


'''simple docstring'''







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     __a       =   [
         "decoder.version",
         "decoder.output_projection.weight",
         "_float_tensor",
         "decoder.embed_positions._float_tensor",
     ]
     for k in ignore_keys:
          state_dict.pop(_lowerCamelCase				,  _lowerCamelCase		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[Any]:
     __a       =   emb.weight.shape
     __a       =   nn.Linear(_lowerCamelCase				,  _lowerCamelCase				,  bias=_lowerCamelCase		)
     __a       =   emb.weight.data
     return lin_layer




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Any:
     __a       =   torch.load(_lowerCamelCase				,  map_location="""cpu"""		)
     __a       =   Namespace(**checkpoint["""cfg"""]["""model"""]		)
     __a       =   checkpoint["model"]
     remove_ignore_keys_(_lowerCamelCase		)
     __a       =   state_dict["decoder.embed_tokens.weight"].shape[0]

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

     __a       =   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				,  )

     __a       =   XGLMForCausalLM(_lowerCamelCase		)
     __a       =   model.load_state_dict(_lowerCamelCase				,  strict=_lowerCamelCase		)
     print(_lowerCamelCase		)
     __a       =   make_linear_from_emb(model.model.embed_tokens		)

     return model


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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_      =			{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'RagModel',
     'RagPreTrainedModel',
     'RagSequenceForGeneration',
     'RagTokenForGeneration',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'TFRagModel',
     'TFRagPreTrainedModel',
     'TFRagSequenceForGeneration',
     'TFRagTokenForGeneration',
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






