
"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_a									=				logging.get_logger(__name__)

_a									=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_a									=				{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

_a									=				{
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}


class A_      (__SCREAMING_SNAKE_CASE ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__     :		List[str]    	=						["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__     :		Tuple    	=						LongformerTokenizer







  def __init__(						self							,  lowercase_=None							,  lowercase_=None							,  lowercase_=None							,  lowercase_="replace"							,  lowercase_="<s>"							,  lowercase_="</s>"							,  lowercase_="</s>"							,  lowercase_="<s>"							,  lowercase_="<unk>"							,  lowercase_="<pad>"							,  lowercase_="<mask>"							,  lowercase_=False							,  lowercase_=True							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(
         __UpperCamelCase							,  __UpperCamelCase							,  tokenizer_file=__UpperCamelCase							,  errors=__UpperCamelCase							,  bos_token=__UpperCamelCase							,  eos_token=__UpperCamelCase							,  sep_token=__UpperCamelCase							,  cls_token=__UpperCamelCase							,  unk_token=__UpperCamelCase							,  pad_token=__UpperCamelCase							,  mask_token=__UpperCamelCase							,  add_prefix_space=__UpperCamelCase							,  trim_offsets=__UpperCamelCase							,  **__UpperCamelCase							,  )

     UpperCAmelCase_   :   str     	=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
     if pre_tok_state.get("add_prefix_space"							,  __UpperCamelCase    ) != add_prefix_space:
        UpperCAmelCase_   :   Any     	=       getattr(__UpperCamelCase							,  pre_tok_state.pop("type"    )    )
        UpperCAmelCase_   :   List[str]     	=       add_prefix_space
        UpperCAmelCase_   :   str     	=       pre_tok_class(**__UpperCamelCase    )

     UpperCAmelCase_   :   Union[str, Any]     	=       add_prefix_space

     UpperCAmelCase_   :   int     	=       "post_processor"
     UpperCAmelCase_   :   Any     	=       getattr(self.backend_tokenizer							,  __UpperCamelCase							,  __UpperCamelCase    )
     if tokenizer_component_instance:
        UpperCAmelCase_   :   Optional[Any]     	=       json.loads(tokenizer_component_instance.__getstate__()    )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
           UpperCAmelCase_   :   Tuple     	=       tuple(state["sep"]    )
        if "cls" in state:
           UpperCAmelCase_   :   str     	=       tuple(state["cls"]    )

        UpperCAmelCase_   :   str     	=       False

        if state.get("add_prefix_space"							,  __UpperCamelCase    ) != add_prefix_space:
           UpperCAmelCase_   :   List[str]     	=       add_prefix_space
           UpperCAmelCase_   :   Any     	=       True

        if state.get("trim_offsets"							,  __UpperCamelCase    ) != trim_offsets:
           UpperCAmelCase_   :   str     	=       trim_offsets
           UpperCAmelCase_   :   Optional[Any]     	=       True

        if changes_to_apply:
           UpperCAmelCase_   :   int     	=       getattr(__UpperCamelCase							,  state.pop("type"    )    )
           UpperCAmelCase_   :   Tuple     	=       component_class(**__UpperCamelCase    )
           setattr(self.backend_tokenizer							,  __UpperCamelCase							,  __UpperCamelCase    )







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""


     if self._mask_token is None:
        if self.verbose:
           logger.error("Using mask_token, but it is not set yet."    )
        return None
     return str(self._mask_token    )







  @mask_token.setter
  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       AddedToken(__UpperCamelCase							,  lstrip=__UpperCamelCase							,  rstrip=__UpperCamelCase    ) if isinstance(__UpperCamelCase							,  __UpperCamelCase    ) else value
     UpperCAmelCase_   :   List[Any]     	=       value







  def 			UpperCamelCase__   (						self							,  *lowercase_							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       kwargs.get("is_split_into_words"							,  __UpperCamelCase    )
     assert self.add_prefix_space or not is_split_into_words, (
         F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

     return super()._batch_encode_plus(*__UpperCamelCase							,  **__UpperCamelCase    )







  def 			UpperCamelCase__   (						self							,  *lowercase_							,  **lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       kwargs.get("is_split_into_words"							,  __UpperCamelCase    )

     assert self.add_prefix_space or not is_split_into_words, (
         F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
         "to use it with pretokenized inputs."
     )

     return super()._encode_plus(*__UpperCamelCase							,  **__UpperCamelCase    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       self._tokenizer.model.save(__UpperCamelCase							,  name=__UpperCamelCase    )
     return tuple(__UpperCamelCase    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_=None    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       [self.sep_token_id]
     UpperCAmelCase_   :   str     	=       [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep    ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



