
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

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

def  __lowerCamelCase					( _lowercase			)       ->      Union[str, Any]:
     UpperCamelCase    ,		UpperCamelCase						    =					emb.weight.shape
     UpperCamelCase						    =					nn.Linear(_lowercase						,     _lowercase						,     bias=_lowercase			)
     UpperCamelCase						    =					emb.weight.data
     return lin_layer

def  __lowerCamelCase					( _lowercase			)       ->      Optional[int]:
     UpperCamelCase						    =					torch.load(_lowercase						,     map_location='cpu'			)
     UpperCamelCase						    =					Namespace(**checkpoint['cfg']['model']			)
     UpperCamelCase						    =					checkpoint['model']
     remove_ignore_keys_(_lowercase			)
     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=_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						,     )

     UpperCamelCase						    =					XGLMForCausalLM(_lowercase			)
     UpperCamelCase						    =					model.load_state_dict(_lowercase						,     strict=_lowercase			)
     print(_lowercase			)
     UpperCamelCase						    =					make_linear_from_emb(model.model.embed_tokens			)

     return model


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




from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


