





'''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					:     List[str]												={
    '''/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_  (						__lowerCamelCase					:					Any ):
   lowercase_			:Dict    			= list(s_dict.keys() )
   for key in keys:
      lowercase_			:List[Any]    			= r".*/layers_(\d+)"
      lowercase_			:Optional[Any]    			= key
      if re.match(__snake_case	,__snake_case ):
         lowercase_			:Union[str, Any]    			= re.sub(r"layers_(\d+)"	,r"block/\1/layer"	,__snake_case )

      lowercase_			:Tuple    			= r"(encoder|decoder)\/"

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

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

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            lowercase_			:Tuple    			= new_key.replace(__snake_case	,__snake_case )

      print(F'{key} -> {new_key}' )
      lowercase_			:Dict    			= s_dict.pop(__snake_case )

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

         s_dict.pop(__snake_case )

   return s_dict


lowerCAmelCase					:     Any												={
    '''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_  (						__lowerCamelCase					:					Dict	,__lowerCamelCase					:					Tuple ):
   import regex as re

   with open(__snake_case	,"r" ) as f:
      lowercase_			:int    			= f.read()

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

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

   lowercase_			:Tuple    			= num_experts
   lowercase_			:List[str]    			= SwitchTransformersConfig(**__snake_case )
   return config
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					str=None	,__lowerCamelCase					:					Optional[Any]="./"	,__lowerCamelCase					:					Dict=8 ):
   print(F'Loading flax weights from : {flax_checkpoint_path}' )
   lowercase_			:Tuple    			= checkpoints.load_tax_checkpoint(__snake_case )

   if gin_file is not None:
      lowercase_			:int    			= convert_gin_to_config(__snake_case	,__snake_case )
   else:
      lowercase_			:Optional[int]    			= SwitchTransformersConfig.from_pretrained(__snake_case )

   lowercase_			:Tuple    			= SwitchTransformersForConditionalGeneration(__snake_case )

   lowercase_			:Optional[int]    			= flax_params["target"]
   lowercase_			:int    			= flatten_dict(__snake_case	,sep="/" )
   lowercase_			:Optional[Any]    			= rename_keys(__snake_case )
   lowercase_			:Tuple    			= unflatten_dict(__snake_case	,sep="/" )

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

   print(F'Save PyTorch model to {pytorch_dump_path}' )
   pt_model.save_pretrained(__snake_case )


if __name__ == "__main__":
 lowerCAmelCase					:     Optional[Any]												=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					:     int												=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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int | float:


					"""simple docstring"""





					if len(__snake_case	) == 0:
										raise ValueError('''find_max() arg is an empty sequence'''	)
					if (
					    left >= len(__snake_case	)
					    or left < -len(__snake_case	)
					    or right >= len(__snake_case	)
					    or right < -len(__snake_case	)
					):
										raise IndexError('''list index out of range'''	)
					if left == right:
										return nums[left]
					_UpperCamelCase							 =  (left + right) >> 1  # the middle
					_UpperCamelCase							 =  find_max(__snake_case,     __snake_case,     __snake_case	)  # find max in range[left, mid]
					_UpperCamelCase							 =  find_max(__snake_case,     mid + 1,     __snake_case	)  # find max in range[mid + 1, right]

					return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






