




"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase					,							UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						,  __UpperCAmelCase						:			Any												=       emb.weight.shape
			__UpperCAmelCase						:			Dict												=       nn.Linear(UpperCamelCase					,							UpperCamelCase					,							bias=UpperCamelCase				)
			__UpperCAmelCase						:			Optional[Any]												=       emb.weight.data
			return lin_layer

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			__UpperCAmelCase						:			Tuple												=       torch.load(UpperCamelCase					,							map_location="cpu"				)
			__UpperCAmelCase						:			Tuple												=       Namespace(**checkpoint["cfg"]["model"]				)
			__UpperCAmelCase						:			str												=       checkpoint["model"]
			remove_ignore_keys_(UpperCamelCase				)
			__UpperCAmelCase						:			int												=       state_dict["decoder.embed_tokens.weight"].shape[0]

			__UpperCAmelCase						:			Optional[int]												=       {key.replace("decoder"					,							"model"				): val for key, val in state_dict.items()}

			__UpperCAmelCase						:			Any												=       XGLMConfig(
			    vocab_size=UpperCamelCase					,							max_position_embeddings=args.max_target_positions					,							num_layers=args.decoder_layers					,							attention_heads=args.decoder_attention_heads					,							ffn_dim=args.decoder_ffn_embed_dim					,							d_model=args.decoder_embed_dim					,							layerdrop=args.decoder_layerdrop					,							dropout=args.dropout					,							attention_dropout=args.attention_dropout					,							activation_dropout=args.activation_dropout					,							activation_function="gelu"					,							scale_embedding=not args.no_scale_embedding					,							tie_word_embeddings=args.share_decoder_input_output_embed					,							)

			__UpperCAmelCase						:			Union[str, Any]												=       XGLMForCausalLM(UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       model.load_state_dict(UpperCamelCase					,							strict=UpperCamelCase				)
			print(UpperCamelCase				)
			__UpperCAmelCase						:			int												=       make_linear_from_emb(model.model.embed_tokens				)

			return model


if __name__ == "__main__":
					A				=							argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
					parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					A				=							parser.parse_args()
					A				=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE					:				Optional[int]									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		{
    '''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
        '''https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'''
    ),
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[int]   						="gptsan-japanese"
						_UpperCAmelCase		:       Union[str, Any]   						=[
						    "past_key_values",
						]
						_UpperCAmelCase		:       int   						={
						    "hidden_size": "d_model",
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      Dict=3_60_00 ,  lowerCAmelCase							:      Union[str, Any]=12_80 ,  lowerCAmelCase							:      Tuple=10_24 ,  lowerCAmelCase							:      Any=81_92 ,  lowerCAmelCase							:      List[str]=40_96 ,  lowerCAmelCase							:      Dict=1_28 ,  lowerCAmelCase							:      int=10 ,  lowerCAmelCase							:      str=0 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      int=16 ,  lowerCAmelCase							:      str=1_28 ,  lowerCAmelCase							:      Dict=0.0 ,  lowerCAmelCase							:      List[str]=1e-5 ,  lowerCAmelCase							:      List[str]=False ,  lowerCAmelCase							:      int=0.0 ,  lowerCAmelCase							:      Tuple="float32" ,  lowerCAmelCase							:      Optional[int]=False ,  lowerCAmelCase							:      int=False ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      Optional[Any]=0.0_0_2 ,  lowerCAmelCase							:      Tuple=False ,  lowerCAmelCase							:      str=True ,  lowerCAmelCase							:      Optional[int]=3_59_98 ,  lowerCAmelCase							:      Optional[Any]=3_59_95 ,  lowerCAmelCase							:      Tuple=3_59_99 ,  **lowerCAmelCase							:      Dict ,  ):
													A_			    =		vocab_size
													A_			    =		max_position_embeddings
													A_			    =		d_model
													A_			    =		d_ff
													A_			    =		d_ext
													A_			    =		d_spout
													A_			    =		num_switch_layers
													A_			    =		num_ext_layers
													A_			    =		num_switch_layers + num_ext_layers
													A_			    =		num_heads
													A_			    =		num_experts
													A_			    =		expert_capacity
													A_			    =		dropout_rate
													A_			    =		layer_norm_epsilon
													A_			    =		router_bias
													A_			    =		router_jitter_noise
													A_			    =		router_dtype
													A_			    =		router_ignore_padding_tokens
													A_			    =		output_hidden_states
													A_			    =		output_attentions
													A_			    =		initializer_factor
													A_			    =		output_router_logits
													A_			    =		use_cache

													super().__init__(
													    separator_token_id=lowerCAmelCase ,  pad_token_id=lowerCAmelCase ,  eos_token_id=lowerCAmelCase ,  **lowerCAmelCase ,  )





