'''simple docstring'''

import argparse
import logging
import os
import re

import tensorflow as tf

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


UpperCamelCase__  :						Union[str, Any]					=      logging.getLogger(__name__)

UpperCamelCase__  :						str					=      tf.data.AUTOTUNE







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Tuple 					=	argparse.ArgumentParser(description='''Train a masked language model on TPU.'''     )
   parser.add_argument(
       '''--pretrained_model_config'''	,      type=_A	,      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=_A	,      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=_A	,      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=_A	,      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=_A	,      help='''Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'''	,      )

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

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

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

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

   parser.add_argument(
       '''--max_length'''	,      type=_A	,      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=_A	,      default=0.1_5	,      help='''Fraction of tokens to mask during training.'''	,      )

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

   UpperCAmelCase__				:					int 					=	parser.parse_args()
   return args







def __UpperCamelCase( _A       :					Dict     ):
   '''simple docstring'''





   try:
      if args.tpu_name:
         UpperCAmelCase__				:					List[Any] 					=	tf.distribute.cluster_resolver.TPUClusterResolver(
             args.tpu_name	,      zone=args.tpu_zone	,      project=args.gcp_project     )
      else:
         UpperCAmelCase__				:					Optional[int] 					=	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(_A     )
   tf.tpu.experimental.initialize_tpu_system(_A     )

   return tpu







def __UpperCamelCase( _A       :					Dict     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   for file in file_list:
      UpperCAmelCase__				:					List[Any] 					=	file.split('''/'''     )[-1]
      UpperCAmelCase__				:					Union[str, Any] 					=	re.search(R'''-\d+-(\d+)\.tfrecord'''	,      _A     ).group(1     )
      UpperCAmelCase__				:					Optional[int] 					=	int(_A     )
      num_samples += sample_count

   return num_samples







def __UpperCamelCase( _A       :					Optional[Any]	,      _A       :					Optional[int]	,      _A       :					Optional[int]	,      _A       :					int	,      _A       :					Optional[Any]	,      _A       :					Tuple=None     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	count_samples(_A     )
   UpperCAmelCase__				:					Tuple 					=	tf.data.Dataset.from_tensor_slices(_A     )
   if shuffle:
      UpperCAmelCase__				:					Optional[int] 					=	dataset.shuffle(len(_A     )     )
   UpperCAmelCase__				:					List[Any] 					=	tf.data.TFRecordDataset(_A	,      num_parallel_reads=_A     )
   # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
   UpperCAmelCase__				:					str 					=	dataset.apply(tf.data.experimental.assert_cardinality(_A     )     )
   UpperCAmelCase__				:					str 					=	dataset.map(_A	,      num_parallel_calls=_A     )
   if shuffle:
      assert shuffle_buffer_size is not None
      UpperCAmelCase__				:					List[str] 					=	dataset.shuffle(args.shuffle_buffer_size     )
   UpperCAmelCase__				:					str 					=	dataset.batch(_A	,      drop_remainder=_A     )
   UpperCAmelCase__				:					str 					=	dataset.map(_A	,      num_parallel_calls=_A     )
   UpperCAmelCase__				:					Tuple 					=	dataset.prefetch(_A     )
   return dataset







def __UpperCamelCase( _A       :					List[str]     ):
   '''simple docstring'''





   if not args.no_tpu:
      UpperCAmelCase__				:					Tuple 					=	initialize_tpu(_A     )
      UpperCAmelCase__				:					List[str] 					=	tf.distribute.TPUStrategy(_A     )
   else:
      UpperCAmelCase__				:					Optional[int] 					=	tf.distribute.OneDeviceStrategy(device='''/gpu:0'''     )

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

   UpperCAmelCase__				:					str 					=	AutoTokenizer.from_pretrained(args.tokenizer     )
   UpperCAmelCase__				:					Optional[Any] 					=	AutoConfig.from_pretrained(args.pretrained_model_config     )
   UpperCAmelCase__				:					Optional[int] 					=	tokenizer.vocab_size

   UpperCAmelCase__				:					str 					=	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}.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	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}.'''     )

   UpperCAmelCase__				:					Union[str, Any] 					=	count_samples(_A     )

