





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase	: str       =logging.get_logger(__name__)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =os.path.abspath(lowercase__	)
   logger.info(F'Converting TensorFlow checkpoint from {tf_path}'	)
   # Load weights from TF model
   UpperCAmelCase_           =tf.train.list_variables(lowercase__	)
   UpperCAmelCase_           =[]
   UpperCAmelCase_           =[]
   UpperCAmelCase_           =[]
   for full_name, shape in init_vars:
      # logger.info(f"Loading TF weight {name} with shape {shape}")
      UpperCAmelCase_           =full_name.split("/"	)
      if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
         logger.info(F'Skipping non-model layer {full_name}'	)
         continue
      if "optimizer" in full_name:
         logger.info(F'Skipping optimization layer {full_name}'	)
         continue
      if name[0] == "model":
         # ignore initial 'model'
         UpperCAmelCase_           =name[1:]
      # figure out how many levels deep the name is
      UpperCAmelCase_           =0
      for _name in name:
         if _name.startswith("layer_with_weights"	):
            depth += 1
         else:
            break
      layer_depth.append(lowercase__	)
      # read data
      UpperCAmelCase_           =tf.train.load_variable(lowercase__	,     lowercase__	)
      names.append("/".join(lowercase__	)	)
      arrays.append(lowercase__	)
   logger.info(F'Read a total of {len(lowercase__	):,} layers'	)

   # Sanity check
   if len(set(lowercase__	)	) != 1:
      raise ValueError(F'Found layer names with different depths (layer depth {list(set(lowercase__	)	)})'	)
   UpperCAmelCase_           =list(set(lowercase__	)	)[0]
   if layer_depth != 1:
      raise ValueError(
          "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
          " heads."	)

   # convert layers
   logger.info("Converting weights..."	)
   for full_name, array in zip(lowercase__	,     lowercase__	):
      UpperCAmelCase_           =full_name.split("/"	)
      UpperCAmelCase_           =model
      UpperCAmelCase_           =[]
      for i, m_name in enumerate(lowercase__	):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith("layer_with_weights"	):
            UpperCAmelCase_           =int(m_name.split("-"	)[-1]	)
            if layer_num <= 2:
               # embedding layers
               # layer_num 0: word_embeddings
               # layer_num 1: position_embeddings
               # layer_num 2: token_type_embeddings
               continue
            elif layer_num == 3:
               # embedding LayerNorm
               trace.extend(["embeddings", "LayerNorm"]	)
               UpperCAmelCase_           =getattr(lowercase__	,     "embeddings"	)
               UpperCAmelCase_           =getattr(lowercase__	,     "LayerNorm"	)
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(["encoder", "layer", str(layer_num - 4	)]	)
               UpperCAmelCase_           =getattr(lowercase__	,     "encoder"	)
               UpperCAmelCase_           =getattr(lowercase__	,     "layer"	)
               UpperCAmelCase_           =pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(["pooler", "dense"]	)
               UpperCAmelCase_           =getattr(lowercase__	,     "pooler"	)
               UpperCAmelCase_           =getattr(lowercase__	,     "dense"	)
         elif m_name == "embeddings":
            trace.append("embeddings"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "embeddings"	)
            if layer_num == 0:
               trace.append("word_embeddings"	)
               UpperCAmelCase_           =getattr(lowercase__	,     "word_embeddings"	)
            elif layer_num == 1:
               trace.append("position_embeddings"	)
               UpperCAmelCase_           =getattr(lowercase__	,     "position_embeddings"	)
            elif layer_num == 2:
               trace.append("token_type_embeddings"	)
               UpperCAmelCase_           =getattr(lowercase__	,     "token_type_embeddings"	)
            else:
               raise ValueError(F'Unknown embedding layer with name {full_name}'	)
            trace.append("weight"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "weight"	)
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(["attention", "self"]	)
            UpperCAmelCase_           =getattr(lowercase__	,     "attention"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "self"	)
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(["attention", "output", "LayerNorm"]	)
            UpperCAmelCase_           =getattr(lowercase__	,     "attention"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "output"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "LayerNorm"	)
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(["attention", "output", "dense"]	)
            UpperCAmelCase_           =getattr(lowercase__	,     "attention"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "output"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "dense"	)
         elif m_name == "_output_dense":
            # output dense
            trace.extend(["output", "dense"]	)
            UpperCAmelCase_           =getattr(lowercase__	,     "output"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "dense"	)
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(["output", "LayerNorm"]	)
            UpperCAmelCase_           =getattr(lowercase__	,     "output"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "LayerNorm"	)
         elif m_name == "_key_dense":
            # attention key
            trace.append("key"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "key"	)
         elif m_name == "_query_dense":
            # attention query
            trace.append("query"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "query"	)
         elif m_name == "_value_dense":
            # attention value
            trace.append("value"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "value"	)
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(["intermediate", "dense"]	)
            UpperCAmelCase_           =getattr(lowercase__	,     "intermediate"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "dense"	)
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append("output"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "output"	)
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append("bias"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "bias"	)
         elif m_name in ["kernel", "gamma"]:
            trace.append("weight"	)
            UpperCAmelCase_           =getattr(lowercase__	,     "weight"	)
         else:
            logger.warning(F'Ignored {m_name}'	)
        # for certain layers reshape is necessary
      UpperCAmelCase_           =".".join(lowercase__	)
      if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"	,     lowercase__	) or re.match(
          R"(\S+)\.attention\.output\.dense\.weight"	,     lowercase__	):
         UpperCAmelCase_           =array.reshape(pointer.data.shape	)
      if "kernel" in full_name:
         UpperCAmelCase_           =array.transpose()
      if pointer.shape == array.shape:
         UpperCAmelCase_           =torch.from_numpy(lowercase__	)
      else:
         raise ValueError(
             F'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
             F' {array.shape}'	)
      logger.info(F'Successfully set variable {full_name} to PyTorch layer {trace}'	)
   return model




