



'''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	:       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 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
   # the original model
   UpperCamelCase					=  list(s_dict.keys()    )
   for key in keys:
      UpperCamelCase					=  R'.*/layers_(\d+)'
      UpperCamelCase					=  key
      if re.match(A__    ,						A__    ):
         UpperCamelCase					=  re.sub(R'layers_(\d+)'    ,						R'block/\1/layer'    ,						A__    )

      UpperCamelCase					=  R'(encoder|decoder)\/'

      if re.match(A__    ,						A__    ):
         UpperCamelCase					=  re.match(A__    ,						A__    ).groups()
         if groups[0] == "encoder":
            UpperCamelCase					=  re.sub(R'/mlp/'    ,						R'/1/mlp/'    ,						A__    )
            UpperCamelCase					=  re.sub(R'/pre_mlp_layer_norm/'    ,						R'/1/layer_norm/'    ,						A__    )

         elif groups[0] == "decoder":
            UpperCamelCase					=  re.sub(R'/mlp/'    ,						R'/2/mlp/'    ,						A__    )
            UpperCamelCase					=  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:
            UpperCamelCase					=  new_key.replace(A__    ,						A__    )

      print(F"""{key} -> {new_key}"""    )
      UpperCamelCase					=  s_dict.pop(A__    )

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

         s_dict.pop(A__    )

   return s_dict


_lowerCamelCase	:       Any      	=	{
    "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__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   # Convert a google style config to the hugging face fromat
   import regex as re

   with open(A__    ,						'r'    ) as f:
      UpperCamelCase					=  f.read()

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

   UpperCamelCase					=  re.findall(R'(.*activations) = \(\'(.*)\',\)'    ,						A__    )[0]
   UpperCamelCase					=  str(activation[1]    )

   UpperCamelCase					=  num_experts
   UpperCamelCase					=  SwitchTransformersConfig(**A__    )
   return config







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__=None    ,						A__="./"    ,						A__=8    )       ->	Dict:






   """simple docstring"""
   # Initialise PyTorch model

   print(F"""Loading flax weights from : {flax_checkpoint_path}"""    )
   UpperCamelCase					=  checkpoints.load_tax_checkpoint(A__    )

   if gin_file is not None:
      UpperCamelCase					=  convert_gin_to_config(A__    ,						A__    )
   else:
      UpperCamelCase					=  SwitchTransformersConfig.from_pretrained(A__    )

   UpperCamelCase					=  SwitchTransformersForConditionalGeneration(A__    )

   UpperCamelCase					=  flax_params['target']
   UpperCamelCase					=  flatten_dict(A__    ,						sep='/'    )
   UpperCamelCase					=  rename_keys(A__    )
   UpperCamelCase					=  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__":
   _lowerCamelCase	:       List[str]      	=	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 . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

