





'''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     :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 	UpperCAmelCase_							(					__lowercase							:      Dict       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   list(s_dict.keys()       )
     for key in keys:
          _UpperCAmelCase     							=   r".*/layers_(\d+)"
          _UpperCAmelCase     							=   key
          if re.match(__lowercase			,     __lowercase       ):
               _UpperCAmelCase     							=   re.sub(r"layers_(\d+)"			,     r"block/\1/layer"			,     __lowercase       )

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

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

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

          print(f'{key} -> {new_key}'       )
          _UpperCAmelCase     							=   s_dict.pop(__lowercase       )

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

               s_dict.pop(__lowercase       )

     return s_dict


__SCREAMING_SNAKE_CASE     :Any       =				{
    '''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 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      List[str]       )		->       Dict:


     '''simple docstring'''



     import regex as re

     with open(__lowercase			,     "r"       ) as f:
          _UpperCAmelCase     							=   f.read()

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

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

     _UpperCAmelCase     							=   num_experts
     _UpperCAmelCase     							=   SwitchTransformersConfig(**__lowercase       )
     return config
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      List[str]			,     __lowercase							:      str=None			,     __lowercase							:      List[str]="./"			,     __lowercase							:      Optional[int]=8       )		->       Optional[int]:


     '''simple docstring'''



     print(f'Loading flax weights from : {flax_checkpoint_path}'       )
     _UpperCAmelCase     							=   checkpoints.load_tax_checkpoint(__lowercase       )

     if gin_file is not None:
          _UpperCAmelCase     							=   convert_gin_to_config(__lowercase			,     __lowercase       )
     else:
          _UpperCAmelCase     							=   SwitchTransformersConfig.from_pretrained(__lowercase       )

     _UpperCAmelCase     							=   SwitchTransformersForConditionalGeneration(__lowercase       )

     _UpperCAmelCase     							=   flax_params["target"]
     _UpperCAmelCase     							=   flatten_dict(__lowercase			,     sep="/"       )
     _UpperCAmelCase     							=   rename_keys(__lowercase       )
     _UpperCAmelCase     							=   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__":
  __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     :Tuple       =				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 .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      if not self.is_available():
           raise RuntimeError(
               f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'    )




 @classmethod
 def      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} hyperparameter search backends available. Using {name} as the default.'       )
          return name
     raise RuntimeError(
         "No hyperparameter search backend available.\n"
         + "\n".join(
             f' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )




