

"""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_					:	Union[str, 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  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]			):
     # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
     # the original model
     A__										=  list(s_dict.keys()			)
     for key in keys:
          A__										=  R""".*/layers_(\d+)"""
          A__										=  key
          if re.match(UpperCAmelCase_					,   UpperCAmelCase_			):
               A__										=  re.sub(R"""layers_(\d+)"""					,   R"""block/\1/layer"""					,   UpperCAmelCase_			)

          A__										=  R"""(encoder|decoder)\/"""

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

               elif groups[0] == "decoder":
                    A__										=  re.sub(R"""/mlp/"""					,   R"""/2/mlp/"""					,   UpperCAmelCase_			)
                    A__										=  re.sub(R"""/pre_mlp_layer_norm/"""					,   R"""/2/layer_norm/"""					,   UpperCAmelCase_			)

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

          print(F"""{key} -> {new_key}"""			)
          A__										=  s_dict.pop(UpperCAmelCase_			)

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

               s_dict.pop(UpperCAmelCase_			)

     return s_dict


SCREAMING_SNAKE_CASE_					:	Tuple							  =						{
    '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  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					Union[str, Any]			):
     # Convert a google style config to the hugging face fromat
     import regex as re

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

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

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

     A__										=  num_experts
     A__										=  SwitchTransformersConfig(**UpperCAmelCase_			)
     return config





def  _snake_case     (    UpperCAmelCase_  :					Union[str, Any]					,   UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					str=None					,   UpperCAmelCase_  :					int="./"					,   UpperCAmelCase_  :					Optional[int]=8			):
     # Initialise PyTorch model

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

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

     A__										=  SwitchTransformersForConditionalGeneration(UpperCAmelCase_			)

     A__										=  flax_params["""target"""]
     A__										=  flatten_dict(UpperCAmelCase_					,   sep="""/"""			)
     A__										=  rename_keys(UpperCAmelCase_			)
     A__										=  unflatten_dict(UpperCAmelCase_					,   sep="""/"""			)

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

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


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_					:	Optional[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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_					:	Any							  =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						[
     'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
     'JukeboxModel',
     'JukeboxPreTrainedModel',
     'JukeboxVQVAE',
     'JukeboxPrior',
 ]

if TYPE_CHECKING:
 from .configuration_jukebox import (
     JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
     JukeboxConfig,
     JukeboxPriorConfig,
     JukeboxVQVAEConfig,
 )
 from .tokenization_jukebox import JukeboxTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_jukebox import (
      JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      JukeboxModel,
      JukeboxPreTrainedModel,
      JukeboxPrior,
      JukeboxVQVAE,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_					:	Optional[int]							  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






