


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def        lowerCAmelCase__ (			a__:		str       )  ->		int:


							'''simple docstring'''





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

def        lowerCAmelCase__ (			a__:		Optional[int]       )  ->		str:


							'''simple docstring'''





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

def        lowerCAmelCase__ (			a__:		Optional[int]       )  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase 		=			torch.load(lowerCAmelCase__			,  map_location='cpu'       )
							_UpperCAmelCase 		=			Namespace(**checkpoint['cfg']['model']       )
							_UpperCAmelCase 		=			checkpoint['model']
							remove_ignore_keys_(lowerCAmelCase__       )
							_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=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			,  )

							_UpperCAmelCase 		=			XGLMForCausalLM(lowerCAmelCase__       )
							_UpperCAmelCase 		=			model.load_state_dict(lowerCAmelCase__			,  strict=lowerCAmelCase__       )
							print(lowerCAmelCase__       )
							_UpperCAmelCase 		=			make_linear_from_emb(model.model.embed_tokens       )

							return model


if __name__ == "__main__":
		lowerCAmelCase__  :int   			=							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.''')
		lowerCAmelCase__  :List[Any]   			=							parser.parse_args()
		lowerCAmelCase__  :Tuple   			=							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_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "UniSpeechForCTC",
				    "UniSpeechForPreTraining",
				    "UniSpeechForSequenceClassification",
				    "UniSpeechModel",
				    "UniSpeechPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_unispeech import (
								    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
								    UniSpeechForCTC,
								    UniSpeechForPreTraining,
								    UniSpeechForSequenceClassification,
								    UniSpeechModel,
								    UniSpeechPreTrainedModel,
								)

else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






