

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class    _a						(__magic_name__		,       __magic_name__		,       __magic_name__    ):


					'''simple docstring'''






					@register_to_config
					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__ = False   ,	):
						super().__init__()

						A__							:   str					       =		nn.Embedding(A__   ,	A__      )

						A__							:   List[str]					       =		nn.Embedding(A__   ,	A__      )
						A__							:   Optional[Any]					       =		False

						A__							:   Tuple					       =		nn.Dropout(p=A__      )

						A__							:   Optional[Any]					       =		TaConfig(
						    vocab_size=A__   ,	d_model=A__   ,	num_heads=A__   ,	d_kv=A__   ,	d_ff=A__   ,	dropout_rate=A__   ,	feed_forward_proj=A__   ,	is_decoder=A__   ,	is_encoder_decoder=A__   ,	)

						A__							:   Union[str, Any]					       =		nn.ModuleList()
						for lyr_num in range(A__      ):
							A__							:   Optional[Any]					       =		TaBlock(A__      )
							self.encoders.append(A__      )

						A__							:   Dict					       =		TaLayerNorm(A__      )
						A__							:   int					       =		nn.Dropout(p=A__      )






					def   __A      (			self   ,	A__   ,	A__      ):
						A__							:   Dict					       =		self.token_embedder(A__      )

						A__							:   Dict					       =		encoder_input_tokens.shape[1]
						A__							:   Union[str, Any]					       =		torch.arange(A__   ,	device=encoder_input_tokens.device      )
						x += self.position_encoding(A__      )

						A__							:   List[str]					       =		self.dropout_pre(A__      )

						# inverted the attention mask
						A__							:   Any					       =		encoder_input_tokens.size()
						A__							:   Tuple					       =		self.get_extended_attention_mask(A__   ,	A__      )

						for lyr in self.encoders:
							A__							:   Dict					       =		lyr(A__   ,	A__      )[0]
						A__							:   str					       =		self.layer_norm(A__      )

						return self.dropout_post(A__      ), encoder_inputs_mask






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	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=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		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      )



