


"""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   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  List[str]="attention" )	->						List[str]:


    """simple docstring"""





    snake_case    :      str      					=    params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
    snake_case    :      Optional[int]      					=    params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
    snake_case    :      Any      					=    params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
    snake_case    :      Tuple      					=    params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  str						,							lowercase  :  Optional[Any]=False )	->						str:


    """simple docstring"""





    if split_mlp_wi:
        snake_case    :      str      					=    params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
        snake_case    :      int      					=    params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
        snake_case    :      Any      					=    (wi_a, wi_a)
    else:
        snake_case    :      List[Any]      					=    params[F'{prefix}/layers_{i}/mlp/wi/kernel']

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







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  List[str]						,							lowercase  :  Optional[int]						,							lowercase  :  Tuple )	->						Union[str, Any]:


    """simple docstring"""





    return params[F'{prefix}/layers_{i}/{layer_name}/scale']







def   __lowerCAmelCase						(						lowercase  :  dict						,							*, lowercase  :  int						,							lowercase  :  bool )	->						str:


    """simple docstring"""





    snake_case    :      str      					=    traverse_util.flatten_dict(variables["target"] )
    snake_case    :      Optional[int]      					=    {"/".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
    snake_case    :      Tuple      					=    "encoder/layers_0/mlp/wi_0/kernel" in old
    print("Split MLP:"						,							lowercase )

    snake_case    :      Dict      					=    collections.OrderedDict()

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

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

        # Block i, layer 1 (MLP).
        snake_case    :      Optional[int]      					=    tax_layer_norm_lookup(lowercase						,							lowercase						,							"encoder"						,							"pre_mlp_layer_norm" )
        snake_case					,snake_case    :      Dict      					=    tax_mlp_lookup(lowercase						,							lowercase						,							"encoder"						,							lowercase )
        snake_case    :      Optional[Any]      					=    layer_norm
        if split_mlp_wi:
            snake_case    :      Optional[Any]      					=    wi[0].T
            snake_case    :      Union[str, Any]      					=    wi[1].T
        else:
            snake_case    :      Union[str, Any]      					=    wi.T
        snake_case    :      Optional[int]      					=    wo.T

    snake_case    :      int      					=    old[
        "encoder/relpos_bias/rel_embedding"
    ].T
    snake_case    :      str      					=    old["encoder/encoder_norm/scale"]

    if not is_encoder_only:
        # Decoder.
        for i in range(lowercase ):
            # Block i, layer 0 (Self Attention).
            snake_case    :      List[str]      					=    tax_layer_norm_lookup(lowercase						,							lowercase						,							"decoder"						,							"pre_self_attention_layer_norm" )
            snake_case					,snake_case					,snake_case					,snake_case    :      List[Any]      					=    tax_attention_lookup(lowercase						,							lowercase						,							"decoder"						,							"self_attention" )
            snake_case    :      List[Any]      					=    layer_norm
            snake_case    :      Optional[int]      					=    k.T
            snake_case    :      str      					=    o.T
            snake_case    :      Optional[int]      					=    q.T
            snake_case    :      str      					=    v.T

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

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

        snake_case    :      Optional[int]      					=    old["decoder/decoder_norm/scale"]
        snake_case    :      int      					=    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:
            snake_case    :      Dict      					=    old["decoder/logits_dense/kernel"].T

    return new







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  bool )	->						Dict:


    """simple docstring"""





    snake_case    :      List[str]      					=    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    :      List[str]      					=    state_dict["shared.weight"]

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

    return state_dict







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Optional[int] )	->						Tuple:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  bool = False )	->						Dict:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    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:
        snake_case    :      Optional[Any]      					=    TaEncoderModel(lowercase )
    else:
        snake_case    :      Optional[Any]      					=    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__":
  __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
  )
  __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
  )









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            print(data[j]						,							end=" " )
        print(" " )
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    snake_case    :      Union[str, Any]      					=    arr[i]
    combination_util(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


if __name__ == "__main__":
  # Driver code to check the function above
  __snake_case   			=    [10, 20, 30, 40, 50]
  print_combination(arr, len(arr), 3)
  # This code is contributed by Ambuj sahu





