






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 	lowerCAmelCase_		(lowercase__						:       List[str]       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						[
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(lowercase__     ,					lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       str       )		->    Optional[int]:


				'''simple docstring'''




				lowerCAmelCase__			,		lowerCAmelCase__      			=						emb.weight.shape
				lowerCAmelCase__      			=						nn.Linear(lowercase__     ,					lowercase__     ,					bias=lowercase__       )
				lowerCAmelCase__      			=						emb.weight.data
				return lin_layer





def 	lowerCAmelCase_		(lowercase__						:       Dict       )		->    Optional[Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						torch.load(lowercase__     ,					map_location='''cpu'''       )
				lowerCAmelCase__      			=						Namespace(**checkpoint['''cfg''']['''model''']       )
				lowerCAmelCase__      			=						checkpoint['''model''']
				remove_ignore_keys_(lowercase__       )
				lowerCAmelCase__      			=						state_dict['''decoder.embed_tokens.weight'''].shape[0]

				lowerCAmelCase__      			=						{key.replace('''decoder'''     ,					'''model'''       ): val for key, val in state_dict.items()}

				lowerCAmelCase__      			=						XGLMConfig(
				    vocab_size=lowercase__     ,					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     ,					)

				lowerCAmelCase__      			=						XGLMForCausalLM(lowercase__       )
				lowerCAmelCase__      			=						model.load_state_dict(lowercase__     ,					strict=lowercase__       )
				print(lowercase__       )
				lowerCAmelCase__      			=						make_linear_from_emb(model.model.embed_tokens       )

				return model


if __name__ == "__main__":
			_UpperCAmelCase			:		int   				=    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.")
			_UpperCAmelCase			:		List[str]   				=    parser.parse_args()
			_UpperCAmelCase			:		Union[str, Any]   				=    convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
			model.save_pretrained(args.pytorch_dump_folder_path)













from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments





