



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_: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 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Tuple:



    """simple docstring"""






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

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

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

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

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

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

            s_dict.pop(__a		)

    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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  List[Any]:



    """simple docstring"""






    import regex as re

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

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

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

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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase="./"      ,     _lowerCAmelCase=8		)       ->  Union[str, Any]:



    """simple docstring"""






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

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

    A :       Dict          =   SwitchTransformersForConditionalGeneration(__a		)

    A :       Dict          =   flax_params['''target''']
    A :       Any          =   flatten_dict(__a      ,     sep="""/"""		)
    A :       List[Any]          =   rename_keys(__a		)
    A :       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__":
     SCREAMING_SNAKE_CASE_: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""")
     SCREAMING_SNAKE_CASE_: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"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


