



"""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    :Optional[int]     =					{
    '/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_						(UpperCamelCase__		:		List[str]							):
    _UpperCAmelCase							:  List[Any]											=    list(s_dict.keys()							)
    for key in keys:
        _UpperCAmelCase							:  Union[str, Any]											=    R""".*/layers_(\d+)"""
        _UpperCAmelCase							:  str											=    key
        if re.match(UpperCamelCase__     ,       UpperCamelCase__							):
            _UpperCAmelCase							:  str											=    re.sub(r'''layers_(\d+)'''     ,       r'''block/\1/layer'''     ,       UpperCamelCase__							)

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

        if re.match(UpperCamelCase__     ,       UpperCamelCase__							):
            _UpperCAmelCase							:  Tuple											=    re.match(UpperCamelCase__     ,       UpperCamelCase__							).groups()
            if groups[0] == "encoder":
                _UpperCAmelCase							:  Tuple											=    re.sub(r'''/mlp/'''     ,       r'''/1/mlp/'''     ,       UpperCamelCase__							)
                _UpperCAmelCase							:  Optional[int]											=    re.sub(r'''/pre_mlp_layer_norm/'''     ,       r'''/1/layer_norm/'''     ,       UpperCamelCase__							)

            elif groups[0] == "decoder":
                _UpperCAmelCase							:  Dict											=    re.sub(r'''/mlp/'''     ,       r'''/2/mlp/'''     ,       UpperCamelCase__							)
                _UpperCAmelCase							:  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:
                _UpperCAmelCase							:  List[str]											=    new_key.replace(UpperCamelCase__     ,       UpperCamelCase__							)

        print(F'{key} -> {new_key}'							)
        _UpperCAmelCase							:  List[str]											=    s_dict.pop(UpperCamelCase__							)

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

            s_dict.pop(UpperCamelCase__							)

    return s_dict


_lowerCAmelCase    :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  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Tuple							):
    import regex as re

    with open(UpperCamelCase__     ,       '''r'''							) as f:
        _UpperCAmelCase							:  Dict											=    f.read()

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

    _UpperCAmelCase							:  Optional[Any]											=    re.findall(r'''(.*activations) = \(\'(.*)\',\)'''     ,       UpperCamelCase__							)[0]
    _UpperCAmelCase							:  Dict											=    str(activation[1]							)

    _UpperCAmelCase							:  Union[str, Any]											=    num_experts
    _UpperCAmelCase							:  Union[str, Any]											=    SwitchTransformersConfig(**UpperCamelCase__							)
    return config





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		List[str]=None     ,       UpperCamelCase__		:		Any="./"     ,       UpperCamelCase__		:		Tuple=8							):
    print(F'Loading flax weights from : {flax_checkpoint_path}'							)
    _UpperCAmelCase							:  Optional[int]											=    checkpoints.load_tax_checkpoint(UpperCamelCase__							)

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

    _UpperCAmelCase							:  Dict											=    SwitchTransformersForConditionalGeneration(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[Any]											=    flax_params["""target"""]
    _UpperCAmelCase							:  List[Any]											=    flatten_dict(UpperCamelCase__     ,       sep='''/'''							)
    _UpperCAmelCase							:  Optional[int]											=    rename_keys(UpperCamelCase__							)
    _UpperCAmelCase							:  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    :Tuple     =					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    :List[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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      '''focalnet'''




  def __init__(    self  ,__UpperCAmelCase=224  ,__UpperCAmelCase=4  ,__UpperCAmelCase=3  ,__UpperCAmelCase=96  ,__UpperCAmelCase=False  ,__UpperCAmelCase=[192, 384, 768, 768]  ,__UpperCAmelCase=[2, 2, 6, 2]  ,__UpperCAmelCase=[2, 2, 2, 2]  ,__UpperCAmelCase=[3, 3, 3, 3]  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=4.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=False  ,__UpperCAmelCase=1E-4  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=32  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		Optional[Any]:
      super().__init__(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					image_size
      lowerCAmelCase__ :  int							      =					patch_size
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  Dict							      =					embed_dim
      lowerCAmelCase__ :  List[str]							      =					use_conv_embed
      lowerCAmelCase__ :  List[Any]							      =					hidden_sizes
      lowerCAmelCase__ :  Dict							      =					depths
      lowerCAmelCase__ :  List[str]							      =					focal_levels
      lowerCAmelCase__ :  List[str]							      =					focal_windows
      lowerCAmelCase__ :  Dict							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					mlp_ratio
      lowerCAmelCase__ :  Tuple							      =					hidden_dropout_prob
      lowerCAmelCase__ :  Tuple							      =					drop_path_rate
      lowerCAmelCase__ :  Dict							      =					use_layerscale
      lowerCAmelCase__ :  Optional[Any]							      =					layerscale_value
      lowerCAmelCase__ :  str							      =					use_post_layernorm
      lowerCAmelCase__ :  Union[str, Any]							      =					use_post_layernorm_in_modulation
      lowerCAmelCase__ :  int							      =					normalize_modulator
      lowerCAmelCase__ :  Optional[Any]							      =					initializer_range
      lowerCAmelCase__ :  List[str]							      =					layer_norm_eps
      lowerCAmelCase__ :  List[Any]							      =					encoder_stride
      lowerCAmelCase__ :  Dict							      =					["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths      ) + 1      )]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names      )


