

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__		 =     {
    '''/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    :Optional[int]		):


       """simple docstring"""




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

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

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

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

              print(F'{key} -> {new_key}'		)
              __snake_case       	=							s_dict.pop(__A		)

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

                     s_dict.pop(__A		)

       return s_dict


UpperCamelCase__		 =     {
    '''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    :Tuple	,__A    :Any		):


       """simple docstring"""




       import regex as re

       with open(__A	,"""r"""		) as f:
              __snake_case       	=							f.read()

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

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

       __snake_case       	=							num_experts
       __snake_case       	=							SwitchTransformersConfig(**__A		)
       return config







def 						lowerCamelCase__      ( __A    :int	,__A    :Optional[int]	,__A    :Optional[Any]=None	,__A    :List[str]="./"	,__A    :Optional[int]=8		):


       """simple docstring"""




       print(F'Loading flax weights from : {flax_checkpoint_path}'		)
       __snake_case       	=							checkpoints.load_tax_checkpoint(__A		)

       if gin_file is not None:
              __snake_case       	=							convert_gin_to_config(__A	,__A		)
       else:
              __snake_case       	=							SwitchTransformersConfig.from_pretrained(__A		)

       __snake_case       	=							SwitchTransformersForConditionalGeneration(__A		)

       __snake_case       	=							flax_params["""target"""]
       __snake_case       	=							flatten_dict(__A	,sep="""/"""		)
       __snake_case       	=							rename_keys(__A		)
       __snake_case       	=							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__		 =     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__		 =     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,
    )




def 						lowerCamelCase__      ( __A    :int	,__A    :int		):


       """simple docstring"""




       if a < 0 or b < 0:
              raise ValueError("""the value of both inputs must be positive"""		)

       __snake_case       	=							str(bin(__A		)		)[2:]  # remove the leading "0b"
       __snake_case       	=							str(bin(__A		)		)[2:]  # remove the leading "0b"

       __snake_case       	=							max(len(__A		)	,len(__A		)		)

       return "0b" + "".join(
           str(int(char_a == """1""" and char_b == """1"""		)		)
           for char_a, char_b in zip(a_binary.zfill(__A		)	,b_binary.zfill(__A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

