





'''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 	UpperCAmelCase_							(					__lowercase							:      List[str]			,     __lowercase							:      List[Any]			,     __lowercase							:      List[Any]       )		->       Dict:


     '''simple docstring'''



     return params[f'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      Any			,     __lowercase							:      Tuple			,     __lowercase							:      Any="attention"       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   _UpperCAmelCase     							=   np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]       )
     _UpperCAmelCase     							=   k_tmp.reshape(k_tmp.shape[0]			,     k_tmp.shape[1] * k_tmp.shape[2]       )
     _UpperCAmelCase     							=   np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]       )
     _UpperCAmelCase     							=   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,     o_tmp.shape[2]       )
     _UpperCAmelCase     							=   np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]       )
     _UpperCAmelCase     							=   q_tmp.reshape(q_tmp.shape[0]			,     q_tmp.shape[1] * q_tmp.shape[2]       )
     _UpperCAmelCase     							=   np.ascontiguousarray(params[f'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]       )
     _UpperCAmelCase     							=   v_tmp.reshape(v_tmp.shape[0]			,     v_tmp.shape[1] * v_tmp.shape[2]       )
     return k, o, q, v
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]=False       )		->       str:


     '''simple docstring'''



     if split_mlp_wi:
          _UpperCAmelCase     							=   params[f'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
          _UpperCAmelCase     							=   params[f'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
          _UpperCAmelCase     							=   (wi_a, wi_a)
     else:
          _UpperCAmelCase     							=   params[f'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

     _UpperCAmelCase     							=   params[f'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
     return wi, wo
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      str			,     __lowercase							:      Dict			,     __lowercase							:      Optional[Any]       )		->       Dict:


     '''simple docstring'''



     return params[f'{prefix}/{prefix}/{layer_name}/scale'][:, i]
def 	UpperCAmelCase_							(					__lowercase							:      dict			,     *, __lowercase							:      int			,     __lowercase							:      bool			,     __lowercase							:      bool = False       )		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   traverse_util.flatten_dict(variables["target"]       )
     _UpperCAmelCase     							=   {"/".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
     _UpperCAmelCase     							=   "encoder/encoder/mlp/wi_0/kernel" in old
     print("Split MLP:"			,     SCREAMING_SNAKE_CASE__       )

     _UpperCAmelCase     							=   collections.OrderedDict()

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

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

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

     _UpperCAmelCase     							=   old["encoder/encoder_norm/scale"]

     if not scalable_attention:
          _UpperCAmelCase     							=   tax_relpos_bias_lookup(
              SCREAMING_SNAKE_CASE__			,     0			,     "encoder"       ).T
          _UpperCAmelCase     							=   tax_relpos_bias_lookup(
              SCREAMING_SNAKE_CASE__			,     0			,     "decoder"       ).T

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

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

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

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    _UpperCAmelCase     							=   tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     "decoder"       ).T

          _UpperCAmelCase     							=   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     							=   old["decoder/logits_dense/kernel"].T

     return new
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      bool       )		->       List[str]:


     '''simple docstring'''



     _UpperCAmelCase     							=   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     							=   state_dict["shared.weight"]

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

     return state_dict
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      List[str]			,     __lowercase							:      List[Any]			,     __lowercase							:      Tuple			,     __lowercase							:      List[str]       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase     							=   convert_tax_to_pytorch(
         SCREAMING_SNAKE_CASE__			,     num_layers=config.num_layers			,     is_encoder_only=SCREAMING_SNAKE_CASE__			,     scalable_attention=SCREAMING_SNAKE_CASE__       )
     _UpperCAmelCase     							=   make_state_dict(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__       )
     model.load_state_dict(SCREAMING_SNAKE_CASE__			,     strict=SCREAMING_SNAKE_CASE__       )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      List[Any]			,     __lowercase							:      int			,     __lowercase							:      bool = False			,     __lowercase							:      bool = False			,     )		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   MTaConfig.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:
          _UpperCAmelCase     							=   UMTaEncoderModel(SCREAMING_SNAKE_CASE__       )
     else:
          _UpperCAmelCase     							=   UMTaForConditionalGeneration(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__			,     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__":
  __SCREAMING_SNAKE_CASE     :Dict       =				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,
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				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,
  )








from __future__ import annotations

import math






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_    )  ->       None:
         __UpperCamelCase    							=size
         # approximate the overall size of segment tree with given value
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         # create array to store lazy update
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]  # flag for lazy update






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2 + 1






    def _a					(			self					,   A_					,   A_					,   A_					,   A_    )  ->       None:
         if left_element == right_element:
              __UpperCamelCase    							=a[left_element - 1]
         else:
              __UpperCamelCase    							=(left_element + right_element) // 2
              self.build(self.left(A_    )					,   A_					,   A_					,   A_    )
              self.build(self.right(A_    )					,   mid + 1					,   A_					,   A_    )
              __UpperCamelCase    							=max(
                  self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       bool:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True

         if right_element < a or left_element > b:
              return True
         if left_element >= a and right_element <= b:
              __UpperCamelCase    							=val
              if left_element != right_element:
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
              return True
         __UpperCamelCase    							=(left_element + right_element) // 2
         self.update(self.left(A_    )					,   A_					,   A_					,   A_					,   A_					,   A_    )
         self.update(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=max(
             self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )
         return True






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int | float:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
         if right_element < a or left_element > b:
              return -math.inf
         if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
         __UpperCamelCase    							=(left_element + right_element) // 2
         __UpperCamelCase    							=self.query(self.left(A_    )					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=self.query(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_    )
         return max(A_					,   A_    )






    def __str__(			self    )  ->       str:
         return str([self.query(1					,   1					,   self.size					,   A_					,   A_    ) for i in range(1					,   self.size + 1    )]    )


if __name__ == "__main__":
   _A		       =						[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   _A		       =						15
   _A		       =						SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 11))
   print(segt.query(1, 1, size, 7, 12))
   segt.update(1, 1, size, 1, 3, 111)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 235)
   print(segt)





