



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      a__			(   lowerCAmelCase__   ):
      # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
      # the original model
      UpperCAmelCase_    				=							list(s_dict.keys()   )
      for key in keys:
            UpperCAmelCase_    				=							r".*/layers_(\d+)"
            UpperCAmelCase_    				=							key
            if re.match(lowerCAmelCase__			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							re.sub(r"layers_(\d+)"			,    r"block/\1/layer"			,    lowerCAmelCase__   )

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

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

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

            print(f"""{key} -> {new_key}"""   )
            UpperCAmelCase_    				=							s_dict.pop(lowerCAmelCase__   )

      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(lowerCAmelCase__   ):
                        UpperCAmelCase_    				=							expert_weihts[idx]
                        print(f"""{key} -> {key.replace('expert/'			,    'nested fstring'   )}"""   )

                  s_dict.pop(lowerCAmelCase__   )

      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      a__			(   lowerCAmelCase__			,    lowerCAmelCase__   ):
      # Convert a google style config to the hugging face fromat
      import regex as re

      with open(lowerCAmelCase__			,    "r"   ) as f:
            UpperCAmelCase_    				=							f.read()

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

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

      UpperCAmelCase_    				=							num_experts
      UpperCAmelCase_    				=							SwitchTransformersConfig(**lowerCAmelCase__   )
      return config
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__=None			,    lowerCAmelCase__="./"			,    lowerCAmelCase__=8   ):
      # Initialise PyTorch model

      print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
      UpperCAmelCase_    				=							checkpoints.load_tax_checkpoint(lowerCAmelCase__   )

      if gin_file is not None:
            UpperCAmelCase_    				=							convert_gin_to_config(lowerCAmelCase__			,    lowerCAmelCase__   )
      else:
            UpperCAmelCase_    				=							SwitchTransformersConfig.from_pretrained(lowerCAmelCase__   )

      UpperCAmelCase_    				=							SwitchTransformersForConditionalGeneration(lowerCAmelCase__   )

      UpperCAmelCase_    				=							flax_params["target"]
      UpperCAmelCase_    				=							flatten_dict(lowerCAmelCase__			,    sep="/"   )
      UpperCAmelCase_    				=							rename_keys(lowerCAmelCase__   )
      UpperCAmelCase_    				=							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__":
  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,
  )









"""simple docstring"""





import string
def      a__			(   lowerCAmelCase__   ):
      for key in range(len(string.ascii_uppercase   )   ):
            UpperCAmelCase_    				=							""
            for symbol in message:
                  if symbol in string.ascii_uppercase:
                        UpperCAmelCase_    				=							string.ascii_uppercase.find(lowerCAmelCase__   )
                        UpperCAmelCase_    				=							num - key
                        if num < 0:
                              UpperCAmelCase_    				=							num + len(string.ascii_uppercase   )
                        UpperCAmelCase_    				=							translated + string.ascii_uppercase[num]
                  else:
                        UpperCAmelCase_    				=							translated + symbol
            print(f"""Decryption using Key #{key}: {translated}"""   )
def      a__			(   ):
      UpperCAmelCase_    				=							input("Encrypted message: "   )
      UpperCAmelCase_    				=							message.upper()
      decrypt(lowerCAmelCase__   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




