
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowerCamelCase      =logging.get_logger(__name__)

lowerCamelCase      ={
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''bloom'''
     SCREAMING_SNAKE_CASE_		   =  ['''past_key_values''']
     SCREAMING_SNAKE_CASE_		   =  {
         '''num_hidden_layers''': '''n_layer''',
         '''num_attention_heads''': '''n_head''',
     }






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=2_5_0_8_8_0						,						__SCREAMING_SNAKE_CASE=6_4						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=8						,						__SCREAMING_SNAKE_CASE=1e-5						,						__SCREAMING_SNAKE_CASE=0.02						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=2						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=0.0						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=False						,						**__SCREAMING_SNAKE_CASE						,						)  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				vocab_size
           # Backward compatibility with n_embed kwarg
           UpperCamelCase__    :   List[str]					=				kwargs.pop('''n_embed'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Any					=				hidden_size if n_embed is None else n_embed
           UpperCamelCase__    :   Tuple					=				n_layer
           UpperCamelCase__    :   Dict					=				n_head
           UpperCamelCase__    :   Optional[Any]					=				layer_norm_epsilon
           UpperCamelCase__    :   Dict					=				initializer_range
           UpperCamelCase__    :   str					=				use_cache
           UpperCamelCase__    :   Any					=				pretraining_tp
           UpperCamelCase__    :   Union[str, Any]					=				apply_residual_connection_post_layernorm
           UpperCamelCase__    :   int					=				hidden_dropout
           UpperCamelCase__    :   Any					=				attention_dropout

           UpperCamelCase__    :   Any					=				bos_token_id
           UpperCamelCase__    :   List[str]					=				eos_token_id
           UpperCamelCase__    :   List[str]					=				slow_but_exact

           super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE						,						eos_token_id=__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  version.parse('''1.12''' )






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = "default"						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = False						,						)  ->   Optional[Any]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						task=__SCREAMING_SNAKE_CASE						,						patching_specs=__SCREAMING_SNAKE_CASE						,						use_past=__SCREAMING_SNAKE_CASE  )
           if not getattr(self._config						,						'''pad_token_id'''						,						__SCREAMING_SNAKE_CASE  ):
                 # TODO: how to do that better?
                 UpperCamelCase__    :   Any					=				0






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Mapping[str, Mapping[int, str]]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}  )
           if self.use_past:
                 # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
                 self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE						,						direction='''inputs'''						,						inverted_values_shape=__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Tuple					=				{0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
                 UpperCamelCase__    :   Optional[Any]					=				{0: '''batch''', 1: '''sequence'''}

           return common_inputs






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           return self._config.n_layer






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           return self._config.n_head






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   float:




           """simple docstring"""







           return 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = -1						,						__SCREAMING_SNAKE_CASE = -1						,						__SCREAMING_SNAKE_CASE = False						,						__SCREAMING_SNAKE_CASE = None						,						)  ->   Mapping[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				super(__SCREAMING_SNAKE_CASE						,						self  ).generate_dummy_inputs(
               __SCREAMING_SNAKE_CASE						,						batch_size=__SCREAMING_SNAKE_CASE						,						seq_length=__SCREAMING_SNAKE_CASE						,						is_pair=__SCREAMING_SNAKE_CASE						,						framework=__SCREAMING_SNAKE_CASE  )

           # We need to order the input in the way they appears in the forward()
           UpperCamelCase__    :   Any					=				OrderedDict({'''input_ids''': common_inputs['''input_ids''']}  )

           # Need to add the past_keys
           if self.use_past:
                 if not is_torch_available():
                       raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''  )
                 else:
                       import torch

                       UpperCamelCase__    :   Union[str, Any]					=				common_inputs['''input_ids'''].shape
                       # Not using the same length for past_key_values
                       UpperCamelCase__    :   List[Any]					=				seqlen + 2
                       UpperCamelCase__    :   Any					=				self._config.hidden_size // self.num_attention_heads
                       UpperCamelCase__    :   List[str]					=				(
                           batch * self.num_attention_heads,
                           head_dim,
                           past_key_values_length,
                       )
                       UpperCamelCase__    :   int					=				(
                           batch * self.num_attention_heads,
                           past_key_values_length,
                           head_dim,
                       )
                       UpperCamelCase__    :   List[Any]					=				[
                           (torch.zeros(__SCREAMING_SNAKE_CASE  ), torch.zeros(__SCREAMING_SNAKE_CASE  )) for _ in range(self.num_layers  )
                       ]

           UpperCamelCase__    :   Tuple					=				common_inputs['''attention_mask''']
           if self.use_past:
                 UpperCamelCase__    :   List[Any]					=				ordered_inputs['''attention_mask'''].dtype
                 UpperCamelCase__    :   Tuple					=				torch.cat(
                     [ordered_inputs['''attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						dtype=__SCREAMING_SNAKE_CASE  )]						,						dim=1  )

           return ordered_inputs






     @property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           return 1_3




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
    lowerCamelCase      =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")
    lowerCamelCase   ,     lowerCamelCase      =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")
        lowerCamelCase      =rh.cluster(
            name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
        )
    else:
        lowerCamelCase      =rh.cluster(
            name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
        )
    lowerCamelCase      =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)


