


"""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
__snake_case       =       {
    """/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     __lowerCAmelCase		(		lowercase    :    Dict					)      ->		Any:


    """simple docstring"""





    snake_case       : str    =       list(s_dict.keys()					)
    for key in keys:
        snake_case       : Union[str, Any]    =       R".*/layers_(\d+)"
        snake_case       : Optional[int]    =       key
        if re.match(lowercase		,				lowercase					):
            snake_case       : Tuple    =       re.sub(R"layers_(\d+)"		,				R"block/\1/layer"		,				lowercase					)

        snake_case       : Dict    =       R"(encoder|decoder)\/"

        if re.match(lowercase		,				lowercase					):
            snake_case       : Dict    =       re.match(lowercase		,				lowercase					).groups()
            if groups[0] == "encoder":
                snake_case       : Optional[Any]    =       re.sub(R"/mlp/"		,				R"/1/mlp/"		,				lowercase					)
                snake_case       : Optional[int]    =       re.sub(R"/pre_mlp_layer_norm/"		,				R"/1/layer_norm/"		,				lowercase					)

            elif groups[0] == "decoder":
                snake_case       : Any    =       re.sub(R"/mlp/"		,				R"/2/mlp/"		,				lowercase					)
                snake_case       : str    =       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:
                snake_case       : str    =       new_key.replace(lowercase		,				lowercase					)

        print(F'{key} -> {new_key}'					)
        snake_case       : int    =       s_dict.pop(lowercase					)

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

            s_dict.pop(lowercase					)

    return s_dict


__snake_case       =       {
    """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     __lowerCAmelCase		(		lowercase    :    List[str]		,				lowercase    :    Any					)      ->		int:


    """simple docstring"""





    import regex as re

    with open(lowercase		,				"r"					) as f:
        snake_case       : Dict    =       f.read()

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

    snake_case       : Any    =       re.findall(R"(.*activations) = \(\'(.*)\',\)"		,				lowercase					)[0]
    snake_case       : Any    =       str(activation[1]					)

    snake_case       : Optional[int]    =       num_experts
    snake_case       : Any    =       SwitchTransformersConfig(**lowercase					)
    return config







def     __lowerCAmelCase		(		lowercase    :    Optional[Any]		,				lowercase    :    Optional[int]		,				lowercase    :    List[str]=None		,				lowercase    :    Tuple="./"		,				lowercase    :    Optional[int]=8					)      ->		Optional[Any]:


    """simple docstring"""





    print(F'Loading flax weights from : {flax_checkpoint_path}'					)
    snake_case       : Optional[Any]    =       checkpoints.load_tax_checkpoint(lowercase					)

    if gin_file is not None:
        snake_case       : Union[str, Any]    =       convert_gin_to_config(lowercase		,				lowercase					)
    else:
        snake_case       : Dict    =       SwitchTransformersConfig.from_pretrained(lowercase					)

    snake_case       : Optional[int]    =       SwitchTransformersForConditionalGeneration(lowercase					)

    snake_case       : Optional[Any]    =       flax_params["target"]
    snake_case       : int    =       flatten_dict(lowercase		,				sep="/"					)
    snake_case       : Optional[int]    =       rename_keys(lowercase					)
    snake_case       : str    =       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__":
  __snake_case       =       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""")
  __snake_case       =       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     __lowerCAmelCase		(		lowercase    :    str					)      ->		None:


    """simple docstring"""





    for key in range(len(string.ascii_uppercase					)					):
        snake_case       : Dict    =       ""
        for symbol in message:
            if symbol in string.ascii_uppercase:
                snake_case       : Dict    =       string.ascii_uppercase.find(lowercase					)
                snake_case       : Optional[int]    =       num - key
                if num < 0:
                    snake_case       : Optional[int]    =       num + len(string.ascii_uppercase					)
                snake_case       : Dict    =       translated + string.ascii_uppercase[num]
            else:
                snake_case       : Any    =       translated + symbol
        print(F'Decryption using Key #{key}: {translated}'					)







def     __lowerCAmelCase		(		)      ->		None:


    """simple docstring"""





    snake_case       : Dict    =       input("Encrypted message: "					)
    snake_case       : Any    =       message.upper()
    decrypt(lowercase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()





