




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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]



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



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase=False   ):
  if split_mlp_wi:
    lowercase    :Any      	=						params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
    lowercase    :int      	=						params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
    lowercase    :Union[str, Any]      	=						(wi_a, wi_a)
  else:
    lowercase    :Optional[Any]      	=						params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

  lowercase    :Union[str, Any]      	=						params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
  return wi, wo



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]



def       UpperCAmelCase__  (     lowerCamelCase,			*, lowerCamelCase,			lowerCamelCase,			lowerCamelCase = False   ):
  lowercase    :Dict      	=						traverse_util.flatten_dict(variables["target"]   )
  lowercase    :Union[str, Any]      	=						{"/".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
  lowercase    :Tuple      	=						"encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:",			lowerCamelCase   )

  lowercase    :Tuple      	=						collections.OrderedDict()

  # Shared embeddings.
  lowercase    :Tuple      	=						old["token_embedder/embedding"]

  # Encoder.
  for i in range(lowerCamelCase   ):
    # Block i, layer 0 (Self Attention).
    lowercase    :List[str]      	=						tax_layer_norm_lookup(lowerCamelCase,			lowerCamelCase,			"encoder",			"pre_attention_layer_norm"   )
    lowercase	,    lowercase	,    lowercase	,    lowercase    :int      	=						tax_attention_lookup(lowerCamelCase,			lowerCamelCase,			"encoder",			"attention"   )
    lowercase    :List[Any]      	=						layer_norm
    lowercase    :int      	=						k.T
    lowercase    :Optional[Any]      	=						o.T
    lowercase    :str      	=						q.T
    lowercase    :str      	=						v.T

    # Block i, layer 1 (MLP).
    lowercase    :Optional[int]      	=						tax_layer_norm_lookup(lowerCamelCase,			lowerCamelCase,			"encoder",			"pre_mlp_layer_norm"   )
    lowercase	,    lowercase    :Tuple      	=						tax_mlp_lookup(lowerCamelCase,			lowerCamelCase,			"encoder",			lowerCamelCase   )
    lowercase    :Optional[int]      	=						layer_norm
    if split_mlp_wi:
      lowercase    :Any      	=						wi[0].T
      lowercase    :Any      	=						wi[1].T
    else:
      lowercase    :int      	=						wi.T
    lowercase    :List[str]      	=						wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      lowercase    :List[Any]      	=						tax_relpos_bias_lookup(
          lowerCamelCase,			lowerCamelCase,			"encoder"   ).T

  lowercase    :Optional[int]      	=						old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    lowercase    :int      	=						tax_relpos_bias_lookup(
        lowerCamelCase,			0,			"encoder"   ).T
    lowercase    :Optional[Any]      	=						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).
      lowercase    :Union[str, Any]      	=						tax_layer_norm_lookup(lowerCamelCase,			lowerCamelCase,			"decoder",			"pre_self_attention_layer_norm"   )
      lowercase	,    lowercase	,    lowercase	,    lowercase    :Any      	=						tax_attention_lookup(lowerCamelCase,			lowerCamelCase,			"decoder",			"self_attention"   )
      lowercase    :str      	=						layer_norm
      lowercase    :List[str]      	=						k.T
      lowercase    :List[str]      	=						o.T
      lowercase    :Optional[Any]      	=						q.T
      lowercase    :List[str]      	=						v.T

      # Block i, layer 1 (Cross Attention).
      lowercase    :Optional[int]      	=						tax_layer_norm_lookup(lowerCamelCase,			lowerCamelCase,			"decoder",			"pre_cross_attention_layer_norm"   )
      lowercase	,    lowercase	,    lowercase	,    lowercase    :Tuple      	=						tax_attention_lookup(lowerCamelCase,			lowerCamelCase,			"decoder",			"encoder_decoder_attention"   )
      lowercase    :Optional[int]      	=						layer_norm
      lowercase    :Tuple      	=						k.T
      lowercase    :Union[str, Any]      	=						o.T
      lowercase    :List[str]      	=						q.T
      lowercase    :Tuple      	=						v.T

      # Block i, layer 2 (MLP).
      lowercase    :str      	=						tax_layer_norm_lookup(lowerCamelCase,			lowerCamelCase,			"decoder",			"pre_mlp_layer_norm"   )
      lowercase	,    lowercase    :Tuple      	=						tax_mlp_lookup(lowerCamelCase,			lowerCamelCase,			"decoder",			lowerCamelCase   )
      lowercase    :str      	=						layer_norm
      if split_mlp_wi:
        lowercase    :Optional[int]      	=						wi[0].T
        lowercase    :str      	=						wi[1].T
      else:
        lowercase    :str      	=						wi.T
      lowercase    :Dict      	=						wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        lowercase    :Any      	=						tax_relpos_bias_lookup(lowerCamelCase,			lowerCamelCase,			"decoder"   ).T

    lowercase    :List[Any]      	=						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:
      lowercase    :Any      	=						old["decoder/logits_dense/kernel"].T

  return new



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  lowercase    :Optional[int]      	=						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:
    lowercase    :int      	=						state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      lowercase    :Union[str, Any]      	=						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."   )
      lowercase    :Optional[Any]      	=						state_dict["shared.weight"]

  return state_dict



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
  lowercase    :List[Any]      	=						checkpoints.load_tax_checkpoint(lowerCamelCase   )
  lowercase    :Optional[Any]      	=						convert_tax_to_pytorch(
      lowerCamelCase,			num_layers=config.num_layers,			is_encoder_only=lowerCamelCase,			scalable_attention=lowerCamelCase   )
  lowercase    :int      	=						make_state_dict(lowerCamelCase,			lowerCamelCase   )
  model.load_state_dict(lowerCamelCase,			strict=lowerCamelCase   )



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase = False,			lowerCamelCase = False,			):
  lowercase    :int      	=						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:
    lowercase    :Dict      	=						UMTaEncoderModel(lowerCamelCase   )
  else:
    lowercase    :List[str]      	=						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__":
     _UpperCAmelCase	:						List[Any]			=     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,
     )
     _UpperCAmelCase	:						Optional[int]			=     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,
     )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Optional[Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



