

"""simple docstring"""
def 					a__						(						lowerCAmelCase = 1			,	lowerCAmelCase = 10_00    )	-> Dict:
      UpperCAmelCase__			:							Optional[int]							=    1
      UpperCAmelCase__			:							Any							=    0

      for divide_by_number in range(__A			,	digit + 1    ):
            UpperCAmelCase__			:							List[str]							=    []
            UpperCAmelCase__			:							str							=    numerator
            for _ in range(1			,	digit + 1    ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(__A    ):
                              UpperCAmelCase__			:							Tuple							=    len(__A    )
                              UpperCAmelCase__			:							int							=    divide_by_number
                  else:
                        has_been_divided.append(__A    )
                        UpperCAmelCase__			:							str							=    now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()






'''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,
  )



