



'''simple docstring'''





import copy
import re






class __A     :



 '''simple docstring'''







 __lowerCamelCase       :			List[Any]		=			'hp'
 __lowerCamelCase       :			str		=			{}
 __lowerCamelCase       :			Tuple		=			None



 @classmethod
 def    a__    (cls					,  A					,  A			)       ->	int:


      """simple docstring"""



      _a           =    prefix
      _a           =    defaults
      cls.build_naming_info()



 @staticmethod
 def    a__    (A					,  A			)       ->	Union[str, Any]:


      """simple docstring"""



      if len(A			) == 0:
           return ""
      _a           =    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(A			) + 1			):
           _a           =    word[:prefix_len]
           if prefix in info["reverse_short_word"]:
                continue
           else:
                _a           =    prefix
                break

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

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

      _a           =    short_word
      _a           =    word
      return short_word



 @staticmethod
 def    a__    (A					,  A			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    param_name.split('''_'''			)

      _a           =    [TrialShortNamer.shortname_for_word(A					,  A			) 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
      _a           =    ['''''', '''_''']

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

      return param_name



 @staticmethod
 def    a__    (A					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    TrialShortNamer.shortname_for_key(A					,  A			)
      _a           =    short_name
      _a           =    param_name



 @classmethod
 def    a__    (cls			)       ->	Dict:


      """simple docstring"""



      if cls.NAMING_INFO is not None:
           return

      _a           =    {
          '''short_word''': {},
          '''reverse_short_word''': {},
          '''short_param''': {},
          '''reverse_short_param''': {},
      }

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

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

      _a           =    info



 @classmethod
 def    a__    (cls					,  A			)       ->	Tuple:


      """simple docstring"""



      cls.build_naming_info()
      assert cls.PREFIX is not None
      _a           =    [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

           _a           =    cls.NAMING_INFO['''short_param'''][k]

           if isinstance(A					,  A			):
                _a           =    1 if v else 0

           _a           =    '''''' if isinstance(A					,  (int, float)			) else '''-'''
           _a           =    f'''{key}{sep}{v}'''
           name.append(A			)

      return "_".join(A			)



 @classmethod
 def    a__    (cls					,  A			)       ->	List[Any]:


      """simple docstring"""



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

      _a           =    {}

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

           _a           =    cls.NAMING_INFO['''reverse_short_param'''][p_k]

           _a           =    p_v

      for k in cls.DEFAULTS:
           if k not in parameters:
                _a           =    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
lowercase_									=    {
    "/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    (__A):



     """simple docstring"""



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

          _a           =    r'''(encoder|decoder)\/'''

          if re.match(__A    ,      __A):
               _a           =    re.match(__A    ,      __A).groups()
               if groups[0] == "encoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/1/mlp/'''    ,      __A)
                    _a           =    re.sub(r'''/pre_mlp_layer_norm/'''    ,      r'''/1/layer_norm/'''    ,      __A)

               elif groups[0] == "decoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/2/mlp/'''    ,      __A)
                    _a           =    re.sub(r'''/pre_mlp_layer_norm/'''    ,      r'''/2/layer_norm/'''    ,      __A)

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

          print(F'''{key} -> {new_key}''')
          _a           =    s_dict.pop(__A)

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

               s_dict.pop(__A)

     return s_dict


lowercase_									=    {
    "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    (__A    ,      __A):



     """simple docstring"""



     import regex as re

     with open(__A    ,      '''r''') as f:
          _a           =    f.read()

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

     _a           =    re.findall(r'''(.*activations) = \(\'(.*)\',\)'''    ,      __A)[0]
     _a           =    str(activation[1])

     _a           =    num_experts
     _a           =    SwitchTransformersConfig(**__A)
     return config




def lowerCAmelCase    (__A    ,      __A    ,      __A=None    ,      __A="./"    ,      __A=8):



     """simple docstring"""



     print(F'''Loading flax weights from : {flax_checkpoint_path}''')
     _a           =    checkpoints.load_tax_checkpoint(__A)

     if gin_file is not None:
          _a           =    convert_gin_to_config(__A    ,      __A)
     else:
          _a           =    SwitchTransformersConfig.from_pretrained(__A)

     _a           =    SwitchTransformersForConditionalGeneration(__A)

     _a           =    flax_params['''target''']
     _a           =    flatten_dict(__A    ,      sep='''/''')
     _a           =    rename_keys(__A)
     _a           =    unflatten_dict(__A    ,      sep='''/''')

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

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


if __name__ == "__main__":
  lowercase_									=    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_									=    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,
  )



