


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def        UpperCamelCase_(   lowerCamelCase_				)					->		str:
    _lowercase :							str   	=	[
        'decoder.version',
        'decoder.output_projection.weight',
        '_float_tensor',
        'decoder.embed_positions._float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_lowercase							,				_lowercase				)



def        UpperCamelCase_(   lowerCamelCase_				)					->		Dict:
    _lowercase	,       _lowercase :							Optional[int]   	=	emb.weight.shape
    _lowercase :							Optional[int]   	=	nn.Linear(_lowercase							,				_lowercase							,				bias=_lowercase				)
    _lowercase :							Any   	=	emb.weight.data
    return lin_layer



def        UpperCamelCase_(   lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							List[Any]   	=	torch.load(_lowercase							,				map_location='cpu'				)
    _lowercase :							Any   	=	Namespace(**checkpoint['cfg']['model']				)
    _lowercase :							List[Any]   	=	checkpoint['model']
    remove_ignore_keys_(_lowercase				)
    _lowercase :							str   	=	state_dict['decoder.embed_tokens.weight'].shape[0]

    _lowercase :							List[Any]   	=	{key.replace('decoder'							,				'model'				): val for key, val in state_dict.items()}

    _lowercase :							List[str]   	=	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							,				)

    _lowercase :							int   	=	XGLMForCausalLM(_lowercase				)
    _lowercase :							int   	=	model.load_state_dict(_lowercase							,				strict=_lowercase				)
    print(_lowercase				)
    _lowercase :							str   	=	make_linear_from_emb(model.model.embed_tokens				)

    return model


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE :   Optional[Any]							      =				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.")
      SCREAMING_SNAKE_CASE :   Any							      =				parser.parse_args()
      SCREAMING_SNAKE_CASE :   List[Any]							      =				convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


SCREAMING_SNAKE_CASE :   List[Any]							      =				subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
SCREAMING_SNAKE_CASE :   Optional[int]							      =				(
    subprocess.check_output(F"git diff --diff-filter=d --name-only {fork_point_sha}".split()).decode("utf-8").split()
)

SCREAMING_SNAKE_CASE :   Any							      =				"|".join(sys.argv[1:])
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				re.compile(rF"^({joined_dirs}).*?\.py$")

SCREAMING_SNAKE_CASE :   List[Any]							      =				[x for x in modified_files if regex.match(x)]
print(" ".join(relevant_modified_files), end="")


