


'''simple docstring'''






import argparse
import logging
import os
import re

import tensorflow as tf

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


lowercase							=logging.getLogger(__name__)

lowercase							=tf.data.AUTOTUNE





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =argparse.ArgumentParser(description='Train a masked language model on TPU.'							)
   parser.add_argument(
       '--pretrained_model_config'	,   type=lowerCamelCase__	,   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=lowerCamelCase__	,   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=lowerCamelCase__	,   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=lowerCamelCase__	,   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=lowerCamelCase__	,   help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'	,   )

   parser.add_argument(
       '--gcp_project'	,   type=lowerCamelCase__	,   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=lowerCamelCase__	,   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=lowerCamelCase__	,   default=2**1_8	,   help='Size of the shuffle buffer (in samples)'	,   )

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

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

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

   parser.add_argument(
       '--max_length'	,   type=lowerCamelCase__	,   default=5_1_2	,   help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'	,   )

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

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

   _UpperCAmelCase       :       int							   =parser.parse_args()
   return args





def lowerCamelCase__      (						__lowerCamelCase	:							List[str]							):







   '''simple docstring'''




   try:
      if args.tpu_name:
         _UpperCAmelCase       :       Union[str, Any]							   =tf.distribute.cluster_resolver.TPUClusterResolver(
             args.tpu_name	,   zone=args.tpu_zone	,   project=args.gcp_project							)
      else:
         _UpperCAmelCase       :       List[str]							   =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(lowerCamelCase__							)
   tf.tpu.experimental.initialize_tpu_system(lowerCamelCase__							)

   return tpu





def lowerCamelCase__      (						__lowerCamelCase	:							List[str]							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   =0
   for file in file_list:
      _UpperCAmelCase       :       Tuple							   =file.split('/'							)[-1]
      _UpperCAmelCase       :       Optional[int]							   =re.search(R'-\d+-(\d+)\.tfrecord'	,   lowerCamelCase__							).group(1							)
      _UpperCAmelCase       :       Optional[Any]							   =int(lowerCamelCase__							)
      num_samples += sample_count

   return num_samples





def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Dict	,   __lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							int=None							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =count_samples(lowerCamelCase__							)
   _UpperCAmelCase       :       Optional[Any]							   =tf.data.Dataset.from_tensor_slices(lowerCamelCase__							)
   if shuffle:
      _UpperCAmelCase       :       Any							   =dataset.shuffle(len(lowerCamelCase__							)							)
   _UpperCAmelCase       :       int							   =tf.data.TFRecordDataset(lowerCamelCase__	,   num_parallel_reads=lowerCamelCase__							)
   # 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(lowerCamelCase__							)							)
   _UpperCAmelCase       :       Union[str, Any]							   =dataset.map(lowerCamelCase__	,   num_parallel_calls=lowerCamelCase__							)
   if shuffle:
      assert shuffle_buffer_size is not None
      _UpperCAmelCase       :       Dict							   =dataset.shuffle(args.shuffle_buffer_size							)
   _UpperCAmelCase       :       Optional[Any]							   =dataset.batch(lowerCamelCase__	,   drop_remainder=lowerCamelCase__							)
   _UpperCAmelCase       :       Dict							   =dataset.map(lowerCamelCase__	,   num_parallel_calls=lowerCamelCase__							)
   _UpperCAmelCase       :       Dict							   =dataset.prefetch(lowerCamelCase__							)
   return dataset





def lowerCamelCase__      (						__lowerCamelCase	:							Any							):







   '''simple docstring'''




   if not args.no_tpu:
      _UpperCAmelCase       :       Dict							   =initialize_tpu(lowerCamelCase__							)
      _UpperCAmelCase       :       List[str]							   =tf.distribute.TPUStrategy(lowerCamelCase__							)
   else:
      _UpperCAmelCase       :       Union[str, Any]							   =tf.distribute.OneDeviceStrategy(device='/gpu:0'							)

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

   _UpperCAmelCase       :       List[Any]							   =AutoTokenizer.from_pretrained(args.tokenizer							)
   _UpperCAmelCase       :       int							   =AutoConfig.from_pretrained(args.pretrained_model_config							)
   _UpperCAmelCase       :       Union[str, Any]							   =tokenizer.vocab_size

   _UpperCAmelCase       :       Union[str, Any]							   =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       :       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(lowerCamelCase__							)

