



import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_	       =			logging.get_logger(__name__)

lowercase_	       =			[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]	)  ->						Union[str, Any]:
					if k == "embeddings.weight":
										return "shared.weight"

					for parlai_name, hf_name in PATTERNS:
										__lowerCAmelCase	   =k.replace(__lowerCamelCase				,   __lowerCamelCase	)

					if k.startswith("""encoder"""	):
										__lowerCAmelCase	   =k.replace(""".attn"""				,   """.self_attn"""	)
										__lowerCAmelCase	   =k.replace("""norm1"""				,   """self_attn_layer_norm"""	)
										__lowerCAmelCase	   =k.replace("""norm2"""				,   """final_layer_norm"""	)
					elif k.startswith("""decoder"""	):
										__lowerCAmelCase	   =k.replace("""norm1"""				,   """self_attn_layer_norm"""	)
										__lowerCAmelCase	   =k.replace("""norm2"""				,   """encoder_attn_layer_norm"""	)
										__lowerCAmelCase	   =k.replace("""norm3"""				,   """final_layer_norm"""	)
					return k






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple	)  ->						str:
					__lowerCAmelCase	   =[
					    """model.encoder.layernorm_embedding.weight""",
					    """model.encoder.layernorm_embedding.bias""",
					    """model.decoder.layernorm_embedding.weight""",
					    """model.decoder.layernorm_embedding.bias""",
					]
					for k in keys:
										__lowerCAmelCase	   =sd.pop(__lowerCamelCase	)
										__lowerCAmelCase	   =k.replace("""layernorm_embedding"""				,   """layer_norm"""	)
										assert new_k not in sd
										__lowerCAmelCase	   =v


lowercase_	       =			['''START''']






@torch.no_grad()
def 					__lowerCAmelCase		(  __lowerCamelCase	:    int				,   __lowerCamelCase	:    Any				,   __lowerCamelCase	:    str	)  ->						Union[str, Any]:
					__lowerCAmelCase	   =torch.load(__lowerCamelCase				,   map_location="""cpu"""	)
					__lowerCAmelCase	   =model["""model"""]
					__lowerCAmelCase	   =BlenderbotConfig.from_json_file(__lowerCamelCase	)
					__lowerCAmelCase	   =BlenderbotForConditionalGeneration(__lowerCamelCase	)
					__lowerCAmelCase	   =m.model.state_dict().keys()
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   ={}
					for k, v in sd.items():
										if k in IGNORE_KEYS:
															continue

										__lowerCAmelCase	   =rename_state_dict_key(__lowerCamelCase	)
										if new_k not in valid_keys:
															failures.append([k, new_k]	)
										else:
															__lowerCAmelCase	   =v
					if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
										rename_layernorm_keys(__lowerCamelCase	)
					m.model.load_state_dict(__lowerCamelCase				,   strict=__lowerCamelCase	)
					m.half()
					m.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
				lowercase_	       =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
				parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
				parser.add_argument(
				    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
				)
				lowercase_	       =			parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)








def 					__lowerCAmelCase		(  __lowerCamelCase	:    int	)  ->						list:
					__lowerCAmelCase	   =int(__lowerCamelCase	)
					if n_element < 1:
										__lowerCAmelCase	   =ValueError("""a should be a positive number"""	)
										raise my_error

					__lowerCAmelCase	   =[1]
					__lowerCAmelCase   ,	__lowerCAmelCase   ,	__lowerCAmelCase	   =(0, 0, 0)
					__lowerCAmelCase	   =1
					while index < n_element:
										while hamming_list[i] * 2 <= hamming_list[-1]:
															i += 1
										while hamming_list[j] * 3 <= hamming_list[-1]:
															j += 1
										while hamming_list[k] * 5 <= hamming_list[-1]:
															k += 1
										hamming_list.append(
										    min(hamming_list[i] * 2				,   hamming_list[j] * 3				,   hamming_list[k] * 5	)	)
										index += 1
					return hamming_list


if __name__ == "__main__":
				lowercase_	       =			input('''Enter the last number (nth term) of the Hamming Number Series: ''')
				print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
				lowercase_	       =			hamming(int(n))
				print('''-----------------------------------------------------''')
				print(F"The list with nth numbers is: {hamming_numbers}")
				print('''-----------------------------------------------------''')




