


'''simple docstring'''



import argparse

import torch

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


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

__A		=[
    ['''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 			_UpperCamelCase					(						UpperCamelCase__    ):
			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						UpperCAmelCase__     :    Dict         =   k.replace(_UpperCAmelCase       ,					_UpperCAmelCase    )

			if k.startswith("""encoder"""    ):
						UpperCAmelCase__     :    Tuple         =   k.replace(""".attn"""       ,					""".self_attn"""    )
						UpperCAmelCase__     :    List[str]         =   k.replace("""norm1"""       ,					"""self_attn_layer_norm"""    )
						UpperCAmelCase__     :    Optional[Any]         =   k.replace("""norm2"""       ,					"""final_layer_norm"""    )
			elif k.startswith("""decoder"""    ):
						UpperCAmelCase__     :    Union[str, Any]         =   k.replace("""norm1"""       ,					"""self_attn_layer_norm"""    )
						UpperCAmelCase__     :    Tuple         =   k.replace("""norm2"""       ,					"""encoder_attn_layer_norm"""    )
						UpperCAmelCase__     :    List[str]         =   k.replace("""norm3"""       ,					"""final_layer_norm"""    )
			return k







def 			_UpperCamelCase					(						UpperCamelCase__    ):
			UpperCAmelCase__     :    Optional[Any]         =   [
			    """model.encoder.layernorm_embedding.weight""",
			    """model.encoder.layernorm_embedding.bias""",
			    """model.decoder.layernorm_embedding.weight""",
			    """model.decoder.layernorm_embedding.bias""",
			]
			for k in keys:
						UpperCAmelCase__     :    Optional[int]         =   sd.pop(_UpperCAmelCase    )
						UpperCAmelCase__     :    Any         =   k.replace("""layernorm_embedding"""       ,					"""layer_norm"""    )
						assert new_k not in sd
						UpperCAmelCase__     :    List[str]         =   v


__A		=['''START''']







@torch.no_grad()
def 			_UpperCamelCase					(						UpperCamelCase__       ,					UpperCamelCase__       ,					UpperCamelCase__    ):
			UpperCAmelCase__     :    int         =   torch.load(_UpperCAmelCase       ,					map_location="""cpu"""    )
			UpperCAmelCase__     :    Union[str, Any]         =   model["""model"""]
			UpperCAmelCase__     :    Union[str, Any]         =   BlenderbotConfig.from_json_file(_UpperCAmelCase    )
			UpperCAmelCase__     :    Any         =   BlenderbotForConditionalGeneration(_UpperCAmelCase    )
			UpperCAmelCase__     :    Optional[int]         =   m.model.state_dict().keys()
			UpperCAmelCase__     :    Tuple         =   []
			UpperCAmelCase__     :    List[str]         =   {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

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


if __name__ == "__main__":
		__A		=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'
		)
		__A		=parser.parse_args()
		convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




from collections import defaultdict





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				first_str.lower().strip()
    __a						       =				second_str.lower().strip()

    # Remove whitespace
    __a						       =				first_str.replace(''' '''   , ''''''				)
    __a						       =				second_str.replace(''' '''   , ''''''				)

    # Strings of different lengths are not anagrams
    if len(_UpperCAmelCase				) != len(_UpperCAmelCase				):
        return False

    # Default values for count should be 0
    __a						       =				defaultdict(_UpperCAmelCase				)

    # For each character in input strings,
    # increment count in the corresponding
    for i in range(len(_UpperCAmelCase				)				):
        count[first_str[i]] += 1
        count[second_str[i]] -= 1

    return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 __snake_case						:Any      		=     input('''Enter the first string ''').strip()
 __snake_case						:int      		=     input('''Enter the second string ''').strip()

 __snake_case						:int      		=     check_anagrams(input_a, input_b)
 print(f'{input_a} and {input_b} are {"" if status else "not "}anagrams.')





