


"""simple docstring"""







from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def _A						(						):







				"""simple docstring"""
				a					=ArgumentParser('''Transformers CLI tool'''	,   usage='''transformers-cli <command> [<args>]'''		)
				a					=parser.add_subparsers(help='''transformers-cli command helpers'''		)

				# Register commands
				ConvertCommand.register_subcommand(lowercase		)
				DownloadCommand.register_subcommand(lowercase		)
				EnvironmentCommand.register_subcommand(lowercase		)
				RunCommand.register_subcommand(lowercase		)
				ServeCommand.register_subcommand(lowercase		)
				UserCommands.register_subcommand(lowercase		)
				AddNewModelCommand.register_subcommand(lowercase		)
				AddNewModelLikeCommand.register_subcommand(lowercase		)
				LfsCommands.register_subcommand(lowercase		)
				PTtoTFCommand.register_subcommand(lowercase		)

				# Let's go
				a					=parser.parse_args()

				if not hasattr(lowercase	,   '''func'''		):
								parser.print_help()
								exit(1		)

				# Run
				a					=args.func(lowercase		)
				service.run()


if __name__ == "__main__":
							main()



"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_       :       str      						=  logging.get_logger(__name__)

lowerCamelCase_       :       Optional[int]      						=  {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "xlm-roberta"


 def __init__(		self			,   __A=3_0522			,   __A=768			,   __A=12			,   __A=12			,   __A=3072			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=512			,   __A=2			,   __A=0.02			,   __A=1E-1_2			,   __A=1			,   __A=0			,   __A=2			,   __A="absolute"			,   __A=True			,   __A=None			,   **__A			,   ) ->					str:
     super().__init__(pad_token_id=__A			,   bos_token_id=__A			,   eos_token_id=__A			,   **__A						)

     a					=vocab_size
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=hidden_act
     a					=intermediate_size
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=max_position_embeddings
     a					=type_vocab_size
     a					=initializer_range
     a					=layer_norm_eps
     a					=position_embedding_type
     a					=use_cache
     a					=classifier_dropout






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         a					={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         a					={0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]						)