

'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			List[str]:



       if divisor % 5 == 0 or divisor % 2 == 0:
              return 0
       A_						=      1
       A_						=      1
       while repunit:
              A_						=      (10 * repunit + 1) % divisor
              repunit_index += 1
       return repunit_index
def       UpperCAmelCase__    (      UpperCAmelCase__ = 1_00_00_00				)			->			int:
       A_						=      limit - 1
       if divisor % 2 == 0:
              divisor += 1
       while least_divisible_repunit(_SCREAMING_SNAKE_CASE				) <= limit:
              divisor += 2
       return divisor


if __name__ == "__main__":
      print(f"""{solution() = }""")



'''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
__lowerCamelCase		       =    {
    '''/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__    (      UpperCAmelCase__				)			->			Tuple:
       # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
       # the original model
       A_						=      list(s_dict.keys()				)
       for key in keys:
              A_						=      r""".*/layers_(\d+)"""
              A_						=      key
              if re.match(UpperCAmelCase__,       UpperCAmelCase__				):
                     A_						=      re.sub(r"""layers_(\d+)""",       r"""block/\1/layer""",       UpperCAmelCase__				)

              A_						=      r"""(encoder|decoder)\/"""

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

                     elif groups[0] == "decoder":
                            A_						=      re.sub(r"""/mlp/""",       r"""/2/mlp/""",       UpperCAmelCase__				)
                            A_						=      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:
                            A_						=      new_key.replace(UpperCAmelCase__,       UpperCAmelCase__				)

              print(F'''{key} -> {new_key}'''				)
              A_						=      s_dict.pop(UpperCAmelCase__				)

       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(UpperCAmelCase__				):
                            A_						=      expert_weihts[idx]
                            print(F'''{key} -> {key.replace("expert/",       "nested fstring"				)}'''				)

                     s_dict.pop(UpperCAmelCase__				)

       return s_dict


__lowerCamelCase		       =    {
    '''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__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(UpperCAmelCase__,       """r"""				) as f:
              A_						=      f.read()

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

       A_						=      re.findall(r"""(.*activations) = \(\'(.*)\',\)""",       UpperCAmelCase__				)[0]
       A_						=      str(activation[1]				)

       A_						=      num_experts
       A_						=      SwitchTransformersConfig(**UpperCAmelCase__				)
       return config
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None,       UpperCAmelCase__="./",       UpperCAmelCase__=8				)			->			List[str]:
       # Initialise PyTorch model

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

       if gin_file is not None:
              A_						=      convert_gin_to_config(UpperCAmelCase__,       UpperCAmelCase__				)
       else:
              A_						=      SwitchTransformersConfig.from_pretrained(UpperCAmelCase__				)

       A_						=      SwitchTransformersForConditionalGeneration(UpperCAmelCase__				)

       A_						=      flax_params["""target"""]
       A_						=      flatten_dict(UpperCAmelCase__,       sep="""/"""				)
       A_						=      rename_keys(UpperCAmelCase__				)
       A_						=      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__":
      __lowerCamelCase		       =    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''')
      __lowerCamelCase		       =    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,
      )
