

'''simple docstring'''
import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowerCamelCase :      Any					 =     logging.getLogger(__name__)

lowerCamelCase :      Optional[Any]					 =     tf.data.AUTOTUNE


def        _lowerCAmelCase					(						)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =argparse.ArgumentParser(description='Train a masked language model on TPU.'				)
     parser.add_argument(
         '--pretrained_model_config'							, type=_UpperCamelCase							, default='roberta-base'							, help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'							, )
     parser.add_argument(
         '--tokenizer'							, type=_UpperCamelCase							, default='unigram-tokenizer-wikitext'							, help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'							, )

     parser.add_argument(
         '--per_replica_batch_size'							, type=_UpperCamelCase							, default=8							, help='Batch size per TPU core.'							, )

     parser.add_argument(
         '--no_tpu'							, action='store_true'							, help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'							, )

     parser.add_argument(
         '--tpu_name'							, type=_UpperCamelCase							, help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'							, default='local'							, )

     parser.add_argument(
         '--tpu_zone'							, type=_UpperCamelCase							, help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'							, )

     parser.add_argument(
         '--gcp_project'							, type=_UpperCamelCase							, help='Google cloud project name. Only used for non-Colab TPU nodes.'				)

     parser.add_argument(
         '--bfloat16'							, action='store_true'							, help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'							, )

     parser.add_argument(
         '--train_dataset'							, type=_UpperCamelCase							, help='Path to training dataset to load. If the path begins with `gs://`'
         ' then the dataset will be loaded from a Google Cloud Storage bucket.'							, )

     parser.add_argument(
         '--shuffle_buffer_size'							, type=_UpperCamelCase							, default=2**18							, help='Size of the shuffle buffer (in samples)'							, )

     parser.add_argument(
         '--eval_dataset'							, type=_UpperCamelCase							, help='Path to evaluation dataset to load. If the path begins with `gs://`'
         ' then the dataset will be loaded from a Google Cloud Storage bucket.'							, )

     parser.add_argument(
         '--num_epochs'							, type=_UpperCamelCase							, default=1							, help='Number of epochs to train for.'							, )

     parser.add_argument(
         '--learning_rate'							, type=_UpperCamelCase							, default=1E-4							, help='Learning rate to use for training.'							, )

     parser.add_argument(
         '--weight_decay_rate'							, type=_UpperCamelCase							, default=1E-3							, help='Weight decay rate to use for training.'							, )

     parser.add_argument(
         '--max_length'							, type=_UpperCamelCase							, default=5_12							, help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'							, )

     parser.add_argument(
         '--mlm_probability'							, type=_UpperCamelCase							, default=0.15							, help='Fraction of tokens to mask during training.'							, )

     parser.add_argument('--output_dir'							, type=_UpperCamelCase							, required=_UpperCamelCase							, help='Path to save model checkpoints to.'				)
     parser.add_argument('--hub_model_id'							, type=_UpperCamelCase							, help='Model ID to upload to on the Hugging Face Hub.'				)

     _SCREAMING_SNAKE_CASE							    =parser.parse_args()
     return args


def        _lowerCAmelCase					(						_UpperCamelCase						:     Any				)      ->			int:
     """simple docstring"""




     try:
          if args.tpu_name:
               _SCREAMING_SNAKE_CASE							    =tf.distribute.cluster_resolver.TPUClusterResolver(
                   args.tpu_name							, zone=args.tpu_zone							, project=args.gcp_project				)
          else:
               _SCREAMING_SNAKE_CASE							    =tf.distribute.cluster_resolver.TPUClusterResolver()
     except ValueError:
          raise RuntimeError(
              'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
              '--gcp_project. When running on a TPU VM, use --tpu_name local.'				)

     tf.config.experimental_connect_to_cluster(_UpperCamelCase				)
     tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase				)

     return tpu


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =0
     for file in file_list:
          _SCREAMING_SNAKE_CASE							    =file.split('/'				)[-1]
          _SCREAMING_SNAKE_CASE							    =re.search(r'-\d+-(\d+)\.tfrecord'							, _UpperCamelCase				).group(1				)
          _SCREAMING_SNAKE_CASE							    =int(_UpperCamelCase				)
          num_samples += sample_count

     return num_samples


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     Dict							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Any							, _UpperCamelCase						:     str=None				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =count_samples(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =tf.data.Dataset.from_tensor_slices(_UpperCamelCase				)
     if shuffle:
          _SCREAMING_SNAKE_CASE							    =dataset.shuffle(len(_UpperCamelCase				)				)
     _SCREAMING_SNAKE_CASE							    =tf.data.TFRecordDataset(_UpperCamelCase							, num_parallel_reads=_UpperCamelCase				)
     # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
     _SCREAMING_SNAKE_CASE							    =dataset.apply(tf.data.experimental.assert_cardinality(_UpperCamelCase				)				)
     _SCREAMING_SNAKE_CASE							    =dataset.map(_UpperCamelCase							, num_parallel_calls=_UpperCamelCase				)
     if shuffle:
          assert shuffle_buffer_size is not None
          _SCREAMING_SNAKE_CASE							    =dataset.shuffle(args.shuffle_buffer_size				)
     _SCREAMING_SNAKE_CASE							    =dataset.batch(_UpperCamelCase							, drop_remainder=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =dataset.map(_UpperCamelCase							, num_parallel_calls=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =dataset.prefetch(_UpperCamelCase				)
     return dataset


