






"""simple docstring"""






import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''



def  __lowerCamelCase					(					):
    A__							 =       _ask_options(
        'In which compute environment are you running?'       ,['This machine', 'AWS (Amazon SageMaker)']       ,_convert_compute_environment       ,)
    if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
        A__							 =       get_sagemaker_input()
    else:
        A__							 =       get_cluster_input()
    return config



def  __lowerCamelCase					(					lowerCAmelCase__=None				):
    if subparsers is not None:
        A__							 =       subparsers.add_parser('config'       ,description=lowerCAmelCase__				)
    else:
        A__							 =       argparse.ArgumentParser('Accelerate config command'       ,description=lowerCAmelCase__				)

    parser.add_argument(
        '--config_file'       ,default=lowerCAmelCase__       ,help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )       ,)

    if subparsers is not None:
        parser.set_defaults(func=lowerCAmelCase__				)
    return parser



def  __lowerCamelCase					(					lowerCAmelCase__				):
    A__							 =       get_user_input()
    if args.config_file is not None:
        A__							 =       args.config_file
    else:
        if not os.path.isdir(lowerCAmelCase__				):
            os.makedirs(lowerCAmelCase__				)
        A__							 =       default_yaml_config_file

    if config_file.endswith('.json'				):
        config.to_json_file(lowerCAmelCase__				)
    else:
        config.to_yaml_file(lowerCAmelCase__				)
    print(f'''accelerate configuration saved at {config_file}'''				)



def  __lowerCamelCase					(					):
    A__							 =       config_command_parser()
    A__							 =       parser.parse_args()
    config_command(lowerCAmelCase__				)


if __name__ == "__main__":
     main()










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


