




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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_="attention"						)       ->	str:



       """simple docstring"""

       A__							=   params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
       A__							=   params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
       A__							=   params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
       A__							=   params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
       return k, o, q, v







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_=False						)       ->	Union[str, Any]:



       """simple docstring"""

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

       A__							=   params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
       return wi, wo







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	Union[str, Any]:



       """simple docstring"""

       return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]







def SCREAMING_SNAKE_CASE    (	lowercase_			,    *, lowercase_			,    lowercase_						)       ->	Union[str, Any]:



       """simple docstring"""

       A__							=   traverse_util.flatten_dict(variables['''target''']						)
       A__							=   {'''/'''.join(lowercase_						): v for k, v in old.items()}

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

       A__							=   collections.OrderedDict()

       # Shared embeddings.
       A__							=   old['''token_embedder/embedding''']

       # Encoder.
       for i in range(lowercase_						):
              # Block i, layer 0 (Self Attention).
              A__							=   tax_layer_norm_lookup(lowercase_			,    lowercase_			,    '''encoder'''			,    '''pre_attention_layer_norm'''						)
              A__			,							A__			,							A__			,							A__							=   tax_attention_lookup(lowercase_			,    lowercase_			,    '''encoder'''			,    '''attention'''						)
              A__							=   layer_norm
              A__							=   k.T
              A__							=   o.T
              A__							=   q.T
              A__							=   v.T

              # Block i, layer 1 (MLP).
              A__							=   tax_layer_norm_lookup(lowercase_			,    lowercase_			,    '''encoder'''			,    '''pre_mlp_layer_norm'''						)
              A__			,							A__							=   tax_mlp_lookup(lowercase_			,    lowercase_			,    '''encoder'''			,    lowercase_						)
              A__							=   layer_norm
              if split_mlp_wi:
                     A__							=   wi[0].T
                     A__							=   wi[1].T
              else:
                     A__							=   wi.T
              A__							=   wo.T

       A__							=   old[
           '''encoder/relpos_bias/rel_embedding'''
       ].T
       A__							=   old['''encoder/encoder_norm/scale''']

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

                     # Block i, layer 1 (Cross Attention).
                     A__							=   tax_layer_norm_lookup(lowercase_			,    lowercase_			,    '''decoder'''			,    '''pre_cross_attention_layer_norm'''						)
                     A__			,							A__			,							A__			,							A__							=   tax_attention_lookup(lowercase_			,    lowercase_			,    '''decoder'''			,    '''encoder_decoder_attention'''						)
                     A__							=   layer_norm
                     A__							=   k.T
                     A__							=   o.T
                     A__							=   q.T
                     A__							=   v.T

                     # Block i, layer 2 (MLP).
                     A__							=   tax_layer_norm_lookup(lowercase_			,    lowercase_			,    '''decoder'''			,    '''pre_mlp_layer_norm'''						)
                     A__			,							A__							=   tax_mlp_lookup(lowercase_			,    lowercase_			,    '''decoder'''			,    lowercase_						)
                     A__							=   layer_norm
                     if split_mlp_wi:
                            A__							=   wi[0].T
                            A__							=   wi[1].T
                     else:
                            A__							=   wi.T
                     A__							=   wo.T

              A__							=   old['''decoder/decoder_norm/scale''']
              A__							=   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:
                     A__							=   old['''decoder/logits_dense/kernel'''].T

       return new







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

       A__							=   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:
              A__							=   state_dict['''shared.weight''']

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

       return state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_						)       ->	List[str]:



       """simple docstring"""

       A__							=   checkpoints.load_tax_checkpoint(lowercase_						)
       A__							=   convert_tax_to_pytorch(lowercase_			,    num_layers=config.num_layers			,    is_encoder_only=lowercase_						)
       A__							=   make_state_dict(lowercase_			,    lowercase_						)
       model.load_state_dict(lowercase_			,    strict=lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_ = False						)       ->	Optional[int]:



       """simple docstring"""

       A__							=   TaConfig.from_json_file(lowercase_						)
       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:
              A__							=   TaEncoderModel(lowercase_						)
       else:
              A__							=   TaForConditionalGeneration(lowercase_						)

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

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

       # Verify that we can load the checkpoint.
       model.from_pretrained(lowercase_						)
       print('''Done'''						)


if __name__ == "__main__":
  _lowerCamelCase     :		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
  )
  _lowerCamelCase     :		Tuple								=       parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )








_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 3.6,
    "mph": 1.609_344,
    "knot": 1.852,
}

_lowerCamelCase     :		dict[str, float]								=       {
    "km/h": 1.0,
    "m/s": 0.277_777_778,
    "mph": 0.621_371_192,
    "knot": 0.539_956_803,
}







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	float:



       """simple docstring"""

       if unit_to not in speed_chart or unit_from not in speed_chart_inverse:
              A__							=   (
                  f"""Incorrect 'from_type' or 'to_type' value: {unit_from!r}, {unit_to!r}\n"""
                  f"""Valid values are: {", ".join(lowercase_						)}"""
              )
              raise ValueError(lowercase_						)
       return round(speed * speed_chart[unit_from] * speed_chart_inverse[unit_to]			,    3						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


