





'''simple docstring'''




import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Tuple		)		->  Dict:
   return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Optional[int]="attention"		)		->  Optional[Any]:
   snake_case 						=   snake_case 						=   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]		)
   snake_case 						=   k_tmp.reshape(k_tmp.shape[0]      ,    k_tmp.shape[1] * k_tmp.shape[2]		)
   snake_case 						=   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]		)
   snake_case 						=   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,    o_tmp.shape[2]		)
   snake_case 						=   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]		)
   snake_case 						=   q_tmp.reshape(q_tmp.shape[0]      ,    q_tmp.shape[1] * q_tmp.shape[2]		)
   snake_case 						=   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]		)
   snake_case 						=   v_tmp.reshape(v_tmp.shape[0]      ,    v_tmp.shape[1] * v_tmp.shape[2]		)
   return k, o, q, v





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Optional[Any]=False		)		->  int:
   if split_mlp_wi:
      snake_case 						=   params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
      snake_case 						=   params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
      snake_case 						=   (wi_a, wi_a)
   else:
      snake_case 						=   params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

   snake_case 						=   params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
   return wi, wo





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							Optional[Any]		)		->  int:
   return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]





def 	__lowerCamelCase			(      __lowerCAmelCase       :							dict      ,    *, __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							bool      ,    __lowerCAmelCase       :							bool = False		)		->  List[str]:
   snake_case 						=   traverse_util.flatten_dict(variables["""target"""]		)
   snake_case 						=   {"""/""".join(__lowerCAmelCase		): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   snake_case 						=   """encoder/encoder/mlp/wi_0/kernel""" in old
   print("""Split MLP:"""      ,    __lowerCAmelCase		)

   snake_case 						=   collections.OrderedDict()

   # Shared embeddings.
   snake_case 						=   old["""token_embedder/embedding"""]

   # Encoder.
   for i in range(__lowerCAmelCase		):
      # Block i, layer 0 (Self Attention).
      snake_case 						=   tax_layer_norm_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """encoder"""      ,    """pre_attention_layer_norm"""		)
      snake_case						,    snake_case						,    snake_case						,    snake_case 						=   tax_attention_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """encoder"""      ,    """attention"""		)
      snake_case 						=   layer_norm
      snake_case 						=   k.T
      snake_case 						=   o.T
      snake_case 						=   q.T
      snake_case 						=   v.T

      # Block i, layer 1 (MLP).
      snake_case 						=   tax_layer_norm_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """encoder"""      ,    """pre_mlp_layer_norm"""		)
      snake_case						,    snake_case 						=   tax_mlp_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """encoder"""      ,    __lowerCAmelCase		)
      snake_case 						=   layer_norm
      if split_mlp_wi:
         snake_case 						=   wi[0].T
         snake_case 						=   wi[1].T
      else:
         snake_case 						=   wi.T
      snake_case 						=   wo.T
      if scalable_attention:
         # convert the rel_embedding of each layer
         snake_case 						=   tax_relpos_bias_lookup(
             __lowerCAmelCase      ,    __lowerCAmelCase      ,    """encoder"""		).T

   snake_case 						=   old["""encoder/encoder_norm/scale"""]

   if not scalable_attention:
      snake_case 						=   tax_relpos_bias_lookup(
          __lowerCAmelCase      ,    0      ,    """encoder"""		).T
      snake_case 						=   tax_relpos_bias_lookup(
          __lowerCAmelCase      ,    0      ,    """decoder"""		).T

   if not is_encoder_only:
      # Decoder.
      for i in range(__lowerCAmelCase		):
         # Block i, layer 0 (Self Attention).
         snake_case 						=   tax_layer_norm_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""      ,    """pre_self_attention_layer_norm"""		)
         snake_case						,    snake_case						,    snake_case						,    snake_case 						=   tax_attention_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""      ,    """self_attention"""		)
         snake_case 						=   layer_norm
         snake_case 						=   k.T
         snake_case 						=   o.T
         snake_case 						=   q.T
         snake_case 						=   v.T

         # Block i, layer 1 (Cross Attention).
         snake_case 						=   tax_layer_norm_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""      ,    """pre_cross_attention_layer_norm"""		)
         snake_case						,    snake_case						,    snake_case						,    snake_case 						=   tax_attention_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""      ,    """encoder_decoder_attention"""		)
         snake_case 						=   layer_norm
         snake_case 						=   k.T
         snake_case 						=   o.T
         snake_case 						=   q.T
         snake_case 						=   v.T

         # Block i, layer 2 (MLP).
         snake_case 						=   tax_layer_norm_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""      ,    """pre_mlp_layer_norm"""		)
         snake_case						,    snake_case 						=   tax_mlp_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""      ,    __lowerCAmelCase		)
         snake_case 						=   layer_norm
         if split_mlp_wi:
            snake_case 						=   wi[0].T
            snake_case 						=   wi[1].T
         else:
            snake_case 						=   wi.T
         snake_case 						=   wo.T

         if scalable_attention:
            # convert the rel_embedding of each layer
            snake_case 						=   tax_relpos_bias_lookup(__lowerCAmelCase      ,    __lowerCAmelCase      ,    """decoder"""		).T

      snake_case 						=   old["""decoder/decoder_norm/scale"""]

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         snake_case 						=   old["""decoder/logits_dense/kernel"""].T

   return new





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Optional[Any]      ,    __lowerCAmelCase       :							bool		)		->  List[Any]:
   snake_case 						=   collections.OrderedDict([(k, torch.from_numpy(v.copy()		)) for (k, v) in converted_params.items()]		)

   # Add what is missing.
   if "encoder.embed_tokens.weight" not in state_dict:
      snake_case 						=   state_dict["""shared.weight"""]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         snake_case 						=   state_dict["""shared.weight"""]

      if "lm_head.weight" not in state_dict:  # For old 1.0 models.
         print("""Using shared word embeddings as lm_head."""		)
         snake_case 						=   state_dict["""shared.weight"""]

   return state_dict





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							List[Any]		)		->  Union[str, Any]:
   snake_case 						=   checkpoints.load_tax_checkpoint(__lowerCAmelCase		)
   snake_case 						=   convert_tax_to_pytorch(
       __lowerCAmelCase      ,    num_layers=config.num_layers      ,    is_encoder_only=__lowerCAmelCase      ,    scalable_attention=__lowerCAmelCase		)
   snake_case 						=   make_state_dict(__lowerCAmelCase      ,    __lowerCAmelCase		)
   model.load_state_dict(__lowerCAmelCase      ,    strict=__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							List[Any]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							bool = False      ,    __lowerCAmelCase       :							bool = False      ,    )		->  Union[str, Any]:
   snake_case 						=   MTaConfig.from_json_file(__lowerCAmelCase		)
   print(F'''Building PyTorch model from configuration: {config}'''		)
   # Non-v1.1 checkpoints could also use T5Model, but this works for all.
   # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
   if is_encoder_only:
      snake_case 						=   UMTaEncoderModel(__lowerCAmelCase		)
   else:
      snake_case 						=   UMTaForConditionalGeneration(__lowerCAmelCase		)

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)

   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)

   # Verify that we can load the checkpoint.
   model.from_pretrained(__lowerCAmelCase		)
   print("""Done"""		)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
 )
 parser.add_argument(
     "--scalable_attention",
     action="store_true",
     help="Whether the model uses scaled attention (umt5 model)",
     default=False,
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path,
     args.config_file,
     args.pytorch_dump_path,
     args.is_encoder_only,
     args.scalable_attention,
 )








'''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,
)

