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

  UpperCamelCase_					:						int =					R'(encoder|decoder)\/'

  if re.match(lowerCamelCase  ,  lowerCamelCase	):
   UpperCamelCase_					:						List[str] =					re.match(lowerCamelCase  ,  lowerCamelCase	).groups()
   if groups[0] == "encoder":
    UpperCamelCase_					:						int =					re.sub(R'/mlp/'  ,  R'/1/mlp/'  ,  lowerCamelCase	)
    UpperCamelCase_					:						Optional[int] =					re.sub(R'/pre_mlp_layer_norm/'  ,  R'/1/layer_norm/'  ,  lowerCamelCase	)

   elif groups[0] == "decoder":
    UpperCamelCase_					:						List[str] =					re.sub(R'/mlp/'  ,  R'/2/mlp/'  ,  lowerCamelCase	)
    UpperCamelCase_					:						List[Any] =					re.sub(R'/pre_mlp_layer_norm/'  ,  R'/2/layer_norm/'  ,  lowerCamelCase	)

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    UpperCamelCase_					:						List[Any] =					new_key.replace(lowerCamelCase  ,  lowerCamelCase	)

  print(F"{key} -> {new_key}"	)
  UpperCamelCase_					:						List[Any] =					s_dict.pop(lowerCamelCase	)

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

   s_dict.pop(lowerCamelCase	)

 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    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Tuple	):
 # Convert a google style config to the hugging face fromat
 import regex as re

 with open(lowerCamelCase  ,  'r'	) as f:
  UpperCamelCase_					:						int =					f.read()

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

 UpperCamelCase_					:						int =					re.findall(R'(.*activations) = \(\'(.*)\',\)'  ,  lowerCamelCase	)[0]
 UpperCamelCase_					:						int =					str(activation[1]	)

 UpperCamelCase_					:						Union[str, Any] =					num_experts
 UpperCamelCase_					:						str =					SwitchTransformersConfig(**lowerCamelCase	)
 return config





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[int]=None  ,  lowerCamelCase					:     Dict="./"  ,  lowerCamelCase					:     Optional[int]=8	):
 # Initialise PyTorch model

 print(F"Loading flax weights from : {flax_checkpoint_path}"	)
 UpperCamelCase_					:						Any =					checkpoints.load_tax_checkpoint(lowerCamelCase	)

 if gin_file is not None:
  UpperCamelCase_					:						Tuple =					convert_gin_to_config(lowerCamelCase  ,  lowerCamelCase	)
 else:
  UpperCamelCase_					:						Dict =					SwitchTransformersConfig.from_pretrained(lowerCamelCase	)

 UpperCamelCase_					:						Union[str, Any] =					SwitchTransformersForConditionalGeneration(lowerCamelCase	)

 UpperCamelCase_					:						Tuple =					flax_params['target']
 UpperCamelCase_					:						Union[str, Any] =					flatten_dict(lowerCamelCase  ,  sep='/'	)
 UpperCamelCase_					:						Dict =					rename_keys(lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					unflatten_dict(lowerCamelCase  ,  sep='/'	)

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

 print(F"Save PyTorch model to {pytorch_dump_path}"	)
 pt_model.save_pretrained(lowerCamelCase	)


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,
   )




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



