowerCAmelCase=1_6       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=4       ,					):
								"""simple docstring"""
								__magic_name__		:str        =      parent
								__magic_name__		:Dict        =      batch_size
								__magic_name__		:Dict        =      seq_length
								__magic_name__		:str        =      is_training
								__magic_name__		:Any        =      use_attention_mask
								__magic_name__		:Dict        =      use_token_type_ids
								__magic_name__		:Any        =      use_labels
								__magic_name__		:Union[str, Any]        =      vocab_size
								__magic_name__		:int        =      hidden_size
								__magic_name__		:Tuple        =      num_hidden_layers
								__magic_name__		:Dict        =      num_attention_heads
								__magic_name__		:Optional[int]        =      intermediate_size
								__magic_name__		:Tuple        =      hidden_act
								__magic_name__		:str        =      hidden_dropout_prob
								__magic_name__		:Union[str, Any]        =      attention_probs_dropout_prob
								__magic_name__		:int        =      max_position_embeddings
								__magic_name__		:List[str]        =      type_vocab_size
								__magic_name__		:Optional[int]        =      type_sequence_label_size
								__magic_name__		:Union[str, Any]        =      initializer_range
								__magic_name__		:int        =      num_choices







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      ids_tensor([self.batch_size, self.seq_length]       ,					self.vocab_size     )

								__magic_name__		:List[str]        =      None
								if self.use_attention_mask:
												__magic_name__		:Optional[int]        =      random_attention_mask([self.batch_size, self.seq_length]     )

								__magic_name__		:Optional[int]        =      None
								if self.use_token_type_ids:
												__magic_name__		:Optional[Any]        =      ids_tensor([self.batch_size, self.seq_length]       ,					self.type_vocab_size     )

								__magic_name__		:Union[str, Any]        =      RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size       ,					hidden_size=self.hidden_size       ,					num_hidden_layers=self.num_hidden_layers       ,					num_attention_heads=self.num_attention_heads       ,					intermediate_size=self.intermediate_size       ,					hidden_act=self.hidden_act       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					max_position_embeddings=self.max_position_embeddings       ,					type_vocab_size=self.type_vocab_size       ,					is_decoder=__lowerCAmelCase       ,					initializer_range=self.initializer_range       ,					)

								return config, input_ids, token_type_ids, attention_mask







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      self.prepare_config_and_inputs()
								__magic_name__		:Tuple        =      config_and_inputs
								__magic_name__		:Optional[int]        =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      self.prepare_config_and_inputs()
								__magic_name__		:Optional[int]        =      config_and_inputs

								__magic_name__		:List[Any]        =      True
								__magic_name__		:Any        =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
								__magic_name__		:str        =      ids_tensor([self.batch_size, self.seq_length]       ,					vocab_size=2     )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 						lowerCamelCase_				(      lowerCamelCase			,				unittest.TestCase ):
				a__					       =  True

				a__					       =  (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      FlaxRobertaPreLayerNormModelTester(self     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_class_name in self.all_model_classes:
												__magic_name__		:Any        =      model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,					from_pt=__lowerCAmelCase     )
												__magic_name__		:Optional[int]        =      model(np.ones((1, 1)     )     )
												self.assertIsNotNone(__lowerCAmelCase     )







@require_flax
class 						lowerCamelCase_				(      unittest.TestCase ):







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,					from_pt=__lowerCAmelCase     )

								__magic_name__		:Tuple        =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       ,					dtype=jnp.intaa     )
								__magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase     )[0]
								__magic_name__		:Tuple        =      [1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape     )       ,					__lowerCAmelCase     )
								# compare the actual values for a slice.
								__magic_name__		:int        =      np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]       ,					dtype=np.floataa     )
								self.assertTrue(np.allclose(output[:, :3, :3]       ,					__lowerCAmelCase       ,					atol=1E-4     )     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''       ,					from_pt=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       ,					dtype=jnp.intaa     )
								__magic_name__		:Optional[int]        =      model(__lowerCAmelCase     )[0]
								# compare the actual values for a slice.
								__magic_name__		:Any        =      np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]       ,					dtype=np.floataa     )
								self.assertTrue(np.allclose(output[:, :3, :3]       ,					__lowerCAmelCase       ,					atol=1E-4     )     )














