





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__lowerCamelCase	:					Union[str, Any]			  =					{
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}




def 		A_    (					_lowerCAmelCase					)						->			Any:
 UpperCamelCase     : int	     =       list(s_dict.keys()					)
 for key in keys:
  UpperCamelCase     : List[str]	     =       r".*/layers_(\d+)"
  UpperCamelCase     : Optional[int]	     =       key
  if re.match(lowerCamelCase__							,       lowerCamelCase__					):
   UpperCamelCase     : Dict	     =       re.sub(r"layers_(\d+)"							,       r"block/\1/layer"							,       lowerCamelCase__					)

  UpperCamelCase     : int	     =       r"(encoder|decoder)\/"

  if re.match(lowerCamelCase__							,       lowerCamelCase__					):
   UpperCamelCase     : int	     =       re.match(lowerCamelCase__							,       lowerCamelCase__					).groups()
   if groups[0] == "encoder":
    UpperCamelCase     : Any	     =       re.sub(r"/mlp/"							,       r"/1/mlp/"							,       lowerCamelCase__					)
    UpperCamelCase     : List[Any]	     =       re.sub(r"/pre_mlp_layer_norm/"							,       r"/1/layer_norm/"							,       lowerCamelCase__					)

   elif groups[0] == "decoder":
    UpperCamelCase     : Dict	     =       re.sub(r"/mlp/"							,       r"/2/mlp/"							,       lowerCamelCase__					)
    UpperCamelCase     : Optional[Any]	     =       re.sub(r"/pre_mlp_layer_norm/"							,       r"/2/layer_norm/"							,       lowerCamelCase__					)

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    UpperCamelCase     : List[Any]	     =       new_key.replace(lowerCamelCase__							,       lowerCamelCase__					)

  print(F"""{key} -> {new_key}"""					)
  UpperCamelCase     : Union[str, Any]	     =       s_dict.pop(lowerCamelCase__					)

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCamelCase     : Any	     =       s_dict[
      "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
  ].T
 if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  UpperCamelCase     : Dict	     =       s_dict[
      "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
  ].T

 # 3. Take extra care of the EXPERTS layer
 for key in list(s_dict.keys()					):
  if "expert" in key:
   UpperCamelCase     : Dict	     =       s_dict[key].shape[0]
   UpperCamelCase     : int	     =       s_dict[key]
   for idx in range(lowerCamelCase__					):
    UpperCamelCase     : Optional[Any]	     =       expert_weihts[idx]
    print(F"""{key} -> {key.replace("expert/"							,       "nested fstring"					)}"""					)

   s_dict.pop(lowerCamelCase__					)

 return s_dict


__lowerCamelCase	:					Tuple			  =					{
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}




def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 import regex as re

 with open(lowerCamelCase__							,       "r"					) as f:
  UpperCamelCase     : List[str]	     =       f.read()

 UpperCamelCase     : Any	     =       re.findall(r"(.*) = ([0-9.]*)"							,       lowerCamelCase__					)
 UpperCamelCase     : Tuple	     =       {}
 for param, value in regex_match:
  if param in GIN_TO_CONFIG_MAPPING and value != "":
   UpperCamelCase     : Tuple	     =       float(lowerCamelCase__					) if "." in value else int(lowerCamelCase__					)

 UpperCamelCase     : Optional[Any]	     =       re.findall(r"(.*activations) = \(\'(.*)\',\)"							,       lowerCamelCase__					)[0]
 UpperCamelCase     : Tuple	     =       str(activation[1]					)

 UpperCamelCase     : int	     =       num_experts
 UpperCamelCase     : Optional[Any]	     =       SwitchTransformersConfig(**lowerCamelCase__					)
 return config




def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase="./"							,       _lowerCAmelCase=8					)						->			Optional[Any]:
 print(F"""Loading flax weights from : {flax_checkpoint_path}"""					)
 UpperCamelCase     : Optional[int]	     =       checkpoints.load_tax_checkpoint(lowerCamelCase__					)

 if gin_file is not None:
  UpperCamelCase     : Optional[Any]	     =       convert_gin_to_config(lowerCamelCase__							,       lowerCamelCase__					)
 else:
  UpperCamelCase     : Any	     =       SwitchTransformersConfig.from_pretrained(lowerCamelCase__					)

 UpperCamelCase     : Optional[Any]	     =       SwitchTransformersForConditionalGeneration(lowerCamelCase__					)

 UpperCamelCase     : Optional[Any]	     =       flax_params["target"]
 UpperCamelCase     : Tuple	     =       flatten_dict(lowerCamelCase__							,       sep="/"					)
 UpperCamelCase     : str	     =       rename_keys(lowerCamelCase__					)
 UpperCamelCase     : Optional[int]	     =       unflatten_dict(lowerCamelCase__							,       sep="/"					)

 # Load the flax params in the PT model
 load_flax_weights_in_pytorch_model(lowerCamelCase__							,       lowerCamelCase__					)

 print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
 pt_model.save_pretrained(lowerCamelCase__					)


if __name__ == "__main__":
    __lowerCamelCase	:					List[Any]			  =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--switch_t5x_checkpoint_path""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
            """ model architecture. If not provided, a `gin_file` has to be provided."""
        ),
    )
    parser.add_argument(
        """--gin_file""",
        default=None,
        type=str,
        required=False,
        help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
    )
    parser.add_argument(
        """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
    )
    parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
    __lowerCamelCase	:					List[Any]			  =					parser.parse_args()
    convert_flax_checkpoint_to_pytorch(
        args.switch_tax_checkpoint_path,
        args.config_name,
        args.gin_file,
        args.pytorch_dump_folder_path,
        args.num_experts,
    )







def 		A_    (					_lowerCAmelCase					)						->			int:
 if n == 1 or not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  return 0
 elif n == 2:
  return 1
 else:
  UpperCamelCase     : Optional[Any]	     =       [0, 1]
  for i in range(2							,       n + 1					):
   sequence.append(sequence[i - 1] + sequence[i - 2]					)

  return sequence[n]



def 		A_    (					_lowerCAmelCase					)						->			int:
 UpperCamelCase     : Any	     =       0
 UpperCamelCase     : List[Any]	     =       2

 while digits < n:
  index += 1
  UpperCamelCase     : Union[str, Any]	     =       len(str(fibonacci(_lowerCAmelCase					)					)					)

 return index



def 		A_    (					_lowerCAmelCase = 1000					)						->			int:
 return fibonacci_digits_index(_lowerCAmelCase					)


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))
