





'''simple docstring'''

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 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Any="attention"	):
    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 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : Union[str, Any]=False	):
    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 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[Any]	):
    return params[F"{prefix}/layers_{i}/{layer_name}/scale"]





def 						UpperCamelCase    (							_lowerCamelCase : dict						,	*, _lowerCamelCase : int						,	_lowerCamelCase : bool	):
    A__    =      traverse_util.flatten_dict(variables["target"]	)
    A__    =      {"/".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
    A__    =      "encoder/layers_0/mlp/wi_0/kernel" in old
    print("Split MLP:"						,	_lowerCamelCase	)

    A__    =      collections.OrderedDict()

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

    # Encoder.
    for i in range(_lowerCamelCase	):
        # Block i, layer 0 (Self Attention).
        A__    =      tax_layer_norm_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"encoder"						,	"pre_attention_layer_norm"	)
        A__,     A__,     A__,     A__    =      tax_attention_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"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(_lowerCamelCase						,	_lowerCamelCase						,	"encoder"						,	"pre_mlp_layer_norm"	)
        A__,     A__    =      tax_mlp_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"encoder"						,	_lowerCamelCase	)
        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(_lowerCamelCase	):
            # Block i, layer 0 (Self Attention).
            A__    =      tax_layer_norm_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"decoder"						,	"pre_self_attention_layer_norm"	)
            A__,     A__,     A__,     A__    =      tax_attention_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"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(_lowerCamelCase						,	_lowerCamelCase						,	"decoder"						,	"pre_cross_attention_layer_norm"	)
            A__,     A__,     A__,     A__    =      tax_attention_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"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(_lowerCamelCase						,	_lowerCamelCase						,	"decoder"						,	"pre_mlp_layer_norm"	)
            A__,     A__    =      tax_mlp_lookup(_lowerCamelCase						,	_lowerCamelCase						,	"decoder"						,	_lowerCamelCase	)
            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 						UpperCamelCase    (							_lowerCamelCase : Optional[int]						,	_lowerCamelCase : bool	):
    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 						UpperCamelCase    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : List[Any]	):
    A__    =      checkpoints.load_tax_checkpoint(_lowerCamelCase	)
    A__    =      convert_tax_to_pytorch(_lowerCamelCase						,	num_layers=config.num_layers						,	is_encoder_only=_lowerCamelCase	)
    A__    =      make_state_dict(_lowerCamelCase						,	_lowerCamelCase	)
    model.load_state_dict(_lowerCamelCase						,	strict=_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : int						,	_lowerCamelCase : bool = False	):
    A__    =      TaConfig.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:
        A__    =      TaEncoderModel(_lowerCamelCase	)
    else:
        A__    =      TaForConditionalGeneration(_lowerCamelCase	)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(_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__":
  __lowerCAmelCase							:     List[str]		     =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							:     str		     =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'''

__lowerCAmelCase							:     Dict		     ="\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

__lowerCAmelCase							:     Optional[Any]		     =[{"type": "code", "content": INSTALL_CONTENT}]
__lowerCAmelCase							:     Tuple		     ={
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