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      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case,  snake_case = None,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      {}

				if train_file is not None:
								__magic_name__		:List[Any]        =      [train_file]
				if eval_file is not None:
								__magic_name__		:int        =      [eval_file]
				if test_file is not None:
								__magic_name__		:Any        =      [test_file]

				__magic_name__		:Optional[Any]        =      datasets.load_dataset('''csv''',  data_files=snake_case )
				__magic_name__		:List[Any]        =      list(ds[list(files.keys() )[0]].features.keys() )
				__magic_name__		:Optional[int]        =      features_name.pop(snake_case )
				__magic_name__		:List[str]        =      list(set(ds[list(files.keys() )[0]][label_name] ) )
				__magic_name__		:List[Any]        =      {label: i for i, label in enumerate(snake_case )}
				__magic_name__		:Optional[Any]        =      tokenizer.model_input_names
				__magic_name__		:Any        =      {}

				if len(snake_case ) == 1:
								for k in files.keys():
												__magic_name__		:Union[str, Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        example[features_name[0]],  truncation=snake_case,  max_length=snake_case,  padding='''max_length''' ),  batched=snake_case,  )
				elif len(snake_case ) == 2:
								for k in files.keys():
												__magic_name__		:Optional[Any]        =      ds[k].map(
												    lambda snake_case : tokenizer.batch_encode_plus(
												        (example[features_name[0]], example[features_name[1]]),  truncation=snake_case,  max_length=snake_case,  padding='''max_length''',  ),  batched=snake_case,  )

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

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

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

				__magic_name__		:Optional[Any]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({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:
								__magic_name__		:Optional[int]        =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

				__magic_name__		:Any        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({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:
								__magic_name__		:str        =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

				__magic_name__		:Optional[int]        =      (
				    tf.data.Dataset.from_generator(
				        snake_case,  ({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:
								__magic_name__		:Dict        =      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

				return train_ds, val_ds, test_ds, labelaid


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.getLogger(__name__)
@dataclass
class 						lowerCamelCase_				:
				a__					       =  field(metadata={'''help''': '''Which column contains the label'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the training file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the development file'''} )
				a__					       =  field(default=lowerCamelCase			,				metadata={'''help''': '''The path of the test file'''} )
				a__					       =  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.'''
				        )
				    }			,				)
				a__					       =  field(
				    default=lowerCamelCase			,				metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )







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







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
				__magic_name__     ,				__magic_name__     ,				__magic_name__		: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.

				__magic_name__		: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,  )

				__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:int        =      get_tfds(
				    train_file=data_args.train_file,  eval_file=data_args.dev_file,  test_file=data_args.test_file,  tokenizer=snake_case,  label_column_id=data_args.label_column_id,  max_seq_length=data_args.max_seq_length,  )

				__magic_name__		:Optional[Any]        =      AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path,  num_labels=len(snake_case ),  labelaid=snake_case,  idalabel={id: label for label, id in labelaid.items()},  finetuning_task='''text-classification''',  cache_dir=model_args.cache_dir,  )

				with training_args.strategy.scope():
								__magic_name__		:Dict        =      TFAutoModelForSequenceClassification.from_pretrained(
								    model_args.model_name_or_path,  from_pt=bool('''.bin''' in model_args.model_name_or_path ),  config=snake_case,  cache_dir=model_args.cache_dir,  )

				def compute_metrics(snake_case ) -> Dict:
								__magic_name__		:str        =      np.argmax(p.predictions,  axis=1 )

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

				# Initialize our Trainer
				__magic_name__		:str        =      TFTrainer(
				    model=snake_case,  args=snake_case,  train_dataset=snake_case,  eval_dataset=snake_case,  compute_metrics=snake_case,  )

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

				# Evaluation
				__magic_name__		:Optional[Any]        =      {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )
								__magic_name__		:int        =      trainer.evaluate()
								__magic_name__		:Dict        =      os.path.join(training_args.output_dir,  '''eval_results.txt''' )

								with open(snake_case,  '''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(snake_case )

				return results


if __name__ == "__main__":
					main()






