

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


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {'''tokenizer_file''': '''tokenizer.json'''}

snake_case_			  = {
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Optional[Any]														=     VOCAB_FILES_NAMES
    __lowerCamelCase						:       Optional[int]														=     PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:       Dict														=     ["""input_ids""", """attention_mask"""]
    __lowerCamelCase						:       Dict														=     None





    def __init__(      self						,   a=None						,   a=None						,   a=None						,   a="<unk>"						,   a="<s>"						,   a="</s>"						,   a="<pad>"						,   a=False						,   a=False						,   **a						,   ):
         super().__init__(
             a						,   a						,   tokenizer_file=a						,   unk_token=a						,   bos_token=a						,   eos_token=a						,   pad_token=a						,   add_prefix_space=a						,   clean_up_tokenization_spaces=a						,   **a						,   )
         lowercase__    :   Any      =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,   a) != add_prefix_space:
              lowercase__    :   Dict      =       getattr(a						,   pre_tok_state.pop('type'))
              lowercase__    :   Any      =       add_prefix_space
              lowercase__    :   List[Any]      =       pre_tok_class(**a)

         lowercase__    :   Tuple      =       add_prefix_space





    def       snake_case_				(      self						,   *a						,   **a):
         lowercase__    :   Optional[int]      =       kwargs.get('is_split_into_words'						,   a)
         if not (self.add_prefix_space or not is_split_into_words):
              raise Exception(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                  ' pretokenized inputs.')

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





    def       snake_case_				(      self						,   *a						,   **a):
         lowercase__    :   Optional[Any]      =       kwargs.get('is_split_into_words'						,   a)

         if not (self.add_prefix_space or not is_split_into_words):
              raise Exception(
                  f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                  ' pretokenized inputs.')

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





    def       snake_case_				(      self						,   a						,   a = None):
         lowercase__    :   Any      =       self._tokenizer.model.save(a						,   name=a)
         return tuple(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Dict      =       []
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(a						,   add_special_tokens=a) + [self.eos_token_id])

         if len(a) > self.model_max_length:
              lowercase__    :   List[str]      =       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
       snake_case_			  = 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''')
       snake_case_  ,		snake_case_			  = 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''')
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
              )
       else:
              snake_case_			  = rh.cluster(
                  name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
              )
       snake_case_			  = 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)