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   logger.info(F'Loading model based on config from {config_path}...'	)
   UpperCAmelCase_           =BertConfig.from_json_file(lowercase__	)
   UpperCAmelCase_           =BertModel(lowercase__	)

   # Load weights from checkpoint
   logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...'	)
   load_tfa_weights_in_bert(lowercase__	,     lowercase__	,     lowercase__	)

   # Save pytorch-model
   logger.info(F'Saving PyTorch model to {pytorch_dump_path}...'	)
   torch.save(model.state_dict()	,     lowercase__	)


if __name__ == "__main__":
     __lowercase	: Tuple       =argparse.ArgumentParser()
     parser.add_argument(
         """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
     )
     parser.add_argument(
         """--bert_config_file""",
         type=str,
         required=True,
         help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""",
         type=str,
         required=True,
         help="""Path to the output PyTorch model (must include filename).""",
     )
     __lowercase	: str       =parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






"""simple docstring"""






from __future__ import annotations

from random import random

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase = None       )			->							str:




										'''simple docstring'''
										__lowercase       =       value
										__lowercase       =       random()
										__lowercase       =       None
										__lowercase       =       None
							def __repr__(     self       )			->							str:




										'''simple docstring'''
										from pprint import pformat

										if self.left is None and self.right is None:
													return f'''\'{self.value}: {self.prior:.5}\''''
										else:
													return pformat(
													    {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)}  ,    indent=1       )





							def __str__(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       str(self.value       ) + " "
										__lowercase       =       str(self.left or ""       )
										__lowercase       =       str(self.right or ""       )
										return value + left + right





def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->tuple[Node | None, Node | None]:
			if root is None:  # None tree is split into 2 Nones
						return None, None
			elif root.value is None:
						return None, None
			else:
						if value < root.value:
									__lowercase   ,					__lowercase       =       split(root.left			,   __magic_name__      )
									return left, root
						else:
									__lowercase   ,					__lowercase       =       split(root.right			,   __magic_name__      )
									return root, right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						__lowercase       =       merge(left.right			,   __magic_name__      )
						return left
			else:
						__lowercase       =       merge(__magic_name__			,   right.left      )
						return right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase       =       Node(__magic_name__      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(merge(__magic_name__			,   __magic_name__      )			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   value - 1      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(__magic_name__			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__      )						->None:
			if not root:  # None
						return
			else:
						inorder(root.left      )
						print(root.value			,   end=","      )
						inorder(root.right      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			for arg in args.split():
						if arg[0] == "+":
									__lowercase       =       insert(__magic_name__			,   int(arg[1:]      )      )

						elif arg[0] == "-":
									__lowercase       =       erase(__magic_name__			,   int(arg[1:]      )      )

						else:
									print("Unknown command"      )

			return root



def      lowerCAmelCase__			(		)						->None:
			__lowercase       =       None
			print(
			    "enter numbers to create a tree, + value to add value into treap, "
			    "- value to erase all nodes with value. 'q' to quit. "      )

			__lowercase       =       input()
			while args != "q":
						__lowercase       =       interact_treap(__magic_name__			,   __magic_name__      )
						print(__magic_name__      )
						__lowercase       =       input()

			print("good by!"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





