





'''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
__SCREAMING_SNAKE_CASE     :Any       =				{
    '''/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_							(					__lowercase							:      Dict       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   list(s_dict.keys()       )
     for key in keys:
          _UpperCAmelCase     							=   r".*/layers_(\d+)"
          _UpperCAmelCase     							=   key
          if re.match(__lowercase			,     __lowercase       ):
               _UpperCAmelCase     							=   re.sub(r"layers_(\d+)"			,     r"block/\1/layer"			,     __lowercase       )

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

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

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

          print(f'{key} -> {new_key}'       )
          _UpperCAmelCase     							=   s_dict.pop(__lowercase       )

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

               s_dict.pop(__lowercase       )

     return s_dict


__SCREAMING_SNAKE_CASE     :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_							(					__lowercase							:      Tuple			,     __lowercase							:      List[str]       )		->       Dict:


     '''simple docstring'''



     import regex as re

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

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

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

     _UpperCAmelCase     							=   num_experts
     _UpperCAmelCase     							=   SwitchTransformersConfig(**__lowercase       )
     return config
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      List[str]			,     __lowercase							:      str=None			,     __lowercase							:      List[str]="./"			,     __lowercase							:      Optional[int]=8       )		->       Optional[int]:


     '''simple docstring'''



     print(f'Loading flax weights from : {flax_checkpoint_path}'       )
     _UpperCAmelCase     							=   checkpoints.load_tax_checkpoint(__lowercase       )

     if gin_file is not None:
          _UpperCAmelCase     							=   convert_gin_to_config(__lowercase			,     __lowercase       )
     else:
          _UpperCAmelCase     							=   SwitchTransformersConfig.from_pretrained(__lowercase       )

     _UpperCAmelCase     							=   SwitchTransformersForConditionalGeneration(__lowercase       )

     _UpperCAmelCase     							=   flax_params["target"]
     _UpperCAmelCase     							=   flatten_dict(__lowercase			,     sep="/"       )
     _UpperCAmelCase     							=   rename_keys(__lowercase       )
     _UpperCAmelCase     							=   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__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				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''')
  __SCREAMING_SNAKE_CASE     :Tuple       =				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




class 							lowerCAmelCase__				:






      '''simple docstring'''

      def __init__(						self     :  Any						,	lowercase_     :  int = 0):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		key
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(lowercase_) ^ key) for ch in content]
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  int    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		''''''

          for ch in content:
              ans += chr(ord(lowercase_) ^ key)

          return ans
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int = 0):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''encrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  str						,	lowercase_     :  int):




          '''simple docstring'''


          assert isinstance(lowercase_						,	lowercase_) and isinstance(lowercase_						,	lowercase_)

          try:
              with open(lowercase_) as fin, open('''decrypt.out'''						,	'''w+''') as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(lowercase_						,	lowercase_))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")


