


'''simple docstring'''
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 			a      (			__a   )  ->	Dict:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= [
					    '''decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__a ,					__a   )


def 			a      (			__a   )  ->	List[Any]:



					'''simple docstring'''






					UpperCamelCase__					,				UpperCamelCase__   :int												= emb.weight.shape
					UpperCamelCase__   :Tuple												= nn.Linear(__a ,					__a ,					bias=__a   )
					UpperCamelCase__   :Dict												= emb.weight.data
					return lin_layer


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= torch.load(__a ,					map_location='''cpu'''   )
					UpperCamelCase__   :Optional[int]												= Namespace(**checkpoint['''cfg''']['''model''']   )
					UpperCamelCase__   :Dict												= checkpoint['''model''']
					remove_ignore_keys_(__a   )
					UpperCamelCase__   :Optional[int]												= state_dict['''decoder.embed_tokens.weight'''].shape[0]

					UpperCamelCase__   :Optional[int]												= {key.replace('''decoder''' ,					'''model'''   ): val for key, val in state_dict.items()}

					UpperCamelCase__   :Union[str, Any]												= XGLMConfig(
					    vocab_size=__a ,					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__   :Any												= XGLMForCausalLM(__a   )
					UpperCamelCase__   :Dict												= model.load_state_dict(__a ,					strict=__a   )
					print(__a   )
					UpperCamelCase__   :Optional[int]												= 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)






'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


a__							:							Tuple ='''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
    import torch

    print('''Torch version:''', torch.__version__)
    print('''Cuda available:''', torch.cuda.is_available())
    print('''Cuda version:''', torch.version.cuda)
    print('''CuDNN version:''', torch.backends.cudnn.version())
    print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
    print('''Torch version:''', None)

try:
    import transformers

    print('''transformers version:''', transformers.__version__)
except ImportError:
    print('''transformers version:''', None)
