'''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			(  a_:       List[str]							):
   _UpperCAmelCase							:	Optional[Any]							=   list(s_dict.keys()							)
   for key in keys:
      _UpperCAmelCase							:	Dict							=   r""".*/layers_(\d+)"""
      _UpperCAmelCase							:	Dict							=   key
      if re.match(_lowercase,					_lowercase							):
         _UpperCAmelCase							:	Dict							=   re.sub(r"layers_(\d+)",					r"block/\1/layer",					_lowercase							)

      _UpperCAmelCase							:	List[Any]							=   r"""(encoder|decoder)\/"""

      if re.match(_lowercase,					_lowercase							):
         _UpperCAmelCase							:	Any							=   re.match(_lowercase,					_lowercase							).groups()
         if groups[0] == "encoder":
            _UpperCAmelCase							:	str							=   re.sub(r"/mlp/",					r"/1/mlp/",					_lowercase							)
            _UpperCAmelCase							:	Optional[Any]							=   re.sub(r"/pre_mlp_layer_norm/",					r"/1/layer_norm/",					_lowercase							)

         elif groups[0] == "decoder":
            _UpperCAmelCase							:	int							=   re.sub(r"/mlp/",					r"/2/mlp/",					_lowercase							)
            _UpperCAmelCase							:	Optional[Any]							=   re.sub(r"/pre_mlp_layer_norm/",					r"/2/layer_norm/",					_lowercase							)

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            _UpperCAmelCase							:	Union[str, Any]							=   new_key.replace(_lowercase,					_lowercase							)

      print(f"""{key} -> {new_key}"""							)
      _UpperCAmelCase							:	List[Any]							=   s_dict.pop(_lowercase							)

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

         s_dict.pop(_lowercase							)

   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			(  a_:       Optional[Any],					a_:       Union[str, Any]							):
   import regex as re

   with open(_lowercase,					"r"							) as f:
      _UpperCAmelCase							:	Tuple							=   f.read()

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

   _UpperCAmelCase							:	Dict							=   re.findall(r"(.*activations) = \(\'(.*)\',\)",					_lowercase							)[0]
   _UpperCAmelCase							:	Optional[int]							=   str(activation[1]							)

   _UpperCAmelCase							:	Dict							=   num_experts
   _UpperCAmelCase							:	int							=   SwitchTransformersConfig(**_lowercase							)
   return config






def    __UpperCAmelCase			(  a_:       Union[str, Any],					a_:       Union[str, Any],					a_:       List[str]=None,					a_:       Optional[Any]="./",					a_:       Tuple=8							):
   print(f"""Loading flax weights from : {flax_checkpoint_path}"""							)
   _UpperCAmelCase							:	List[Any]							=   checkpoints.load_tax_checkpoint(_lowercase							)

   if gin_file is not None:
      _UpperCAmelCase							:	Dict							=   convert_gin_to_config(_lowercase,					_lowercase							)
   else:
      _UpperCAmelCase							:	Optional[Any]							=   SwitchTransformersConfig.from_pretrained(_lowercase							)

   _UpperCAmelCase							:	Union[str, Any]							=   SwitchTransformersForConditionalGeneration(_lowercase							)

   _UpperCAmelCase							:	Tuple							=   flax_params["""target"""]
   _UpperCAmelCase							:	Any							=   flatten_dict(_lowercase,					sep="/"							)
   _UpperCAmelCase							:	int							=   rename_keys(_lowercase							)
   _UpperCAmelCase							:	List[Any]							=   unflatten_dict(_lowercase,					sep="/"							)

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

   print(f"""Save PyTorch model to {pytorch_dump_path}"""							)
   pt_model.save_pretrained(_lowercase							)


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"""
from __future__ import annotations

__a      =				10
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			1
						snake_case_ :List[str]						=			max(_lowercase	)
						while placement <= max_digit:
												# declare and initialize empty buckets
												snake_case_ :list[list]						=			[[] for _ in range(_lowercase	)]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		snake_case_ :Any						=			int((i / placement) % RADIX	)
																		buckets[tmp].append(_lowercase	)
												# put each buckets' contents into list_of_ints
												snake_case_ :Optional[Any]						=			0
												for b in range(_lowercase	):
																		for i in buckets[b]:
																								snake_case_ :Union[str, Any]						=			i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()
