




"""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  UpperCAmelCase__	(snake_case__							: Any   ):
  """simple docstring"""

  _snake_case							:		List[str]								=  list(s_dict.keys()   )
  for key in keys:
    _snake_case							:		Tuple								=  R""".*/layers_(\d+)"""
    _snake_case							:		Any								=  key
    if re.match(__a		,       __a   ):
      _snake_case							:		List[Any]								=  re.sub(R"""layers_(\d+)"""		,       R"""block/\1/layer"""		,       __a   )

    _snake_case							:		int								=  R"""(encoder|decoder)\/"""

    if re.match(__a		,       __a   ):
      _snake_case							:		List[str]								=  re.match(__a		,       __a   ).groups()
      if groups[0] == "encoder":
        _snake_case							:		int								=  re.sub(R"""/mlp/"""		,       R"""/1/mlp/"""		,       __a   )
        _snake_case							:		Dict								=  re.sub(R"""/pre_mlp_layer_norm/"""		,       R"""/1/layer_norm/"""		,       __a   )

      elif groups[0] == "decoder":
        _snake_case							:		Union[str, Any]								=  re.sub(R"""/mlp/"""		,       R"""/2/mlp/"""		,       __a   )
        _snake_case							:		str								=  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:
        _snake_case							:		int								=  new_key.replace(__a		,       __a   )

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

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

      s_dict.pop(__a   )

  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  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  import regex as re

  with open(__a		,       """r"""   ) as f:
    _snake_case							:		List[Any]								=  f.read()

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

  _snake_case							:		List[Any]								=  re.findall(R"""(.*activations) = \(\'(.*)\',\)"""		,       __a   )[0]
  _snake_case							:		str								=  str(activation[1]   )

  _snake_case							:		str								=  num_experts
  _snake_case							:		Any								=  SwitchTransformersConfig(**__a   )
  return config


def  UpperCAmelCase__	(snake_case__							: List[Any]		,       snake_case__							: int		,       snake_case__							: Union[str, Any]=None		,       snake_case__							: Union[str, Any]="./"		,       snake_case__							: Dict=8   ):
  """simple docstring"""

  print(F"Loading flax weights from : {flax_checkpoint_path}"   )
  _snake_case							:		Union[str, Any]								=  checkpoints.load_tax_checkpoint(__a   )

  if gin_file is not None:
    _snake_case							:		List[str]								=  convert_gin_to_config(__a		,       __a   )
  else:
    _snake_case							:		str								=  SwitchTransformersConfig.from_pretrained(__a   )

  _snake_case							:		str								=  SwitchTransformersForConditionalGeneration(__a   )

  _snake_case							:		int								=  flax_params["""target"""]
  _snake_case							:		List[Any]								=  flatten_dict(__a		,       sep="""/"""   )
  _snake_case							:		List[str]								=  rename_keys(__a   )
  _snake_case							:		str								=  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__":
     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,
     )








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  []
  _snake_case							:		Dict								=  set({"""(""", """[""", """{"""}   )
  _snake_case							:		Union[str, Any]								=  set({""")""", """]""", """}"""}   )
  _snake_case							:		Tuple								=  {"""{""": """}""", """[""": """]""", """(""": """)"""}

  for i in range(len(snake_case__   )   ):
    if s[i] in open_brackets:
      stack.append(s[i]   )

    elif s[i] in closed_brackets and (
        len(snake_case__   ) == 0 or (len(snake_case__   ) > 0 and open_to_closed[stack.pop()] != s[i])
    ):
      return False

  return len(snake_case__   ) == 0


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Any								=  input("""Enter sequence of brackets: """   )
  if is_balanced(snake_case__   ):
    print(snake_case__		,       """is balanced"""   )
  else:
    print(snake_case__		,       """is not balanced"""   )


if __name__ == "__main__":
     main()