   _UpperCAmelCase       :       Tuple							   =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       :       List[Any]							   =TFAutoModelForMaskedLM.from_config(lowerCamelCase__							)
      model(model.dummy_inputs							)  # Pass some dummy inputs through the model to ensure all the weights are built
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Union[str, Any]							   =create_optimizer(
          num_train_steps=lowerCamelCase__	,   num_warmup_steps=total_train_steps // 2_0	,   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=lowerCamelCase__	,   metrics=['accuracy']							)

   def decode_fn(__lowerCamelCase	:							Optional[Any]							):
      _UpperCAmelCase       :       int							   ={
          '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(lowerCamelCase__	,   lowerCamelCase__							)

   # 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       :       Tuple							   =DataCollatorForLanguageModeling(
       tokenizer=lowerCamelCase__	,   mlm_probability=args.mlm_probability	,   mlm=lowerCamelCase__	,   return_tensors='tf'							)

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

   _UpperCAmelCase       :       List[str]							   =args.per_replica_batch_size * strategy.num_replicas_in_sync

   _UpperCAmelCase       :       Union[str, Any]							   =prepare_dataset(
       lowerCamelCase__	,   decode_fn=lowerCamelCase__	,   mask_fn=lowerCamelCase__	,   batch_size=lowerCamelCase__	,   shuffle=lowerCamelCase__	,   shuffle_buffer_size=args.shuffle_buffer_size	,   )

   _UpperCAmelCase       :       Optional[Any]							   =prepare_dataset(
       lowerCamelCase__	,   decode_fn=lowerCamelCase__	,   mask_fn=lowerCamelCase__	,   batch_size=lowerCamelCase__	,   shuffle=lowerCamelCase__	,   )

   _UpperCAmelCase       :       Union[str, Any]							   =[]
   if args.hub_model_id:
      callbacks.append(
          PushToHubCallback(output_dir=args.output_dir	,   hub_model_id=args.hub_model_id	,   tokenizer=lowerCamelCase__							)							)

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

   model.save_pretrained(args.output_dir							)


if __name__ == "__main__":
 lowercase							=parse_args()
 main(args)



'''simple docstring'''






import numpy as np






class  __magic_name__							:






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =(0, 0)
          _UpperCAmelCase       :       Any							   =None
          _UpperCAmelCase       :       List[str]							   =0
          _UpperCAmelCase       :       Dict							   =0
          _UpperCAmelCase       :       Dict							   =0






       def __eq__(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.position == cell.position






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




          '''simple docstring'''
          print(self.position)






class  __magic_name__							:






       def __init__(      self  ,							snake_case=(5, 5))						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =np.zeros(snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =world_size[0]
          _UpperCAmelCase       :       Any							   =world_size[1]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          print(self.w)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =[
              (-1, -1),
              (-1, 0),
              (-1, 1),
              (0, -1),
              (0, 1),
              (1, -1),
              (1, 0),
              (1, 1),
          ]
          _UpperCAmelCase       :       int							   =cell.position[0]
          _UpperCAmelCase       :       Optional[Any]							   =cell.position[1]
          _UpperCAmelCase       :       Dict							   =[]
          for n in neughbour_cord:
             _UpperCAmelCase       :       List[str]							   =current_x + n[0]
             _UpperCAmelCase       :       int							   =current_y + n[1]
             if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                _UpperCAmelCase       :       List[Any]							   =Cell()
                _UpperCAmelCase       :       Dict							   =(x, y)
                _UpperCAmelCase       :       Any							   =cell
                neighbours.append(snake_case)
          return neighbours






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =[]
   _UpperCAmelCase       :       Union[str, Any]							   =[]
   _open.append(__lowerCamelCase							)

   while _open:
      _UpperCAmelCase       :       List[str]							   =np.argmin([n.f for n in _open]							)
      _UpperCAmelCase       :       Optional[int]							   =_open[min_f]
      _closed.append(_open.pop(__lowerCamelCase							)							)
      if current == goal:
         break
      for n in world.get_neigbours(__lowerCamelCase							):
         for c in _closed:
            if c == n:
               continue
         _UpperCAmelCase       :       Tuple							   =current.g + 1
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =n.position
         _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =goal.position
         _UpperCAmelCase       :       Optional[int]							   =(ya - ya) ** 2 + (xa - xa) ** 2
         _UpperCAmelCase       :       Optional[Any]							   =n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =[]
   while current.parent is not None:
      path.append(current.position							)
      _UpperCAmelCase       :       Any							   =current.parent
   path.append(current.position							)
   return path[::-1]


if __name__ == "__main__":
 lowercase							=Gridworld()
 # Start position and goal
 lowercase							=Cell()
 lowercase							=(0, 0)
 lowercase							=Cell()
 lowercase							=(4, 4)
 print(F"""path from {start.position} to {goal.position}""")
 lowercase							=astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  lowercase							=1
 print(world.w)

