'''simple docstring'''
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
lowercase__  :      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  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  list(s_dict.keys()					)
      for key in keys:
            UpperCAmelCase_            =  R'''.*/layers_(\d+)'''
            UpperCAmelCase_            =  key
            if re.match(_UpperCamelCase							,    _UpperCamelCase					):
                  UpperCAmelCase_            =  re.sub(R'''layers_(\d+)'''							,    R'''block/\1/layer'''							,    _UpperCamelCase					)

            UpperCAmelCase_            =  R'''(encoder|decoder)\/'''

            if re.match(_UpperCamelCase							,    _UpperCamelCase					):
                  UpperCAmelCase_            =  re.match(_UpperCamelCase							,    _UpperCamelCase					).groups()
                  if groups[0] == "encoder":
                        UpperCAmelCase_            =  re.sub(R'''/mlp/'''							,    R'''/1/mlp/'''							,    _UpperCamelCase					)
                        UpperCAmelCase_            =  re.sub(R'''/pre_mlp_layer_norm/'''							,    R'''/1/layer_norm/'''							,    _UpperCamelCase					)

                  elif groups[0] == "decoder":
                        UpperCAmelCase_            =  re.sub(R'''/mlp/'''							,    R'''/2/mlp/'''							,    _UpperCamelCase					)
                        UpperCAmelCase_            =  re.sub(R'''/pre_mlp_layer_norm/'''							,    R'''/2/layer_norm/'''							,    _UpperCamelCase					)

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

            print(F"""{key} -> {new_key}"""					)
            UpperCAmelCase_            =  s_dict.pop(_UpperCamelCase					)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            UpperCAmelCase_            =  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_            =  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_            =  s_dict[key].shape[0]
                  UpperCAmelCase_            =  s_dict[key]
                  for idx in range(_UpperCamelCase					):
                        UpperCAmelCase_            =  expert_weihts[idx]
                        print(F"""{key} -> {key.replace("expert/"							,    "nested fstring"					)}"""					)

                  s_dict.pop(_UpperCamelCase					)

      return s_dict


lowercase__  :      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  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Any					):


      '''simple docstring'''


      import regex as re

      with open(_UpperCamelCase							,    '''r'''					) as f:
            UpperCAmelCase_            =  f.read()

      UpperCAmelCase_            =  re.findall(R'''(.*) = ([0-9.]*)'''							,    _UpperCamelCase					)
      UpperCAmelCase_            =  {}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  UpperCAmelCase_            =  float(_UpperCamelCase					) if '''.''' in value else int(_UpperCamelCase					)

      UpperCAmelCase_            =  re.findall(R'''(.*activations) = \(\'(.*)\',\)'''							,    _UpperCamelCase					)[0]
      UpperCAmelCase_            =  str(activation[1]					)

      UpperCAmelCase_            =  num_experts
      UpperCAmelCase_            =  SwitchTransformersConfig(**_UpperCamelCase					)
      return config



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Optional[int]=None							,    _UpperCamelCase  :  str="./"							,    _UpperCamelCase  :  int=8					):


      '''simple docstring'''


      print(F"""Loading flax weights from : {flax_checkpoint_path}"""					)
      UpperCAmelCase_            =  checkpoints.load_tax_checkpoint(_UpperCamelCase					)

      if gin_file is not None:
            UpperCAmelCase_            =  convert_gin_to_config(_UpperCamelCase							,    _UpperCamelCase					)
      else:
            UpperCAmelCase_            =  SwitchTransformersConfig.from_pretrained(_UpperCamelCase					)

      UpperCAmelCase_            =  SwitchTransformersForConditionalGeneration(_UpperCamelCase					)

      UpperCAmelCase_            =  flax_params['''target''']
      UpperCAmelCase_            =  flatten_dict(_UpperCamelCase							,    sep='''/'''					)
      UpperCAmelCase_            =  rename_keys(_UpperCamelCase					)
      UpperCAmelCase_            =  unflatten_dict(_UpperCamelCase							,    sep='''/'''					)

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

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


if __name__ == "__main__":
      lowercase__  :      Optional[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")
      lowercase__  :      Optional[int]        =					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,
      )






'''simple docstring'''
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ''''''
      for word_or_phrase in separated:
            if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
                  raise Exception('''join() accepts only strings to be joined'''					)
            joined += word_or_phrase + separator
      return joined.strip(_UpperCamelCase					)


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

      testmod()