   UpperCAmelCase__				:					Dict 					=	num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
   UpperCAmelCase__				:					int 					=	steps_per_epoch * args.num_epochs

   with strategy.scope():
      UpperCAmelCase__				:					Dict 					=	TFAutoModelForMaskedLM.from_config(_A     )
      model(model.dummy_inputs     )  # Pass some dummy inputs through the model to ensure all the weights are built
      UpperCAmelCase__				:					List[str] 					=	create_optimizer(
          num_train_steps=_A	,      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=_A	,      metrics=['''accuracy''']     )

   def decode_fn(_A       :					Tuple     ):
      UpperCAmelCase__				:					Tuple 					=	{
          "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(_A	,      _A     )

   # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
   # use their methods in our data pipeline.
   UpperCAmelCase__				:					List[str] 					=	DataCollatorForLanguageModeling(
       tokenizer=_A	,      mlm_probability=args.mlm_probability	,      mlm=_A	,      return_tensors='''tf'''     )

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

   UpperCAmelCase__				:					Optional[Any] 					=	args.per_replica_batch_size * strategy.num_replicas_in_sync

   UpperCAmelCase__				:					Dict 					=	prepare_dataset(
       _A	,      decode_fn=_A	,      mask_fn=_A	,      batch_size=_A	,      shuffle=_A	,      shuffle_buffer_size=args.shuffle_buffer_size	,      )

   UpperCAmelCase__				:					Tuple 					=	prepare_dataset(
       _A	,      decode_fn=_A	,      mask_fn=_A	,      batch_size=_A	,      shuffle=_A	,      )

   UpperCAmelCase__				:					List[Any] 					=	[]
   if args.hub_model_id:
      callbacks.append(
          PushToHubCallback(output_dir=args.output_dir	,      hub_model_id=args.hub_model_id	,      tokenizer=_A     )     )

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

   model.save_pretrained(args.output_dir     )


if __name__ == "__main__":
  UpperCamelCase__  :						List[Any]					=      parse_args()
  main(args)








"""simple docstring"""



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    (KDPMaDiscreteScheduler,)
   __UpperCAmelCase					:			List[Any]				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




       snake_case       : List[str]    =       {
           "num_train_timesteps": 1100,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,      [0.0002, 0.002, 0.02]		):
           self.check_over_configs(beta_start=UpperCamelCase__		,      beta_end=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : str    =       self.scheduler_classes[0]
       snake_case       : str    =       self.get_scheduler_config(prediction_type="v_prediction"		)
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : List[str]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : Optional[Any]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : int    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : int    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Optional[Any]    =       output.prev_sample

       snake_case       : Optional[Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Any    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.6934e-07		) < 1e-2
           assert abs(result_mean.item() - 6.1112e-10		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 4.693_4286_5017_0972e-07		) < 1e-2
           assert abs(result_mean.item() - 0.0002		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Optional[Any]    =       self.scheduler_classes[0]
       snake_case       : Optional[int]    =       self.get_scheduler_config()
       snake_case       : Dict    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		)

       snake_case       : List[Any]    =       self.dummy_model()
       snake_case       : Union[str, Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case       : List[str]    =       sample.to(UpperCamelCase__		)

       for i, t in enumerate(scheduler.timesteps		):
           snake_case       : List[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Optional[int]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : int    =       output.prev_sample

       snake_case       : int    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Union[str, Any]    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						Optional[int]:

       '''simple docstring'''




       if torch_device == "mps":
           return
       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Any    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**UpperCamelCase__		)

       scheduler.set_timesteps(self.num_inference_steps		,      device=UpperCamelCase__		)

       snake_case       : Optional[Any]    =       self.dummy_model()
       snake_case       : Optional[int]    =       self.dummy_sample_deter.to(UpperCamelCase__		) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
           snake_case       : Optional[Any]    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : Any    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           snake_case       : List[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		)
           snake_case       : Dict    =       output.prev_sample

       snake_case       : Dict    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : str    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       if str(UpperCamelCase__		).startswith("cpu"		):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3
       else:
           # CUDA
           assert abs(result_sum.item() - 20.4125		) < 1e-2
           assert abs(result_mean.item() - 0.0266		) < 1e-3