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]				)      ->			Tuple:
     """simple docstring"""




     if not args.no_tpu:
          _SCREAMING_SNAKE_CASE							    =initialize_tpu(_UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =tf.distribute.TPUStrategy(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =tf.distribute.OneDeviceStrategy(device='/gpu:0'				)

     if args.bfloataa:
          tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'				)

     _SCREAMING_SNAKE_CASE							    =AutoTokenizer.from_pretrained(args.tokenizer				)
     _SCREAMING_SNAKE_CASE							    =AutoConfig.from_pretrained(args.pretrained_model_config				)
     _SCREAMING_SNAKE_CASE							    =tokenizer.vocab_size

     _SCREAMING_SNAKE_CASE							    =tf.io.gfile.glob(os.path.join(args.train_dataset							, '*.tfrecord'				)				)
     if not training_records:
          raise ValueError(f"No .tfrecord files found in {args.train_dataset}."				)
     _SCREAMING_SNAKE_CASE							    =tf.io.gfile.glob(os.path.join(args.eval_dataset							, '*.tfrecord'				)				)
     if not eval_records:
          raise ValueError(f"No .tfrecord files found in {args.eval_dataset}."				)

     _SCREAMING_SNAKE_CASE							    =count_samples(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
     _SCREAMING_SNAKE_CASE							    =steps_per_epoch * args.num_epochs

     with strategy.scope():
          _SCREAMING_SNAKE_CASE							    =TFAutoModelForMaskedLM.from_config(_UpperCamelCase				)
          model(model.dummy_inputs				)  # Pass some dummy inputs through the model to ensure all the weights are built
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =create_optimizer(
              num_train_steps=_UpperCamelCase							, num_warmup_steps=total_train_steps // 20							, init_lr=args.learning_rate							, weight_decay_rate=args.weight_decay_rate							, )

          # Transformers models compute the right loss for their task by default when labels are passed, and will
          # use this for training unless you specify your own loss function in compile().
          model.compile(optimizer=_UpperCamelCase							, metrics=['accuracy']				)

     def decode_fn(_UpperCamelCase						:     Any				):
          _SCREAMING_SNAKE_CASE							    ={
              'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa							, shape=(args.max_length,)				),
              'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa							, shape=(args.max_length,)				),
          }
          return tf.io.parse_single_example(_UpperCamelCase							, _UpperCamelCase				)

     # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
     # use their methods in our data pipeline.
     _SCREAMING_SNAKE_CASE							    =DataCollatorForLanguageModeling(
         tokenizer=_UpperCamelCase							, mlm_probability=args.mlm_probability							, mlm=_UpperCamelCase							, return_tensors='tf'				)

     def mask_with_collator(_UpperCamelCase						:     List[str]				):
          # TF really needs an isin() function
          _SCREAMING_SNAKE_CASE							    =(
              ~tf.cast(batch['attention_mask']							, tf.bool				)
              | (batch['input_ids'] == tokenizer.cls_token_id)
              | (batch['input_ids'] == tokenizer.sep_token_id)
          )
          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =data_collator.tf_mask_tokens(
              batch['input_ids']							, vocab_size=len(_UpperCamelCase				)							, mask_token_id=tokenizer.mask_token_id							, special_tokens_mask=_UpperCamelCase							, )
          return batch

     _SCREAMING_SNAKE_CASE							    =args.per_replica_batch_size * strategy.num_replicas_in_sync

     _SCREAMING_SNAKE_CASE							    =prepare_dataset(
         _UpperCamelCase							, decode_fn=_UpperCamelCase							, mask_fn=_UpperCamelCase							, batch_size=_UpperCamelCase							, shuffle=_UpperCamelCase							, shuffle_buffer_size=args.shuffle_buffer_size							, )

     _SCREAMING_SNAKE_CASE							    =prepare_dataset(
         _UpperCamelCase							, decode_fn=_UpperCamelCase							, mask_fn=_UpperCamelCase							, batch_size=_UpperCamelCase							, shuffle=_UpperCamelCase							, )

