






'''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__				,     lowercase__				,     lowercase__				,     lowercase__="attention"):
 lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
 lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
 lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
 lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
 return k, o, q, v






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=False):
 if split_mlp_wi:
  lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
  lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
  lowerCamelCase__  =     (wi_a, wi_a)
 else:
  lowerCamelCase__  =     params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']






def    lowerCamelCase_     (    lowercase__				,     *, lowercase__				,     lowercase__):
 lowerCamelCase__  =     traverse_util.flatten_dict(variables["target"])
 lowerCamelCase__  =     {'/'.join(SCREAMING_SNAKE_CASE_): v for k, v in old.items()}

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

 lowerCamelCase__  =     collections.OrderedDict()

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

 # Encoder.
 for i in range(SCREAMING_SNAKE_CASE_):
  # Block i, layer 0 (Self Attention).
  lowerCamelCase__  =     tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "encoder"				,     "pre_attention_layer_norm")
  lowerCamelCase__  =     tax_attention_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "encoder"				,     "attention")
  lowerCamelCase__  =     layer_norm
  lowerCamelCase__  =     k.T
  lowerCamelCase__  =     o.T
  lowerCamelCase__  =     q.T
  lowerCamelCase__  =     v.T

  # Block i, layer 1 (MLP).
  lowerCamelCase__  =     tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "encoder"				,     "pre_mlp_layer_norm")
  lowerCamelCase__  =     tax_mlp_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "encoder"				,     SCREAMING_SNAKE_CASE_)
  lowerCamelCase__  =     layer_norm
  if split_mlp_wi:
   lowerCamelCase__  =     wi[0].T
   lowerCamelCase__  =     wi[1].T
  else:
   lowerCamelCase__  =     wi.T
  lowerCamelCase__  =     wo.T

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

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

   # Block i, layer 1 (Cross Attention).
   lowerCamelCase__  =     tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "decoder"				,     "pre_cross_attention_layer_norm")
   lowerCamelCase__  =     tax_attention_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "decoder"				,     "encoder_decoder_attention")
   lowerCamelCase__  =     layer_norm
   lowerCamelCase__  =     k.T
   lowerCamelCase__  =     o.T
   lowerCamelCase__  =     q.T
   lowerCamelCase__  =     v.T

   # Block i, layer 2 (MLP).
   lowerCamelCase__  =     tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "decoder"				,     "pre_mlp_layer_norm")
   lowerCamelCase__  =     tax_mlp_lookup(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     "decoder"				,     SCREAMING_SNAKE_CASE_)
   lowerCamelCase__  =     layer_norm
   if split_mlp_wi:
    lowerCamelCase__  =     wi[0].T
    lowerCamelCase__  =     wi[1].T
   else:
    lowerCamelCase__  =     wi.T
   lowerCamelCase__  =     wo.T

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

 return new






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 lowerCamelCase__  =     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:
  lowerCamelCase__  =     state_dict['shared.weight']

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

 return state_dict






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_)
 lowerCamelCase__  =     convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_				,     num_layers=config.num_layers				,     is_encoder_only=SCREAMING_SNAKE_CASE_)
 lowerCamelCase__  =     make_state_dict(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_)
 model.load_state_dict(SCREAMING_SNAKE_CASE_				,     strict=SCREAMING_SNAKE_CASE_)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = False):
 lowerCamelCase__  =     TaConfig.from_json_file(SCREAMING_SNAKE_CASE_)
 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:
  lowerCamelCase__  =     TaEncoderModel(SCREAMING_SNAKE_CASE_)
 else:
  lowerCamelCase__  =     TaForConditionalGeneration(SCREAMING_SNAKE_CASE_)

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

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

 # Verify that we can load the checkpoint.
 model.from_pretrained(SCREAMING_SNAKE_CASE_)
 print("Done")


if __name__ == "__main__":
 __A							:							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
 )
 __A							:							Dict  							=		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'''





import tensorflow as tf

from ...tf_utils import shape_list
class 				lowercase       (							tf.keras.layers.Layer      ):


    '''simple docstring'''

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Dict				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int=1				,     __lowerCamelCase     :     Tuple=False				,     **__lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     d_embed
     lowerCamelCase__  =     d_proj

     lowerCamelCase__  =     cutoffs + [vocab_size]
     lowerCamelCase__  =     [0] + self.cutoffs
     lowerCamelCase__  =     div_val

     lowerCamelCase__  =     self.cutoffs[0]
     lowerCamelCase__  =     len(self.cutoffs     ) - 1
     lowerCamelCase__  =     self.shortlist_size + self.n_clusters
     lowerCamelCase__  =     keep_order

