






"""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
UpperCAmelCase_   : str   			= {
    """/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 					_A			(__a							) ->       Tuple:
    """simple docstring"""






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

        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		R'''(encoder|decoder)\/'''

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

            elif groups[0] == "decoder":
                SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		re.sub(R'''/mlp/'''    ,				R'''/2/mlp/'''    ,				__a							)
                SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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:
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		new_key.replace(__a    ,				__a							)

        print(f'{key} -> {new_key}'							)
        SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		s_dict.pop(__a							)

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

            s_dict.pop(__a							)

    return s_dict


UpperCAmelCase_   : 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 					_A			(__a    ,				__a							) ->       List[Any]:
    """simple docstring"""






    import regex as re

    with open(__a    ,				'''r'''							) as f:
        SCREAMING_SNAKE_CASE_      :  List[Any]    						=		f.read()

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

    SCREAMING_SNAKE_CASE_      :  Dict    						=		re.findall(R'''(.*activations) = \(\'(.*)\',\)'''    ,				__a							)[0]
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		str(activation[1]							)

    SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_experts
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		SwitchTransformersConfig(**__a							)
    return config



def 					_A			(__a    ,				__a    ,				__a=None    ,				__a="./"    ,				__a=8							) ->       Union[str, Any]:
    """simple docstring"""






    print(f'Loading flax weights from : {flax_checkpoint_path}'							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		checkpoints.load_tax_checkpoint(__a							)

    if gin_file is not None:
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		convert_gin_to_config(__a    ,				__a							)
    else:
        SCREAMING_SNAKE_CASE_      :  int    						=		SwitchTransformersConfig.from_pretrained(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		SwitchTransformersForConditionalGeneration(__a							)

    SCREAMING_SNAKE_CASE_      :  Dict    						=		flax_params['''target''']
    SCREAMING_SNAKE_CASE_      :  Any    						=		flatten_dict(__a    ,				sep='''/'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		rename_keys(__a							)
    SCREAMING_SNAKE_CASE_      :  int    						=		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__":
  UpperCAmelCase_   : List[Any]   			= 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""")
  UpperCAmelCase_   : 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 ..utils import DummyObject, requires_backends





class      lowerCamelCase__   (     metaclass=A				):







					"""simple docstring"""


					__a	       =							["""note_seq"""]






					def __init__(       self    : List[Any] ,       *UpperCamelCase    : List[Any] ,       **UpperCamelCase    : Tuple  ):
												'''simple docstring'''






												requires_backends(self ,       ["""note_seq"""]  )






					@classmethod
					def lowerCamelCase__							(       cls    : Union[str, Any] ,       *UpperCamelCase    : Union[str, Any] ,       **UpperCamelCase    : str  ):
												'''simple docstring'''






												requires_backends(cls ,       ["""note_seq"""]  )






					@classmethod
					def lowerCamelCase__							(       cls    : List[str] ,       *UpperCamelCase    : Optional[Any] ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												requires_backends(cls ,       ["""note_seq"""]  )
