





"""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     =    {
    "/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_			(	__a		)					->		List[str]:







  """simple docstring"""





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

    lowerCamelCase__:				Any        =R"(encoder|decoder)\/"

    if re.match(__a     ,   __a		):
      lowerCamelCase__:				Union[str, Any]        =re.match(__a     ,   __a		).groups()
      if groups[0] == "encoder":
        lowerCamelCase__:				Union[str, Any]        =re.sub(R"/mlp/"     ,   R"/1/mlp/"     ,   __a		)
        lowerCamelCase__:				Optional[int]        =re.sub(R"/pre_mlp_layer_norm/"     ,   R"/1/layer_norm/"     ,   __a		)

      elif groups[0] == "decoder":
        lowerCamelCase__:				Tuple        =re.sub(R"/mlp/"     ,   R"/2/mlp/"     ,   __a		)
        lowerCamelCase__:				Optional[int]        =re.sub(R"/pre_mlp_layer_norm/"     ,   R"/2/layer_norm/"     ,   __a		)

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

    print(F"""{key} -> {new_key}"""		)
    lowerCamelCase__:				Any        =s_dict.pop(__a		)

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

      s_dict.pop(__a		)

  return s_dict


__A     =    {
    "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_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  import regex as re

  with open(__a     ,   "r"		) as f:
    lowerCamelCase__:				Optional[int]        =f.read()

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

  lowerCamelCase__:				str        =re.findall(R"(.*activations) = \(\'(.*)\',\)"     ,   __a		)[0]
  lowerCamelCase__:				Tuple        =str(activation[1]		)

  lowerCamelCase__:				List[str]        =num_experts
  lowerCamelCase__:				List[str]        =SwitchTransformersConfig(**__a		)
  return config





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None     ,   __a="./"     ,   __a=8		)					->		List[Any]:







  """simple docstring"""





  print(F"""Loading flax weights from : {flax_checkpoint_path}"""		)
  lowerCamelCase__:				str        =checkpoints.load_tax_checkpoint(__a		)

  if gin_file is not None:
    lowerCamelCase__:				int        =convert_gin_to_config(__a     ,   __a		)
  else:
    lowerCamelCase__:				Dict        =SwitchTransformersConfig.from_pretrained(__a		)

  lowerCamelCase__:				str        =SwitchTransformersForConditionalGeneration(__a		)

  lowerCamelCase__:				str        =flax_params["target"]
  lowerCamelCase__:				Tuple        =flatten_dict(__a     ,   sep="/"		)
  lowerCamelCase__:				Tuple        =rename_keys(__a		)
  lowerCamelCase__:				Union[str, Any]        =unflatten_dict(__a     ,   sep="/"		)

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

  print(F"""Save PyTorch model to {pytorch_dump_path}"""		)
  pt_model.save_pretrained(__a		)


if __name__ == "__main__":
     __A     =    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     =    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,
     )








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A     =    logging.get_logger(__name__)

__A     =    {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "distilbert"
 lowercase_		=      {
     "hidden_size": "dim",
     "num_attention_heads": "n_heads",
     "num_hidden_layers": "n_layers",
 }

 def __init__(self    :       Any	,			UpperCAmelCase_    :       str=30_522	,			UpperCAmelCase_    :       Union[str, Any]=512	,			UpperCAmelCase_    :       int=False	,			UpperCAmelCase_    :       Optional[Any]=6	,			UpperCAmelCase_    :       Optional[Any]=12	,			UpperCAmelCase_    :       Any=768	,			UpperCAmelCase_    :       List[Any]=4 * 768	,			UpperCAmelCase_    :       Tuple=0.1	,			UpperCAmelCase_    :       List[Any]=0.1	,			UpperCAmelCase_    :       Any="gelu"	,			UpperCAmelCase_    :       int=0.02	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       Optional[int]=0.2	,			UpperCAmelCase_    :       int=0	,			**UpperCAmelCase_    :       List[Any]	,			)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				int        =vocab_size
   lowerCamelCase__:				Any        =max_position_embeddings
   lowerCamelCase__:				Optional[int]        =sinusoidal_pos_embds
   lowerCamelCase__:				str        =n_layers
   lowerCamelCase__:				str        =n_heads
   lowerCamelCase__:				str        =dim
   lowerCamelCase__:				Optional[Any]        =hidden_dim
   lowerCamelCase__:				Dict        =dropout
   lowerCamelCase__:				Optional[Any]        =attention_dropout
   lowerCamelCase__:				int        =activation
   lowerCamelCase__:				Dict        =initializer_range
   lowerCamelCase__:				Optional[Any]        =qa_dropout
   lowerCamelCase__:				int        =seq_classif_dropout
   super().__init__(**UpperCAmelCase_	,			pad_token_id=UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__:				Dict        ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__:				Optional[int]        ={0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ])

