
from argparse import ArgumentParser

from . import BaseTransformersCLICommand





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    return DownloadCommand(args.model     ,		args.cache_dir     ,		args.force     ,		args.trust_remote_code				)





class        _SCREAMING_SNAKE_CASE	(   SCREAMING_SNAKE_CASE_			):
      @staticmethod
      def A_      (      lowerCamelCase			):
          snake_case__    =					parser.add_parser("download"			)
          download_parser.add_argument(
              "--cache-dir"		,						type=lowerCamelCase		,						default=lowerCamelCase		,						help="Path to location to store the models"			)
          download_parser.add_argument(
              "--force"		,						action="store_true"		,						help="Force the model to be download even if already in cache-dir"			)
          download_parser.add_argument(
              "--trust-remote-code"		,						action="store_true"		,						help="Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine"		,						)
          download_parser.add_argument("model"		,						type=lowerCamelCase		,						help="Name of the model to download"			)
          download_parser.set_defaults(func=lowerCamelCase			)
      def __init__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			):
          snake_case__    =					model
          snake_case__    =					cache
          snake_case__    =					force
          snake_case__    =					trust_remote_code



      def A_      (      self			):
          from ..models.auto import AutoModel, AutoTokenizer

          AutoModel.from_pretrained(
              self._model		,						cache_dir=self._cache		,						force_download=self._force		,						trust_remote_code=self._trust_remote_code			)
          AutoTokenizer.from_pretrained(
              self._model		,						cache_dir=self._cache		,						force_download=self._force		,						trust_remote_code=self._trust_remote_code			)








"""simple docstring"""







from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=    logging.get_logger(__name__)

a_							=    {
    """microsoft/xprophetnet-large-wiki100-cased""": (
        """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
    ),
}







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			a_       :			List[Any]												=			"""xlm-prophetnet"""
			a_       :			Any												=			["""past_key_values"""]
			a_       :			Optional[int]												=			{
			    """num_attention_heads""": """num_encoder_attention_heads""",
			}
			def __init__(	self				,   A = 0.1				,   A = "gelu"				,   A = 3_0522				,   A = 1024				,   A = 4096				,   A = 12				,   A = 16				,   A = 4096				,   A = 12				,   A = 16				,   A = 0.1				,   A = 0.1				,   A = 512				,   A = 0.0_2				,   A = True				,   A = True				,   A = 0				,   A = 2				,   A = 32				,   A = 128				,   A = False				,   A = 0.0				,   A = True				,   A = 0				,   A = 1				,   A = 2				,   **A				,   ):
						_lowerCamelCase					:					Dict	       =    vocab_size
						_lowerCamelCase					:					List[Any]	       =    hidden_size
						_lowerCamelCase					:					List[Any]	       =    encoder_ffn_dim
						_lowerCamelCase					:					Any	       =    num_encoder_layers
						_lowerCamelCase					:					Any	       =    num_encoder_attention_heads
						_lowerCamelCase					:					List[str]	       =    decoder_ffn_dim
						_lowerCamelCase					:					Optional[int]	       =    num_decoder_layers
						_lowerCamelCase					:					List[str]	       =    num_decoder_attention_heads
						_lowerCamelCase					:					Optional[Any]	       =    max_position_embeddings
						_lowerCamelCase					:					Optional[Any]	       =    init_std  # Normal(0, this parameter)
						_lowerCamelCase					:					Optional[int]	       =    activation_function

						# parameters for xlmprophetnet
						_lowerCamelCase					:					Any	       =    ngram
						_lowerCamelCase					:					Dict	       =    num_buckets
						_lowerCamelCase					:					Dict	       =    relative_max_distance
						_lowerCamelCase					:					Optional[Any]	       =    disable_ngram_loss
						_lowerCamelCase					:					Union[str, Any]	       =    eps

						# 3 Types of Dropout
						_lowerCamelCase					:					int	       =    attention_dropout
						_lowerCamelCase					:					str	       =    activation_dropout
						_lowerCamelCase					:					Optional[int]	       =    dropout

						_lowerCamelCase					:					List[Any]	       =    use_cache

						super().__init__(
						    pad_token_id=A				,   bos_token_id=A				,   eos_token_id=A				,   is_encoder_decoder=A				,   add_cross_attention=A				,   decoder_start_token_id=A				,   **A				,   )
			@property
			def    _lowerCAmelCase		(	self							):
						return self.num_encoder_layers + self.num_decoder_layers

			@num_hidden_layers.setter
			def    _lowerCAmelCase		(	self				,   A							):
						raise NotImplementedError(
						    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
						    ' `num_decoder_layers`.'							)





