





"""simple docstring"""


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




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




      """simple docstring"""

      return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_="attention"			)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]			)
      _UpperCamelCase      :	Tuple						=       k_tmp.reshape(k_tmp.shape[0]		,k_tmp.shape[1] * k_tmp.shape[2]			)
      _UpperCamelCase      :	Optional[Any]						=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]			)
      _UpperCamelCase      :	Dict						=       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,o_tmp.shape[2]			)
      _UpperCamelCase      :	str						=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]			)
      _UpperCamelCase      :	int						=       q_tmp.reshape(q_tmp.shape[0]		,q_tmp.shape[1] * q_tmp.shape[2]			)
      _UpperCamelCase      :	Dict						=       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]			)
      _UpperCamelCase      :	Optional[Any]						=       v_tmp.reshape(v_tmp.shape[0]		,v_tmp.shape[1] * v_tmp.shape[2]			)
      return k, o, q, v




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_=False			)					->		Dict:




      """simple docstring"""

      if split_mlp_wi:
            _UpperCamelCase      :	Dict						=       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
            _UpperCamelCase      :	Optional[Any]						=       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
            _UpperCamelCase      :	Union[str, Any]						=       (wi_a, wi_a)
      else:
            _UpperCamelCase      :	Optional[Any]						=       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

      _UpperCamelCase      :	int						=       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
      return wi, wo




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




      """simple docstring"""

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




def 					lowercase__    (					lowercase_		,*, lowercase_		,lowercase_		,lowercase_ = False			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase      :	int						=       traverse_util.flatten_dict(variables["target"]			)
      _UpperCamelCase      :	str						=       {"/".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
      _UpperCamelCase      :	Union[str, Any]						=       "encoder/encoder/mlp/wi_0/kernel" in old
      print("Split MLP:"		,lowercase_			)

      _UpperCamelCase      :	List[Any]						=       collections.OrderedDict()

      # Shared embeddings.
      _UpperCamelCase      :	str						=       old["token_embedder/embedding"]

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

            # Block i, layer 1 (MLP).
            _UpperCamelCase      :	Dict						=       tax_layer_norm_lookup(lowercase_		,lowercase_		,"encoder"		,"pre_mlp_layer_norm"			)
            _UpperCamelCase      :	int						=       tax_mlp_lookup(lowercase_		,lowercase_		,"encoder"		,lowercase_			)
            _UpperCamelCase      :	int						=       layer_norm
            if split_mlp_wi:
                  _UpperCamelCase      :	Optional[int]						=       wi[0].T
                  _UpperCamelCase      :	Optional[Any]						=       wi[1].T
            else:
                  _UpperCamelCase      :	Any						=       wi.T
            _UpperCamelCase      :	Optional[Any]						=       wo.T
            if scalable_attention:
                  # convert the rel_embedding of each layer
                  _UpperCamelCase      :	Optional[int]						=       tax_relpos_bias_lookup(
                      lowercase_		,lowercase_		,"encoder"			).T

      _UpperCamelCase      :	str						=       old["encoder/encoder_norm/scale"]

      if not scalable_attention:
            _UpperCamelCase      :	Any						=       tax_relpos_bias_lookup(
                lowercase_		,0		,"encoder"			).T
            _UpperCamelCase      :	str						=       tax_relpos_bias_lookup(
                lowercase_		,0		,"decoder"			).T

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

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

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

                  if scalable_attention:
                        # convert the rel_embedding of each layer
                        _UpperCamelCase      :	Optional[int]						=       tax_relpos_bias_lookup(lowercase_		,lowercase_		,"decoder"			).T

            _UpperCamelCase      :	Tuple						=       old["decoder/decoder_norm/scale"]

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

      return new




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Dict						=       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      :	str						=       state_dict["shared.weight"]

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  _UpperCamelCase      :	Optional[int]						=       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 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_			)					->		Dict:




      """simple docstring"""

      _UpperCamelCase      :	int						=       checkpoints.load_tax_checkpoint(lowercase_			)
      _UpperCamelCase      :	List[str]						=       convert_tax_to_pytorch(
          lowercase_		,num_layers=config.num_layers		,is_encoder_only=lowercase_		,scalable_attention=lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       make_state_dict(lowercase_		,lowercase_			)
      model.load_state_dict(lowercase_		,strict=lowercase_			)




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_ = False		,lowercase_ = False		,)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	int						=       MTaConfig.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:
            _UpperCamelCase      :	Optional[int]						=       UMTaEncoderModel(lowercase_			)
      else:
            _UpperCamelCase      :	Optional[int]						=       UMTaForConditionalGeneration(lowercase_			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(lowercase_		,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__				  =   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
  )
  parser.add_argument(
      "--scalable_attention",
      action="store_true",
      help="Whether the model uses scaled attention (umt5 model)",
      default=False,
  )
  lowerCamelCase__				  =   parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path,
      args.config_file,
      args.pytorch_dump_path,
      args.is_encoder_only,
      args.scalable_attention,
  )







"""simple docstring"""


import math



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       list[list[float]]					,   __a   :       list[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       0.0
        _UpperCamelCase      :	Union[str, Any]						=       0.0
        for i in range(len(__a	)	):
              da += math.pow((sample[i] - weights[0][i])					,   2	)
              da += math.pow((sample[i] - weights[1][i])					,   2	)
              return 0 if da > da else 1
        return 0


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       list[list[int | float]]					,   __a   :       list[int]					,   __a   :       int					,   __a   :       float	)		->				list[list[int | float]]:
        for i in range(len(__a	)	):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights





def 					lowercase__    (					)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

      # weight initialization ( n, C )
      _UpperCamelCase      :	List[str]						=       [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

      # training
      _UpperCamelCase      :	List[Any]						=       SelfOrganizingMap()
      _UpperCamelCase      :	int						=       3
      _UpperCamelCase      :	List[Any]						=       0.5

      for _ in range(lowercase_			):
            for j in range(len(lowercase_			)			):
                  # training sample
                  _UpperCamelCase      :	int						=       training_samples[j]

                  # Compute the winning vector
                  _UpperCamelCase      :	Tuple						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

                  # Update the winning vector
                  _UpperCamelCase      :	int						=       self_organizing_map.update(lowercase_		,lowercase_		,lowercase_		,lowercase_			)

    # classify test sample
      _UpperCamelCase      :	Optional[int]						=       [0, 0, 0, 1]
      _UpperCamelCase      :	Dict						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

      # results
      print(F'''Clusters that the test sample belongs to : {winner}'''			)
      print(F'''Weights that have been trained : {weights}'''			)


# running the main() function
if __name__ == "__main__":
  main()
