




'''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
_A:					Tuple							=				{
    """/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     (      _lowerCAmelCase    )->     str:
 # 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


_A:					Dict							=				{
    """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     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 # 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 _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase=None       ,    _lowerCAmelCase="./"       ,    _lowerCAmelCase=8    )->     Optional[Any]:
 # 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__":
    _A:					Tuple							=				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:					Union[str, Any]							=				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 string import ascii_uppercase

_A:					Union[str, Any]							=				{char: i for i, char in enumerate(ascii_uppercase)}
_A:					str							=				dict(enumerate(ascii_uppercase))







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   len(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 while True:
  if x == i:
   __UpperCAmelCase					=   0
  if len(_lowerCAmelCase    ) == len(_lowerCAmelCase    ):
   break
  key += key[i]
  i += 1
 return key







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in message:
  if letter == " ":
   cipher_text += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] - dicta[key_new[i]]) % 26
   i += 1
   cipher_text += dicta[x]
 return cipher_text







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in cipher_text:
  if letter == " ":
   or_txt += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] + dicta[key_new[i]] + 26) % 26
   i += 1
   or_txt += dicta[x]
 return or_txt







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   'THE GERMAN ATTACK'
 __UpperCAmelCase					=   'SECRET'
 __UpperCAmelCase					=   generate_key(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   cipher_text(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print(F'Encrypted Text = {s}'    )
 print(F'Original Text = {original_text(_lowerCAmelCase       ,    _lowerCAmelCase    )}'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