     _SCREAMING_SNAKE_CASE							    =[]
     if args.hub_model_id:
          callbacks.append(
              PushToHubCallback(output_dir=args.output_dir							, hub_model_id=args.hub_model_id							, tokenizer=_UpperCamelCase				)				)

     model.fit(
         _UpperCamelCase							, validation_data=_UpperCamelCase							, epochs=args.num_epochs							, callbacks=_UpperCamelCase							, )

     model.save_pretrained(args.output_dir				)


if __name__ == "__main__":
     lowerCamelCase :      Union[str, Any]					 =     parse_args()
     main(args)



'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase :      Dict					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'deta'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
  }





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=None							,					_a      :	int=900							,					_a      :	Optional[Any]=2048							,					_a      :	int=6							,					_a      :	Tuple=2048							,					_a      :	Optional[int]=8							,					_a      :	Any=6							,					_a      :	str=1024							,					_a      :	int=8							,					_a      :	int=0.0							,					_a      :	Optional[Any]=True							,					_a      :	Tuple="relu"							,					_a      :	Union[str, Any]=256							,					_a      :	Tuple=0.1							,					_a      :	str=0.0							,					_a      :	Dict=0.0							,					_a      :	Tuple=0.02							,					_a      :	Union[str, Any]=1.0							,					_a      :	Any=True							,					_a      :	Tuple=False							,					_a      :	List[Any]="sine"							,					_a      :	str=5							,					_a      :	List[Any]=4							,					_a      :	str=4							,					_a      :	Union[str, Any]=True							,					_a      :	Optional[int]=300							,					_a      :	Dict=True							,					_a      :	List[Any]=True							,					_a      :	List[Any]=1							,					_a      :	List[str]=5							,					_a      :	int=2							,					_a      :	Dict=1							,					_a      :	str=1							,					_a      :	Optional[Any]=5							,					_a      :	Union[str, Any]=2							,					_a      :	List[str]=0.1							,					_a      :	List[Any]=0.25							,					**_a      :	Union[str, Any]							,					)	->    List[str]:





       '''simple docstring'''






       if backbone_config is None:
            logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.' )
            _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4'] )
       else:
            if isinstance(_a							,					_a ):
                 _SCREAMING_SNAKE_CASE							    =backbone_config.pop('model_type' )
                 _SCREAMING_SNAKE_CASE							    =CONFIG_MAPPING[backbone_model_type]
                 _SCREAMING_SNAKE_CASE							    =config_class.from_dict(_a )

       _SCREAMING_SNAKE_CASE							    =backbone_config
       _SCREAMING_SNAKE_CASE							    =num_queries
       _SCREAMING_SNAKE_CASE							    =max_position_embeddings
       _SCREAMING_SNAKE_CASE							    =d_model
       _SCREAMING_SNAKE_CASE							    =encoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =encoder_layers
       _SCREAMING_SNAKE_CASE							    =encoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =decoder_ffn_dim
       _SCREAMING_SNAKE_CASE							    =decoder_layers
       _SCREAMING_SNAKE_CASE							    =decoder_attention_heads
       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std
       _SCREAMING_SNAKE_CASE							    =init_xavier_std
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =auxiliary_loss
       _SCREAMING_SNAKE_CASE							    =position_embedding_type
       # deformable attributes
       _SCREAMING_SNAKE_CASE							    =num_feature_levels
       _SCREAMING_SNAKE_CASE							    =encoder_n_points
       _SCREAMING_SNAKE_CASE							    =decoder_n_points
       _SCREAMING_SNAKE_CASE							    =two_stage
       _SCREAMING_SNAKE_CASE							    =two_stage_num_proposals
       _SCREAMING_SNAKE_CASE							    =with_box_refine
       _SCREAMING_SNAKE_CASE							    =assign_first_stage
       if two_stage is True and with_box_refine is False:
            raise ValueError('If two_stage is True, with_box_refine must be True.' )
       # Hungarian matcher
       _SCREAMING_SNAKE_CASE							    =class_cost
       _SCREAMING_SNAKE_CASE							    =bbox_cost
       _SCREAMING_SNAKE_CASE							    =giou_cost
       # Loss coefficients
       _SCREAMING_SNAKE_CASE							    =mask_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =dice_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =bbox_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =giou_loss_coefficient
       _SCREAMING_SNAKE_CASE							    =eos_coefficient
       _SCREAMING_SNAKE_CASE							    =focal_alpha
       super().__init__(is_encoder_decoder=_a							,					**_a )





  @property
  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






       return self.encoder_attention_heads





  @property
  def 						A  (		self      :	List[Any] )	->    int:





       '''simple docstring'''






       return self.d_model





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =copy.deepcopy(self.__dict__ )
       _SCREAMING_SNAKE_CASE							    =self.backbone_config.to_dict()
       _SCREAMING_SNAKE_CASE							    =self.__class__.model_type
       return output
