



'''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
_lowerCAmelCase      						=	{
    '''/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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					list(s_dict.keys()			)
    for key in keys:
        lowerCAmelCase__ :  Union[str, Any]							      =					R""".*/layers_(\d+)"""
        lowerCAmelCase__ :  str							      =					key
        if re.match(UpperCamelCase							,    UpperCamelCase			):
            lowerCAmelCase__ :  str							      =					re.sub(R"""layers_(\d+)"""							,    R"""block/\1/layer"""							,    UpperCamelCase			)

        lowerCAmelCase__ :  Optional[int]							      =					R"""(encoder|decoder)\/"""

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

            elif groups[0] == "decoder":
                lowerCAmelCase__ :  Dict							      =					re.sub(R"""/mlp/"""							,    R"""/2/mlp/"""							,    UpperCamelCase			)
                lowerCAmelCase__ :  List[Any]							      =					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:
                lowerCAmelCase__ :  List[str]							      =					new_key.replace(UpperCamelCase							,    UpperCamelCase			)

        print(f"""{key} -> {new_key}"""			)
        lowerCAmelCase__ :  List[str]							      =					s_dict.pop(UpperCamelCase			)

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

            s_dict.pop(UpperCamelCase			)

    return s_dict


_lowerCAmelCase      						=	{
    '''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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    import regex as re

    with open(UpperCamelCase							,    """r"""			) as f:
        lowerCAmelCase__ :  Dict							      =					f.read()

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

    lowerCAmelCase__ :  Optional[Any]							      =					re.findall(R"""(.*activations) = \(\'(.*)\',\)"""							,    UpperCamelCase			)[0]
    lowerCAmelCase__ :  Dict							      =					str(activation[1]			)

    lowerCAmelCase__ :  Union[str, Any]							      =					num_experts
    lowerCAmelCase__ :  Union[str, Any]							      =					SwitchTransformersConfig(**UpperCamelCase			)
    return config
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase="./"							,    UpperCamelCase=8			):



    """simple docstring"""





    print(f"""Loading flax weights from : {flax_checkpoint_path}"""			)
    lowerCAmelCase__ :  Optional[int]							      =					checkpoints.load_tax_checkpoint(UpperCamelCase			)

    if gin_file is not None:
        lowerCAmelCase__ :  Dict							      =					convert_gin_to_config(UpperCamelCase							,    UpperCamelCase			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					SwitchTransformersConfig.from_pretrained(UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					SwitchTransformersForConditionalGeneration(UpperCamelCase			)

    lowerCAmelCase__ :  Optional[Any]							      =					flax_params["""target"""]
    lowerCAmelCase__ :  List[Any]							      =					flatten_dict(UpperCamelCase							,    sep="""/"""			)
    lowerCAmelCase__ :  Optional[int]							      =					rename_keys(UpperCamelCase			)
    lowerCAmelCase__ :  List[str]							      =					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__":
 _lowerCAmelCase      						=	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''')
 _lowerCAmelCase      						=	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,
 )




# flake8: noqa
# Lint as: python3
A     :	Optional[Any]    = [
    'VerificationMode',
    'Version',
    'disable_progress_bar',
    'enable_progress_bar',
    'is_progress_bar_enabled',
    'experimental',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental