






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def        _a  (     UpperCamelCase_					:				str   ) ->   Dict:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				Dict   ) ->   Tuple:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				List[str]   ) ->   List[str]:
						"""simple docstring"""







						lowerCAmelCase__       =						torch.load(lowerCAmelCase__    ,   map_location="cpu"   )
						lowerCAmelCase__       =						Namespace(**checkpoint["cfg"]["model"]   )
						lowerCAmelCase__       =						checkpoint["model"]
						remove_ignore_keys_(lowerCAmelCase__   )
						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=lowerCAmelCase__    ,   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(lowerCAmelCase__   )
						lowerCAmelCase__       =						model.load_state_dict(lowerCAmelCase__    ,   strict=lowerCAmelCase__   )
						print(lowerCAmelCase__   )
						lowerCAmelCase__       =						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)














# 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


a_							=    subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
a_							=    subprocess.check_output(F"git diff --name-only {fork_point_sha}".split()).decode('''utf-8''').split()

a_							=    '''|'''.join(sys.argv[1:])
a_							=    re.compile(rF"^({joined_dirs}).*?\.py$")

a_							=    [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')






