



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def    _lowerCAmelCase  (	A__:      str						):


    '''simple docstring'''


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




def    _lowerCAmelCase  (	A__:      int						):


    '''simple docstring'''


    UpperCAmelCase      =       emb.weight.shape
    UpperCAmelCase      =       nn.Linear(__UpperCAmelCase						,		__UpperCAmelCase						,		bias=__UpperCAmelCase						)
    UpperCAmelCase      =       emb.weight.data
    return lin_layer




def    _lowerCAmelCase  (	A__:      Tuple						):


    '''simple docstring'''


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

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

    UpperCAmelCase      =       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      =       XGLMForCausalLM(__UpperCAmelCase						)
    UpperCAmelCase      =       model.load_state_dict(__UpperCAmelCase						,		strict=__UpperCAmelCase						)
    print(__UpperCAmelCase						)
    UpperCAmelCase      =       make_linear_from_emb(model.model.embed_tokens						)

    return model


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












"""simple docstring"""





from random import randint
from tempfile import TemporaryFile

import numpy as np



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Optional[int]  )    ->							int:
						__magic_name__:	Optional[int]							=       0
						if start < end:
												__magic_name__:	Union[str, Any]							=       randint(__UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	int							=       a[end]
												__magic_name__:	Optional[int]							=       a[pivot]
												__magic_name__:	Tuple							=       temp

												__magic_name__,			__magic_name__:	int							=       _in_place_partition(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												count += _in_place_quick_sort(__UpperCAmelCase      ,   __UpperCAmelCase      ,   p - 1  )
												count += _in_place_quick_sort(__UpperCAmelCase      ,   p + 1      ,   __UpperCAmelCase  )
						return count



def   a    (     __UpperCAmelCase      :    Optional[Any]      ,   __UpperCAmelCase      :    str      ,   __UpperCAmelCase      :    List[Any]  )    ->							Union[str, Any]:
						__magic_name__:	Union[str, Any]							=       0
						__magic_name__:	str							=       randint(__UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       a[end]
						__magic_name__:	Optional[int]							=       a[pivot]
						__magic_name__:	Optional[int]							=       temp
						__magic_name__:	Dict							=       start - 1
						for index in range(__UpperCAmelCase      ,   __UpperCAmelCase  ):
												count += 1
												if a[index] < a[end]:  # check if current val is less than pivot value
																		__magic_name__:	List[Any]							=       new_pivot_index + 1
																		__magic_name__:	Any							=       a[new_pivot_index]
																		__magic_name__:	int							=       a[index]
																		__magic_name__:	Union[str, Any]							=       temp

						__magic_name__:	List[Any]							=       a[new_pivot_index + 1]
						__magic_name__:	Union[str, Any]							=       a[end]
						__magic_name__:	Dict							=       temp
						return new_pivot_index + 1, count


__lowerCamelCase							=  TemporaryFile()
__lowerCamelCase							=  1_00  # 1000 elements are to be sorted


__lowerCamelCase		,					__lowerCamelCase							=  0, 1  # mean and standard deviation
__lowerCamelCase							=  np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
__lowerCamelCase							=  np.load(outfile)
__lowerCamelCase							=  len(M) - 1
__lowerCamelCase							=  _in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)



