



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

            __snake_case :   Union[str, Any]													=    r'(encoder|decoder)\/'

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

                  elif groups[0] == "decoder":
                        __snake_case :   Any													=    re.sub(r'/mlp/' ,r'/2/mlp/' ,_UpperCAmelCase				)
                        __snake_case :   str													=    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:
                        __snake_case :   Any													=    new_key.replace(_UpperCAmelCase ,_UpperCAmelCase				)

            print(f'''{key} -> {new_key}'''				)
            __snake_case :   Tuple													=    s_dict.pop(_UpperCAmelCase				)

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

                  s_dict.pop(_UpperCAmelCase				)

      return s_dict


A__    :   int           =					{
    '''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_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      str				)    ->  str:
      # Convert a google style config to the hugging face fromat
      import regex as re

      with open(_UpperCAmelCase ,'r'				) as f:
            __snake_case :   List[str]													=    f.read()

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

      __snake_case :   List[str]													=    re.findall(r'(.*activations) = \(\'(.*)\',\)' ,_UpperCAmelCase				)[0]
      __snake_case :   Union[str, Any]													=    str(activation[1]				)

      __snake_case :   List[str]													=    num_experts
      __snake_case :   List[Any]													=    SwitchTransformersConfig(**_UpperCAmelCase				)
      return config





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      Optional[Any]=None ,_UpperCAmelCase    :      Dict="./" ,_UpperCAmelCase    :      Union[str, Any]=8				)    ->  Tuple:
      # Initialise PyTorch model

      print(f'''Loading flax weights from : {flax_checkpoint_path}'''				)
      __snake_case :   Union[str, Any]													=    checkpoints.load_tax_checkpoint(_UpperCAmelCase				)

      if gin_file is not None:
            __snake_case :   Any													=    convert_gin_to_config(_UpperCAmelCase ,_UpperCAmelCase				)
      else:
            __snake_case :   str													=    SwitchTransformersConfig.from_pretrained(_UpperCAmelCase				)

      __snake_case :   Dict													=    SwitchTransformersForConditionalGeneration(_UpperCAmelCase				)

      __snake_case :   int													=    flax_params['target']
      __snake_case :   Dict													=    flatten_dict(_UpperCAmelCase ,sep='/'				)
      __snake_case :   int													=    rename_keys(_UpperCAmelCase				)
      __snake_case :   Union[str, Any]													=    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__    :   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''')
    A__    :   Dict           =					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'''





from ..utils import DummyObject, requires_backends



class  snake_case__						(    metaclass=SCREAMING_SNAKE_CASE_				):
 A__				= ['''note_seq''']
 def __init__(   self						:							Tuple      ,     *__a						:							int      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(self      ,     ['note_seq']  )
 @classmethod
 def A_   (   cls						:							Optional[Any]      ,     *__a						:							Optional[Any]      ,     **__a						:							List[str]  )      ->   Union[str, Any]:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )





 @classmethod
 def A_   (   cls						:							List[Any]      ,     *__a						:							Union[str, Any]      ,     **__a						:							List[str]  )      ->   Any:







       '''simple docstring'''
       requires_backends(cls      ,     ['note_seq']  )



