



'''simple docstring'''





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 		__snake_case(							_lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Optional[int]       		= [
          "decoder.version",
          "decoder.output_projection.weight",
          "_float_tensor",
          "decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(__UpperCamelCase	,  __UpperCamelCase						)





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       str       		= emb.weight.shape
      snake_case__    :       List[str]       		= nn.Linear(__UpperCamelCase	,  __UpperCamelCase	,  bias=__UpperCamelCase						)
      snake_case__    :       Any       		= emb.weight.data
      return lin_layer





def 		__snake_case(							_lowerCAmelCase						)						->					Dict:
      snake_case__    :       Tuple       		= torch.load(__UpperCamelCase	,  map_location="""cpu"""						)
      snake_case__    :       List[Any]       		= Namespace(**checkpoint["""cfg"""]["""model"""]						)
      snake_case__    :       List[str]       		= checkpoint["model"]
      remove_ignore_keys_(__UpperCamelCase						)
      snake_case__    :       Any       		= state_dict["decoder.embed_tokens.weight"].shape[0]

      snake_case__    :       List[str]       		= {key.replace("""decoder"""	,  """model"""						): val for key, val in state_dict.items()}

      snake_case__    :       Dict       		= 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	,  )

      snake_case__    :       Tuple       		= XGLMForCausalLM(__UpperCamelCase						)
      snake_case__    :       List[Any]       		= model.load_state_dict(__UpperCamelCase	,  strict=__UpperCamelCase						)
      print(__UpperCamelCase						)
      snake_case__    :       Tuple       		= 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


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

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

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


