



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Any	)  ->						List[Any]:
					__lowerCAmelCase	   =[
					    """decoder.version""",
					    """decoder.output_projection.weight""",
					    """_float_tensor""",
					    """decoder.embed_positions._float_tensor""",
					]
					for k in ignore_keys:
										state_dict.pop(UpperCamelCase__				,   UpperCamelCase__	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]	)  ->						int:
					__lowerCAmelCase   ,	__lowerCAmelCase	   =emb.weight.shape
					__lowerCAmelCase	   =nn.Linear(UpperCamelCase__				,   UpperCamelCase__				,   bias=UpperCamelCase__	)
					__lowerCAmelCase	   =emb.weight.data
					return lin_layer






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple	)  ->						str:
					__lowerCAmelCase	   =torch.load(UpperCamelCase__				,   map_location="""cpu"""	)
					__lowerCAmelCase	   =Namespace(**checkpoint["""cfg"""]["""model"""]	)
					__lowerCAmelCase	   =checkpoint["""model"""]
					remove_ignore_keys_(UpperCamelCase__	)
					__lowerCAmelCase	   =state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

					__lowerCAmelCase	   =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				,   )

					__lowerCAmelCase	   =XGLMForCausalLM(UpperCamelCase__	)
					__lowerCAmelCase	   =model.load_state_dict(UpperCamelCase__				,   strict=UpperCamelCase__	)
					print(UpperCamelCase__	)
					__lowerCAmelCase	   =make_linear_from_emb(model.model.embed_tokens	)

					return model


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









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[int]	)  ->						int:
					if not numbers:
										return 0

					if not isinstance(__lowerCamelCase				,   (list, tuple)	) or not all(
					    isinstance(__lowerCamelCase				,   __lowerCamelCase	) for number in numbers	):
										raise ValueError("""numbers must be an iterable of integers"""	)

					__lowerCAmelCase	   =__lowerCAmelCase	   =__lowerCAmelCase	   =numbers[0]

					for i in range(1				,   len(__lowerCamelCase	)	):
										# update the maximum and minimum subarray products
										__lowerCAmelCase	   =numbers[i]
										if number < 0:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =min_till_now, max_till_now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   max_till_now * number	)
										__lowerCAmelCase	   =min(__lowerCamelCase				,   min_till_now * number	)

										# update the maximum product found till now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   __lowerCamelCase	)

					return max_prod




