






"""simple docstring"""







import random






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase						     =      a[left_index]
       UpperCAmelCase						     =      left_index + 1
       for j in range(left_index + 1						,   A__		):
              if a[j] < pivot:
                     UpperCAmelCase						     =      a[i], a[j]
                     i += 1
       UpperCAmelCase						     =      a[i - 1], a[left_index]
       return i - 1






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase						,   lowerCAmelCase		):







       '''simple docstring'''
       if left < right:
              UpperCAmelCase						     =      random.randint(A__						,   right - 1		)
              UpperCAmelCase						     =      (
                  a[left],
                  a[pivot],
              )  # switches the pivot with the left most bound
              UpperCAmelCase						     =      partition(A__						,   A__						,   A__		)
              quick_sort_random(
                  A__						,   A__						,   A__		)  # recursive quicksort to the left of the pivot point
              quick_sort_random(
                  A__						,   pivot_index + 1						,   A__		)  # recursive quicksort to the right of the pivot point






def    _lowerCAmelCase							(   ):







       '''simple docstring'''
       UpperCAmelCase						     =      input("""Enter numbers separated by a comma:\n"""		).strip()
       UpperCAmelCase						     =      [int(A__		) for item in user_input.split(""","""		)]

       quick_sort_random(A__						,   0						,   len(A__		)		)

       print(A__		)


if __name__ == "__main__":
 main()




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Any					=					[
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'encoder.embed_positions._float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(A__,      A__				)






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[Any]					=					emb.weight.shape
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Linear(A__,      A__,      bias=A__				)
			SCREAMING_SNAKE_CASE_		:     Dict					=					emb.weight.data
			return lin_layer






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.load(A__,      map_location='cpu'				)
			SCREAMING_SNAKE_CASE_		:     Tuple					=					mam_aaa['args'] or mam_aaa['cfg']['model']
			SCREAMING_SNAKE_CASE_		:     Any					=					mam_aaa['model']
			remove_ignore_keys_(A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					state_dict['encoder.embed_tokens.weight'].shape[0]

			SCREAMING_SNAKE_CASE_		:     Dict					=					MaMaaaConfig(
			    vocab_size=A__,      max_position_embeddings=1_0_2_4,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      encoder_layerdrop=args.encoder_layerdrop,      decoder_layerdrop=args.decoder_layerdrop,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='relu',      )

			SCREAMING_SNAKE_CASE_		:     int					=					state_dict['decoder.embed_tokens.weight']
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					MaMaaaForConditionalGeneration(A__				)
			model.model.load_state_dict(A__,      strict=A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					make_linear_from_emb(model.model.shared				)

			return model


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[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__			:      Optional[Any]								=parser.parse_args()
				lowerCAmelCase__			:      int								=convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
				model.save_pretrained(args.pytorch_dump_folder_path)





