

'''simple docstring'''






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 	_A  (					A__   ):






 """simple docstring"""



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


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase						,			__lowercase													=				emb.weight.shape
 __lowercase													=				nn.Linear(A__  ,       A__  ,       bias=A__   )
 __lowercase													=				emb.weight.data
 return lin_layer


def 	_A  (					A__   ):






 """simple docstring"""



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

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

 __lowercase													=				XGLMConfig(
     vocab_size=A__  ,       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  ,       )

 __lowercase													=				XGLMForCausalLM(A__   )
 __lowercase													=				model.load_state_dict(A__  ,       strict=A__   )
 print(A__   )
 __lowercase													=				make_linear_from_emb(model.model.embed_tokens   )

 return model


if __name__ == "__main__":
 lowerCAmelCase__      		=			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.''')
 lowerCAmelCase__      		=			parser.parse_args()
 lowerCAmelCase__      		=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
 model.save_pretrained(args.pytorch_dump_folder_path)




'''simple docstring'''






from collections.abc import Callable

import numpy as np


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				int(np.ceil((x_end - xa) / step_size   )   )
 __lowercase													=				np.zeros((n + 1,)   )
 __lowercase													=				ya
 __lowercase													=				xa

 for k in range(A__   ):
  __lowercase													=				y[k] + step_size * ode_func(A__  ,       y[k]   )
  x += step_size

 return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()