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




     if self.n_clusters > 0:
      lowerCamelCase__  =     self.add_weight(
          shape=(self.n_clusters, self.d_embed)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name="cluster_weight"     )
      lowerCamelCase__  =     self.add_weight(
          shape=(self.n_clusters,)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name="cluster_bias"     )

     if self.div_val == 1:
      for i in range(len(self.cutoffs     )     ):
       if self.d_proj != self.d_embed:
        lowerCamelCase__  =     self.add_weight(
            shape=(self.d_embed, self.d_proj)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_projs_._{i}'''				,     )
        self.out_projs.append(__lowerCamelCase     )
       else:
        self.out_projs.append(__lowerCamelCase     )
       lowerCamelCase__  =     self.add_weight(
           shape=(self.vocab_size, self.d_embed)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._weight'''				,     )
       lowerCamelCase__  =     self.add_weight(
           shape=(self.vocab_size,)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._bias'''				,     )
       self.out_layers.append((weight, bias)     )
     else:
      for i in range(len(self.cutoffs     )     ):
       lowerCamelCase__  ,	lowerCamelCase__  =     self.cutoff_ends[i], self.cutoff_ends[i + 1]
       lowerCamelCase__  =     self.d_embed // (self.div_val**i)

       lowerCamelCase__  =     self.add_weight(
           shape=(d_emb_i, self.d_proj)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_projs_._{i}'''     )
       self.out_projs.append(__lowerCamelCase     )
       lowerCamelCase__  =     self.add_weight(
           shape=(r_idx - l_idx, d_emb_i)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._weight'''				,     )
       lowerCamelCase__  =     self.add_weight(
           shape=(r_idx - l_idx,)				,     initializer="zeros"				,     trainable=__lowerCamelCase				,     name=f'''out_layers_._{i}_._bias'''				,     )
       self.out_layers.append((weight, bias)     )
     super().build(__lowerCamelCase     )

    @staticmethod
    def    a__				(					__lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     List[str]=None     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     x
     if proj is not None:
      lowerCamelCase__  =     tf.einsum("ibd,ed->ibe"				,     __lowerCamelCase				,     __lowerCamelCase     )
     return tf.einsum("ibd,nd->ibn"				,     __lowerCamelCase				,     __lowerCamelCase     ) + b

    @staticmethod
    def    a__				(					__lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Union[str, Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     shape_list(__lowerCamelCase     )
     lowerCamelCase__  =     tf.range(lp_size[0]				,     dtype=target.dtype     )
     lowerCamelCase__  =     tf.stack([r, target]				,     1     )
     return tf.gather_nd(__lowerCamelCase				,     __lowerCamelCase     )




    def    a__				(					self     :     Any				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     str=True				,     __lowerCamelCase     :     Tuple=False     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     0
     if self.n_clusters == 0:
      lowerCamelCase__  =     self._logit(__lowerCamelCase				,     self.out_layers[0][0]				,     self.out_layers[0][1]				,     self.out_projs[0]     )
      if target is not None:
       lowerCamelCase__  =     tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__lowerCamelCase				,     logits=__lowerCamelCase     )
      lowerCamelCase__  =     tf.nn.log_softmax(__lowerCamelCase				,     axis=-1     )
     else:
      lowerCamelCase__  =     shape_list(__lowerCamelCase     )
      lowerCamelCase__  =     []
      lowerCamelCase__  =     tf.zeros(hidden_sizes[:2]     )
      for i in range(len(self.cutoffs     )     ):
       lowerCamelCase__  ,	lowerCamelCase__  =     self.cutoff_ends[i], self.cutoff_ends[i + 1]
       if target is not None:
        lowerCamelCase__  =     (target >= l_idx) & (target < r_idx)
        lowerCamelCase__  =     tf.where(__lowerCamelCase     )
        lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     ) - l_idx

       if self.div_val == 1:
        lowerCamelCase__  =     self.out_layers[0][0][l_idx:r_idx]
        lowerCamelCase__  =     self.out_layers[0][1][l_idx:r_idx]
       else:
        lowerCamelCase__  =     self.out_layers[i][0]
        lowerCamelCase__  =     self.out_layers[i][1]

       if i == 0:
        lowerCamelCase__  =     tf.concat([cur_W, self.cluster_weight]				,     0     )
        lowerCamelCase__  =     tf.concat([cur_b, self.cluster_bias]				,     0     )

        lowerCamelCase__  =     self._logit(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     self.out_projs[0]     )
        lowerCamelCase__  =     tf.nn.log_softmax(__lowerCamelCase     )
        out.append(head_logprob[..., : self.cutoffs[0]]     )
        if target is not None:
         lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     )
         lowerCamelCase__  =     self._gather_logprob(__lowerCamelCase				,     __lowerCamelCase     )
       else:
        lowerCamelCase__  =     self._logit(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     self.out_projs[i]     )
        lowerCamelCase__  =     tf.nn.log_softmax(__lowerCamelCase     )
        lowerCamelCase__  =     self.cutoffs[0] + i - 1  # No probability for the head cluster
        lowerCamelCase__  =     head_logprob[..., cluster_prob_idx, None] + tail_logprob
        out.append(__lowerCamelCase     )
        if target is not None:
         lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     )
         lowerCamelCase__  =     tf.boolean_mask(__lowerCamelCase				,     __lowerCamelCase     )
         lowerCamelCase__  =     self._gather_logprob(__lowerCamelCase				,     __lowerCamelCase     )
         cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
       if target is not None:
        loss += tf.scatter_nd(__lowerCamelCase				,     -cur_logprob				,     shape_list(__lowerCamelCase     )     )
      lowerCamelCase__  =     tf.concat(__lowerCamelCase				,     axis=-1     )

     if target is not None:
      if return_mean:
       lowerCamelCase__  =     tf.reduce_mean(__lowerCamelCase     )
      # Add the training-time loss value to the layer using `self.add_loss()`.
      self.add_loss(__lowerCamelCase     )

      # Log the loss as a metric (we could log arbitrary metrics,
      # including different metrics for training and inference.
      self.add_metric(__lowerCamelCase				,     name=self.name				,     aggregation="mean" if return_mean else ""     )

     return out

