



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


__UpperCAmelCase	       =	logging.get_logger(__name__)

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

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

__UpperCAmelCase	       =	{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : List[str]       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : Any       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : List[Any]       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : Tuple       					=  ["input_ids", "attention_mask"]
       UpperCAmelCase__    : Dict       					=  GPTaTokenizer
       def __init__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_="<|endoftext|>",      SCREAMING_SNAKE_CASE_="<|endoftext|>",      SCREAMING_SNAKE_CASE_="<|endoftext|>",      SCREAMING_SNAKE_CASE_=False,      **SCREAMING_SNAKE_CASE_,      )					->		Union[str, Any]:
          super().__init__(
              SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      tokenizer_file=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      add_prefix_space=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  Union[str, Any]						     =     kwargs.pop('add_bos_token',      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Dict						     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
          if pre_tok_state.get('add_prefix_space',      SCREAMING_SNAKE_CASE_	) != add_prefix_space:
             UpperCamelCase   :  List[str]						     =     getattr(SCREAMING_SNAKE_CASE_,      pre_tok_state.pop('type'	)	)
             UpperCamelCase   :  Union[str, Any]						     =     add_prefix_space
             UpperCamelCase   :  Tuple						     =     pre_tok_class(**SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     add_prefix_space
       def  snake_case_	(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		BatchEncoding:
          UpperCamelCase   :  Optional[Any]						     =     kwargs.get('is_split_into_words',      SCREAMING_SNAKE_CASE_	)
          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(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		BatchEncoding:
          UpperCamelCase   :  int						     =     kwargs.get('is_split_into_words',      SCREAMING_SNAKE_CASE_	)

          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(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          UpperCamelCase   :  Dict						     =     self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,      name=SCREAMING_SNAKE_CASE_	)
          return tuple(SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[int]:
          UpperCamelCase   :  int						     =     []
          for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,      add_special_tokens=SCREAMING_SNAKE_CASE_	) + [self.eos_token_id]	)

          if len(SCREAMING_SNAKE_CASE_	) > self.model_max_length:
             UpperCamelCase   :  List[Any]						     =     input_ids[-self.model_max_length :]
          return input_ids








import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
    # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
    # setup instructions, if using on-demand hardware

    # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
    # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
    # Throw an error if user passes both BYO and on-demand cluster args
    # Otherwise, use default values
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    parser.add_argument('''--user''', type=str, default='''ubuntu''')
    parser.add_argument('''--host''', type=str, default='''localhost''')
    parser.add_argument('''--key_path''', type=str, default=None)
    parser.add_argument('''--instance''', type=str, default='''V100:1''')
    parser.add_argument('''--provider''', type=str, default='''cheapest''')
    parser.add_argument('''--use_spot''', type=bool, default=False)
    parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
    __UpperCAmelCase       ,  __UpperCAmelCase	       =	parser.parse_known_args()
    if args.host != "localhost":
        if args.instance != "V100:1" or args.provider != "cheapest":
            raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
        )
    else:
        __UpperCAmelCase	       =	rh.cluster(
            name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    __UpperCAmelCase	       =	args.example.rsplit('''/''', 1)[0]

    # Set up remote environment
    cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
    # Note transformers is copied into the home directory on the remote machine, so we can install from there
    cluster.run([F"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
    cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

    # Run example. You can bypass the CLI wrapper and paste your own code here.
    cluster.run([F"""python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}"""])

    # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
    # from my_script... import train
    # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
    # launch_train_gpu = rh.function(fn=train,
    #                                system=gpu,
    #                                reqs=reqs,
    #                                name='train_bert_glue')
    #
    # We can pass in arguments just like we would to a function:
    # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
    #                  stream_logs=True)



