
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_="attention"						)     ->     Tuple:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
   return k, o, q, v
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False						)     ->     Dict:




   """simple docstring"""
   if split_mlp_wi:
      _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
      _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
      _SCREAMING_SNAKE_CASE      =				(wi_a, wi_a)
   else:
      _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

   _SCREAMING_SNAKE_CASE      =				params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
   return wi, wo
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   return params[F"{prefix}/layers_{i}/{layer_name}/scale"]
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      *, SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				traverse_util.flatten_dict(variables["""target"""]						)
   _SCREAMING_SNAKE_CASE      =				{"""/""".join(SCREAMING_SNAKE_CASE_						): v for k, v in old.items()}

   # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
   _SCREAMING_SNAKE_CASE      =				"""encoder/layers_0/mlp/wi_0/kernel""" in old
   print("""Split MLP:"""    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				collections.OrderedDict()

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

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

      # Block i, layer 1 (MLP).
      _SCREAMING_SNAKE_CASE      =				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """encoder"""    ,      """pre_mlp_layer_norm"""						)
      _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				tax_mlp_lookup(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      """encoder"""    ,      SCREAMING_SNAKE_CASE_						)
      _SCREAMING_SNAKE_CASE      =				layer_norm
      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				wi[0].T
         _SCREAMING_SNAKE_CASE      =				wi[1].T
      else:
         _SCREAMING_SNAKE_CASE      =				wi.T
      _SCREAMING_SNAKE_CASE      =				wo.T

   _SCREAMING_SNAKE_CASE      =				old[
       """encoder/relpos_bias/rel_embedding"""
   ].T
   _SCREAMING_SNAKE_CASE      =				old["""encoder/encoder_norm/scale"""]

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

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

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

      _SCREAMING_SNAKE_CASE      =				old["""decoder/decoder_norm/scale"""]
      _SCREAMING_SNAKE_CASE      =				old[
          """decoder/relpos_bias/rel_embedding"""
      ].T

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

   return new
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_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:
      _SCREAMING_SNAKE_CASE      =				state_dict["""shared.weight"""]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         _SCREAMING_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."""						)
         _SCREAMING_SNAKE_CASE      =				state_dict["""shared.weight"""]

   return state_dict
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_    ,      num_layers=config.num_layers    ,      is_encoder_only=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				make_state_dict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   model.load_state_dict(SCREAMING_SNAKE_CASE_    ,      strict=SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				TaConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
   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:
      _SCREAMING_SNAKE_CASE      =				TaEncoderModel(SCREAMING_SNAKE_CASE_						)
   else:
      _SCREAMING_SNAKE_CASE      =				TaForConditionalGeneration(SCREAMING_SNAKE_CASE_						)

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

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

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


if __name__ == "__main__":
     UpperCamelCase__      :		Optional[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
     )
     UpperCamelCase__      :		int       							=  parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )








'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   return "".join([hex(SCREAMING_SNAKE_CASE_						)[2:].zfill(2						).upper() for byte in list(SCREAMING_SNAKE_CASE_						)]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bytes:




   """simple docstring"""
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(SCREAMING_SNAKE_CASE_						) % 2) != 0:
      raise ValueError(
          """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""						)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(SCREAMING_SNAKE_CASE_						) <= set("""0123456789ABCDEF"""						):
      raise ValueError(
          """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""						)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]    ,      16						) for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)    ,      2						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






