





'''simple docstring'''

import copy
import re







class __a			:
   __UpperCamelCase :			List[Any]						   =						'hp'
   __UpperCamelCase :			int						   =						{}
   __UpperCamelCase :			List[str]						   =						None





   @classmethod
   def   UpperCAmelCase__  (       cls					:      List[str]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							prefix
      __SCREAMING_SNAKE_CASE   				=							defaults
      cls.build_naming_info()





   @staticmethod
   def   UpperCAmelCase__  (       lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      if len(lowerCamelCase     ) == 0:
         return ""
      __SCREAMING_SNAKE_CASE   				=							None
      if any(char.isdigit() for char in word     ):
         raise Exception(f"""Parameters should not contain numbers: '{word}' contains a number"""     )
      if word in info["short_word"]:
         return info["short_word"][word]
      for prefix_len in range(1					,len(lowerCamelCase     ) + 1     ):
         __SCREAMING_SNAKE_CASE   				=							word[:prefix_len]
         if prefix in info["reverse_short_word"]:
            continue
         else:
            __SCREAMING_SNAKE_CASE   				=							prefix
            break

      if short_word is None:
         # Paranoid fallback
         def int_to_alphabetic(lowerCamelCase					:      Tuple     ):
            __SCREAMING_SNAKE_CASE   				=							""""""
            while integer != 0:
               __SCREAMING_SNAKE_CASE   				=							chr(ord("""A"""     ) + integer % 10     ) + s
               integer //= 10
            return s

         __SCREAMING_SNAKE_CASE   				=							0
         while True:
            __SCREAMING_SNAKE_CASE   				=							word + """#""" + int_to_alphabetic(lowerCamelCase     )
            if sword in info["reverse_short_word"]:
               continue
            else:
               __SCREAMING_SNAKE_CASE   				=							sword
               break

      __SCREAMING_SNAKE_CASE   				=							short_word
      __SCREAMING_SNAKE_CASE   				=							word
      return short_word





   @staticmethod
   def   UpperCAmelCase__  (       lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							param_name.split("""_"""     )

      __SCREAMING_SNAKE_CASE   				=							[TrialShortNamer.shortname_for_word(lowerCamelCase					,lowerCamelCase     ) for word in words]

      # We try to create a separatorless short name, but if there is a collision we have to fallback
      # to a separated short name
      __SCREAMING_SNAKE_CASE   				=							["""""", """_"""]

      for separator in separators:
         __SCREAMING_SNAKE_CASE   				=							separator.join(lowerCamelCase     )
         if shortname not in info["reverse_short_param"]:
            __SCREAMING_SNAKE_CASE   				=							shortname
            __SCREAMING_SNAKE_CASE   				=							param_name
            return shortname

      return param_name





   @staticmethod
   def   UpperCAmelCase__  (       lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							TrialShortNamer.shortname_for_key(lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							short_name
      __SCREAMING_SNAKE_CASE   				=							param_name





   @classmethod
   def   UpperCAmelCase__  (       cls					:      Dict     ):

      '''simple docstring'''

      if cls.NAMING_INFO is not None:
         return

      __SCREAMING_SNAKE_CASE   				=							{
          """short_word""": {},
          """reverse_short_word""": {},
          """short_param""": {},
          """reverse_short_param""": {},
      }

      __SCREAMING_SNAKE_CASE   				=							list(cls.DEFAULTS.keys()     )

      for k in field_keys:
         cls.add_new_param_name(lowerCamelCase					,lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							info





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      cls.build_naming_info()
      assert cls.PREFIX is not None
      __SCREAMING_SNAKE_CASE   				=							[copy.copy(cls.PREFIX     )]

      for k, v in params.items():
         if k not in cls.DEFAULTS:
            raise Exception(f"""You should provide a default value for the param name {k} with value {v}"""     )
         if v == cls.DEFAULTS[k]:
            # The default value is not added to the name
            continue

         __SCREAMING_SNAKE_CASE   				=							cls.NAMING_INFO["""short_param"""][k]

         if isinstance(lowerCamelCase					,lowerCamelCase     ):
            __SCREAMING_SNAKE_CASE   				=							1 if v else 0

         __SCREAMING_SNAKE_CASE   				=							"""""" if isinstance(lowerCamelCase					,(int, float)     ) else """-"""
         __SCREAMING_SNAKE_CASE   				=							f"""{key}{sep}{v}"""
         name.append(lowerCamelCase     )

      return "_".join(lowerCamelCase     )





   @classmethod
   def   UpperCAmelCase__  (       cls					:      Optional[Any]					,lowerCamelCase					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							repr[len(cls.PREFIX     ) + 1 :]
      if repr == "":
         __SCREAMING_SNAKE_CASE   				=							[]
      else:
         __SCREAMING_SNAKE_CASE   				=							repr.split("""_"""     )

      __SCREAMING_SNAKE_CASE   				=							{}

      for value in values:
         if "-" in value:
            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							value.split("""-"""     )
         else:
            __SCREAMING_SNAKE_CASE   				=							re.sub("""[0-9.]"""					,""""""					,lowerCamelCase     )
            __SCREAMING_SNAKE_CASE   				=							float(re.sub("""[^0-9.]"""					,""""""					,lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							cls.NAMING_INFO["""reverse_short_param"""][p_k]

         __SCREAMING_SNAKE_CASE   				=							p_v

      for k in cls.DEFAULTS:
         if k not in parameters:
            __SCREAMING_SNAKE_CASE   				=							cls.DEFAULTS[k]

      return parameters












'''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
a   	=    {
    "/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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







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

      __SCREAMING_SNAKE_CASE   				=							R"""(encoder|decoder)\/"""

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

         elif groups[0] == "decoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/2/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							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:
            __SCREAMING_SNAKE_CASE   				=							new_key.replace(__UpperCAmelCase				,						__UpperCAmelCase   )

      print(f"""{key} -> {new_key}"""   )
      __SCREAMING_SNAKE_CASE   				=							s_dict.pop(__UpperCAmelCase   )

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

         s_dict.pop(__UpperCAmelCase   )

   return s_dict


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   import regex as re

   with open(__UpperCAmelCase				,						"""r"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.read()

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

   __SCREAMING_SNAKE_CASE   				=							re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,						__UpperCAmelCase   )[0]
   __SCREAMING_SNAKE_CASE   				=							str(activation[1]   )

   __SCREAMING_SNAKE_CASE   				=							num_experts
   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig(**__UpperCAmelCase   )
   return config

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







   print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
   __SCREAMING_SNAKE_CASE   				=							checkpoints.load_tax_checkpoint(__UpperCAmelCase   )

   if gin_file is not None:
      __SCREAMING_SNAKE_CASE   				=							convert_gin_to_config(__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							flax_params["""target"""]
   __SCREAMING_SNAKE_CASE   				=							flatten_dict(__UpperCAmelCase				,						sep="""/"""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							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__":
    a   	=    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")
    a   	=    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,
    )





