



"""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_						(UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		List[Any]="attention"							):
    _UpperCAmelCase							:  List[str]											=    params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
    _UpperCAmelCase							:  str											=    params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
    _UpperCAmelCase							:  Tuple											=    params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
    _UpperCAmelCase							:  Any											=    params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Tuple=False							):
    if split_mlp_wi:
        _UpperCAmelCase							:  Dict											=    params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
        _UpperCAmelCase							:  str											=    params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
        _UpperCAmelCase							:  Any											=    (wi_a, wi_a)
    else:
        _UpperCAmelCase							:  Tuple											=    params[F'{prefix}/layers_{i}/mlp/wi/kernel']

    _UpperCAmelCase							:  Optional[Any]											=    params[F'{prefix}/layers_{i}/mlp/wo/kernel']
    return wi, wo





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Any							):
    return params[F'{prefix}/layers_{i}/{layer_name}/scale']





def  lowerCamelCase_						(UpperCamelCase__		:		dict     ,       *, UpperCamelCase__		:		int     ,       UpperCamelCase__		:		bool							):
    _UpperCAmelCase							:  Any											=    traverse_util.flatten_dict(variables['''target''']							)
    _UpperCAmelCase							:  str											=    {'''/'''.join(UpperCamelCase__							): v for k, v in old.items()}

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

    _UpperCAmelCase							:  Optional[Any]											=    collections.OrderedDict()

    # Shared embeddings.
    _UpperCAmelCase							:  int											=    old['''token_embedder/embedding''']

    # Encoder.
    for i in range(UpperCamelCase__							):
        # Block i, layer 0 (Self Attention).
        _UpperCAmelCase							:  Tuple											=    tax_layer_norm_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''encoder'''     ,       '''pre_attention_layer_norm'''							)
        _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    tax_attention_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''encoder'''     ,       '''attention'''							)
        _UpperCAmelCase							:  List[str]											=    layer_norm
        _UpperCAmelCase							:  Union[str, Any]											=    k.T
        _UpperCAmelCase							:  str											=    o.T
        _UpperCAmelCase							:  Tuple											=    q.T
        _UpperCAmelCase							:  List[str]											=    v.T

        # Block i, layer 1 (MLP).
        _UpperCAmelCase							:  Optional[int]											=    tax_layer_norm_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''encoder'''     ,       '''pre_mlp_layer_norm'''							)
        _UpperCAmelCase							,				_UpperCAmelCase							:  Optional[int]											=    tax_mlp_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''encoder'''     ,       UpperCamelCase__							)
        _UpperCAmelCase							:  Tuple											=    layer_norm
        if split_mlp_wi:
            _UpperCAmelCase							:  List[Any]											=    wi[0].T
            _UpperCAmelCase							:  List[Any]											=    wi[1].T
        else:
            _UpperCAmelCase							:  Tuple											=    wi.T
        _UpperCAmelCase							:  Union[str, Any]											=    wo.T

    _UpperCAmelCase							:  Optional[int]											=    old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    _UpperCAmelCase							:  Tuple											=    old['''encoder/encoder_norm/scale''']

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

            # Block i, layer 1 (Cross Attention).
            _UpperCAmelCase							:  Optional[int]											=    tax_layer_norm_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''decoder'''     ,       '''pre_cross_attention_layer_norm'''							)
            _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    tax_attention_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''decoder'''     ,       '''encoder_decoder_attention'''							)
            _UpperCAmelCase							:  Dict											=    layer_norm
            _UpperCAmelCase							:  Tuple											=    k.T
            _UpperCAmelCase							:  Optional[int]											=    o.T
            _UpperCAmelCase							:  Optional[Any]											=    q.T
            _UpperCAmelCase							:  Tuple											=    v.T

            # Block i, layer 2 (MLP).
            _UpperCAmelCase							:  Optional[Any]											=    tax_layer_norm_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''decoder'''     ,       '''pre_mlp_layer_norm'''							)
            _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    tax_mlp_lookup(UpperCamelCase__     ,       UpperCamelCase__     ,       '''decoder'''     ,       UpperCamelCase__							)
            _UpperCAmelCase							:  Dict											=    layer_norm
            if split_mlp_wi:
                _UpperCAmelCase							:  Optional[Any]											=    wi[0].T
                _UpperCAmelCase							:  Union[str, Any]											=    wi[1].T
            else:
                _UpperCAmelCase							:  int											=    wi.T
            _UpperCAmelCase							:  List[Any]											=    wo.T

        _UpperCAmelCase							:  Any											=    old['''decoder/decoder_norm/scale''']
        _UpperCAmelCase							:  Dict											=    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:
            _UpperCAmelCase							:  Union[str, Any]											=    old['''decoder/logits_dense/kernel'''].T

    return new





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		bool							):
    _UpperCAmelCase							:  Tuple											=    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:
        _UpperCAmelCase							:  int											=    state_dict['''shared.weight''']

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

    return state_dict





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    checkpoints.load_tax_checkpoint(UpperCamelCase__							)
    _UpperCAmelCase							:  int											=    convert_tax_to_pytorch(UpperCamelCase__     ,       num_layers=config.num_layers     ,       is_encoder_only=UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    make_state_dict(UpperCamelCase__     ,       UpperCamelCase__							)
    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		bool = False							):
    _UpperCAmelCase							:  Any											=    TaConfig.from_json_file(UpperCamelCase__							)
    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:
        _UpperCAmelCase							:  str											=    TaEncoderModel(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:  Dict											=    TaForConditionalGeneration(UpperCamelCase__							)

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

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

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


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, 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
  )







"""simple docstring"""
def  lowerCamelCase_						(UpperCamelCase__		:		int							):
    if not isinstance(UpperCamelCase__     ,       UpperCamelCase__							) or number < 0:
        raise ValueError('''Input must be a non-negative integer'''							)

    _UpperCAmelCase							:  str											=    0
    while number:
        # This way we arrive at next set bit (next 1) instead of looping
        # through each bit and checking for 1s hence the
        # loop won't run 32 times it will only run the number of `1` times
        number &= number - 1
        count += 1
    return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()


