





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
__UpperCAmelCase	:       Tuple    	=							{
    "/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__							(   SCREAMING_SNAKE_CASE__)       ->   Any:
  # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
  # the original model
  __snake_case:						Optional[int]			  =						list(s_dict.keys())
  for key in keys:
    __snake_case:						List[Any]			  =						r""".*/layers_(\d+)"""
    __snake_case:						Any			  =						key
    if re.match(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
      __snake_case:						Union[str, Any]			  =						re.sub(r"""layers_(\d+)""" , r"""block/\1/layer""" , SCREAMING_SNAKE_CASE__)

    __snake_case:						Dict			  =						r"""(encoder|decoder)\/"""

    if re.match(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
      __snake_case:						Any			  =						re.match(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__).groups()
      if groups[0] == "encoder":
        __snake_case:						Tuple			  =						re.sub(r"""/mlp/""" , r"""/1/mlp/""" , SCREAMING_SNAKE_CASE__)
        __snake_case:						List[str]			  =						re.sub(r"""/pre_mlp_layer_norm/""" , r"""/1/layer_norm/""" , SCREAMING_SNAKE_CASE__)

      elif groups[0] == "decoder":
        __snake_case:						Any			  =						re.sub(r"""/mlp/""" , r"""/2/mlp/""" , SCREAMING_SNAKE_CASE__)
        __snake_case:						str			  =						re.sub(r"""/pre_mlp_layer_norm/""" , r"""/2/layer_norm/""" , SCREAMING_SNAKE_CASE__)

        # 2. Convert other classic mappings
    for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
      if old_key in new_key:
        __snake_case:						Dict			  =						new_key.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

    print(F'''{key} -> {new_key}''')
    __snake_case:						Optional[int]			  =						s_dict.pop(SCREAMING_SNAKE_CASE__)

  if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
    __snake_case:						int			  =						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:
    __snake_case:						Tuple			  =						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:
      __snake_case:						Dict			  =						s_dict[key].shape[0]
      __snake_case:						Any			  =						s_dict[key]
      for idx in range(SCREAMING_SNAKE_CASE__):
        __snake_case:						Optional[Any]			  =						expert_weihts[idx]
        print(F'''{key} -> {key.replace('expert/' , 'nested fstring')}''')

      s_dict.pop(SCREAMING_SNAKE_CASE__)

  return s_dict


__UpperCAmelCase	:       Union[str, Any]    	=							{
    "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__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   Tuple:
  # Convert a google style config to the hugging face fromat
  import regex as re

  with open(SCREAMING_SNAKE_CASE__ , """r""") as f:
    __snake_case:						Optional[Any]			  =						f.read()

  __snake_case:						Any			  =						re.findall(r"""(.*) = ([0-9.]*)""" , SCREAMING_SNAKE_CASE__)
  __snake_case:						Union[str, Any]			  =						{}
  for param, value in regex_match:
    if param in GIN_TO_CONFIG_MAPPING and value != "":
      __snake_case:						List[str]			  =						float(SCREAMING_SNAKE_CASE__) if """.""" in value else int(SCREAMING_SNAKE_CASE__)

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

  __snake_case:						Any			  =						num_experts
  __snake_case:						Tuple			  =						SwitchTransformersConfig(**SCREAMING_SNAKE_CASE__)
  return config







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__="./" , SCREAMING_SNAKE_CASE__=8)       ->   int:
  # Initialise PyTorch model

  print(F'''Loading flax weights from : {flax_checkpoint_path}''')
  __snake_case:						Tuple			  =						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__)

  if gin_file is not None:
    __snake_case:						Optional[Any]			  =						convert_gin_to_config(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
  else:
    __snake_case:						Dict			  =						SwitchTransformersConfig.from_pretrained(SCREAMING_SNAKE_CASE__)

  __snake_case:						Dict			  =						SwitchTransformersForConditionalGeneration(SCREAMING_SNAKE_CASE__)

  __snake_case:						Any			  =						flax_params["""target"""]
  __snake_case:						Optional[int]			  =						flatten_dict(SCREAMING_SNAKE_CASE__ , sep="""/""")
  __snake_case:						List[Any]			  =						rename_keys(SCREAMING_SNAKE_CASE__)
  __snake_case:						List[Any]			  =						unflatten_dict(SCREAMING_SNAKE_CASE__ , sep="""/""")

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

  print(F'''Save PyTorch model to {pytorch_dump_path}''')
  pt_model.save_pretrained(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      __UpperCAmelCase	:       int    	=							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")
      __UpperCAmelCase	:       Union[str, 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,
      )













import math







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   int:

  if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE__)

  if number < 1:
    __snake_case:						Optional[int]			  =						F'''Input value of [number={number}] must be > 0'''
    raise ValueError(SCREAMING_SNAKE_CASE__)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    __snake_case:						List[Any]			  =						int(math.log(number // 3 , 2)) + 2

    __snake_case:						str			  =						[3, 5]
    __snake_case:						int			  =						2
    __snake_case:						List[str]			  =						3
    for block in range(1 , SCREAMING_SNAKE_CASE__):
      for _ in range(SCREAMING_SNAKE_CASE__):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1])
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      for number in range(11):
            __UpperCAmelCase	:       Optional[int]    	=							0
            try:
                  __UpperCAmelCase	:       int    	=							proth(number)
            except ValueError:
                  print(f'ValueError: there is no {number}th Proth number')
                  continue

            print(f'The {number}th Proth number: {value}')






