
"""simple docstring"""
from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


lowercase		:						List[str] =					'Update an existing config file with the latest defaults while maintaining the old configuration.'


def     __UpperCAmelCase    (	lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''
					__a:						Any		    =					args.config_file
					if config_file is None and Path(lowerCamelCase_ ).exists():
										__a:						str		    =					default_config_file
					elif not Path(lowerCamelCase_ ).exists():
										raise ValueError(f"""The passed config file located at {config_file} doesn't exist.""" )
					__a:						Optional[Any]		    =					load_config_from_file(lowerCamelCase_ )

					if config_file.endswith(""".json""" ):
										config.to_json_file(lowerCamelCase_ )
					else:
										config.to_yaml_file(lowerCamelCase_ )
					return config_file


def     __UpperCAmelCase    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


					'''simple docstring'''
					__a:						List[Any]		    =					parser.add_parser("""update"""	,				parents=lowerCamelCase_	,				help=lowerCamelCase_	,				formatter_class=lowerCamelCase_ )
					parser.add_argument(
					    """--config_file"""	,				default=lowerCamelCase_	,				help=(
					        """The path to the config file to update. 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'."""
					    )	,				)

					parser.set_defaults(func=lowerCamelCase_ )
					return parser




def     __UpperCAmelCase    (	lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					__a:						List[Any]		    =					update_config(lowerCamelCase_ )
					print(f"""Sucessfully updated the configuration file at {config_file}.""" )


"""simple docstring"""
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    _snake_case  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
