




"""simple docstring"""







def 							lowercase					(snake_case__   : list )     ->  list:


				'''simple docstring'''






				if len(snake_case__ ) < 2:
								return collection

				def circle_sort_util(snake_case__   : list      ,       snake_case__   : int      ,       snake_case__   : int ) -> bool:
								lowerCAmelCase  =				False

								if low == high:
												return swapped

								lowerCAmelCase  =				low
								lowerCAmelCase  =				high

								while left < right:
												if collection[left] > collection[right]:
																lowerCAmelCase ,						lowerCAmelCase  =				(
																    collection[right],
																    collection[left],
																)
																lowerCAmelCase  =				True

												left += 1
												right -= 1

								if left == right and collection[left] > collection[right + 1]:
												lowerCAmelCase ,						lowerCAmelCase  =				(
												    collection[right + 1],
												    collection[left],
												)

												lowerCAmelCase  =				True

								lowerCAmelCase  =				low + int((high - low) / 2 )
								lowerCAmelCase  =				circle_sort_util(snake_case__      ,       snake_case__      ,       snake_case__ )
								lowerCAmelCase  =				circle_sort_util(snake_case__      ,       mid + 1      ,       snake_case__ )

								return swapped or left_swap or right_swap

				lowerCAmelCase  =				True

				while is_not_sorted is True:
								lowerCAmelCase  =				circle_sort_util(snake_case__      ,       0      ,       len(snake_case__ ) - 1 )

				return collection


if __name__ == "__main__":
		a     :   Optional[int]      =							input('Enter numbers separated by a comma:\n').strip()
		a     :   Optional[Any]      =							[int(item) for item in user_input.split(',')]
		print(circle_sort(unsorted))











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """decoder.version""",
				    """decoder.output_projection.weight""",
				    """_float_tensor""",
				    """decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				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=snake_case__      ,       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(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


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





