

"""simple docstring"""





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()
_a       =							logging.get_logger(__name__)




def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       int,			UpperCamelCase_     :       Dict)	->						List[Any]:
   '''simple docstring'''


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

   # Sanity check
   if len(set(lowerCAmelCase__)) != 1:
      raise ValueError(F"""Found layer names with different depths (layer depth {list(set(lowerCAmelCase__))})""")
   __lowercase								=						list(set(lowerCAmelCase__))[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(lowerCAmelCase__,			lowerCAmelCase__):
      __lowercase								=						full_name.split("/")
      __lowercase								=						model
      __lowercase								=						[]
      for i, m_name in enumerate(lowerCAmelCase__):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith("layer_with_weights"):
            __lowercase								=						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"])
               __lowercase								=						getattr(lowerCAmelCase__,			"embeddings")
               __lowercase								=						getattr(lowerCAmelCase__,			"LayerNorm")
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(["encoder", "layer", str(layer_num - 4)])
               __lowercase								=						getattr(lowerCAmelCase__,			"encoder")
               __lowercase								=						getattr(lowerCAmelCase__,			"layer")
               __lowercase								=						pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(["pooler", "dense"])
               __lowercase								=						getattr(lowerCAmelCase__,			"pooler")
               __lowercase								=						getattr(lowerCAmelCase__,			"dense")
         elif m_name == "embeddings":
            trace.append("embeddings")
            __lowercase								=						getattr(lowerCAmelCase__,			"embeddings")
            if layer_num == 0:
               trace.append("word_embeddings")
               __lowercase								=						getattr(lowerCAmelCase__,			"word_embeddings")
            elif layer_num == 1:
               trace.append("position_embeddings")
               __lowercase								=						getattr(lowerCAmelCase__,			"position_embeddings")
            elif layer_num == 2:
               trace.append("token_type_embeddings")
               __lowercase								=						getattr(lowerCAmelCase__,			"token_type_embeddings")
            else:
               raise ValueError(F"""Unknown embedding layer with name {full_name}""")
            trace.append("weight")
            __lowercase								=						getattr(lowerCAmelCase__,			"weight")
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(["attention", "self"])
            __lowercase								=						getattr(lowerCAmelCase__,			"attention")
            __lowercase								=						getattr(lowerCAmelCase__,			"self")
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(["attention", "output", "LayerNorm"])
            __lowercase								=						getattr(lowerCAmelCase__,			"attention")
            __lowercase								=						getattr(lowerCAmelCase__,			"output")
            __lowercase								=						getattr(lowerCAmelCase__,			"LayerNorm")
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(["attention", "output", "dense"])
            __lowercase								=						getattr(lowerCAmelCase__,			"attention")
            __lowercase								=						getattr(lowerCAmelCase__,			"output")
            __lowercase								=						getattr(lowerCAmelCase__,			"dense")
         elif m_name == "_output_dense":
            # output dense
            trace.extend(["output", "dense"])
            __lowercase								=						getattr(lowerCAmelCase__,			"output")
            __lowercase								=						getattr(lowerCAmelCase__,			"dense")
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(["output", "LayerNorm"])
            __lowercase								=						getattr(lowerCAmelCase__,			"output")
            __lowercase								=						getattr(lowerCAmelCase__,			"LayerNorm")
         elif m_name == "_key_dense":
            # attention key
            trace.append("key")
            __lowercase								=						getattr(lowerCAmelCase__,			"key")
         elif m_name == "_query_dense":
            # attention query
            trace.append("query")
            __lowercase								=						getattr(lowerCAmelCase__,			"query")
         elif m_name == "_value_dense":
            # attention value
            trace.append("value")
            __lowercase								=						getattr(lowerCAmelCase__,			"value")
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(["intermediate", "dense"])
            __lowercase								=						getattr(lowerCAmelCase__,			"intermediate")
            __lowercase								=						getattr(lowerCAmelCase__,			"dense")
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append("output")
            __lowercase								=						getattr(lowerCAmelCase__,			"output")
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append("bias")
            __lowercase								=						getattr(lowerCAmelCase__,			"bias")
         elif m_name in ["kernel", "gamma"]:
            trace.append("weight")
            __lowercase								=						getattr(lowerCAmelCase__,			"weight")
         else:
            logger.warning(F"""Ignored {m_name}""")
                # for certain layers reshape is necessary
      __lowercase								=						'''.'''.join(lowerCAmelCase__)
      if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)",			lowerCAmelCase__) or re.match(
          r"(\S+)\.attention\.output\.dense\.weight",			lowerCAmelCase__):
         __lowercase								=						array.reshape(pointer.data.shape)
      if "kernel" in full_name:
         __lowercase								=						array.transpose()
      if pointer.shape == array.shape:
         __lowercase								=						torch.from_numpy(lowerCAmelCase__)
      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  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       Optional[Any])	->						Optional[int]:
   '''simple docstring'''


   logger.info(F"""Loading model based on config from {config_path}...""")
   __lowercase								=						BertConfig.from_json_file(lowerCAmelCase__)
   __lowercase								=						BertModel(lowerCAmelCase__)

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

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


if __name__ == "__main__":
   _a       =							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).',
   )
   _a       =							parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






from __future__ import annotations




def    __UpperCamelCase     (				lowerCAmelCase__							:				list[float]  ,   lowerCAmelCase__							:				list[float]  ):
 __a :  Dict						=  sorted(numsa + numsa  )
 __a				,       __a :  Optional[Any]						=  divmod(len(lowerCAmelCase__  )  ,   2  )
 if mod == 1:
  return all_numbers[div]
 else:
  return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__       =[float(x) for x in input('Enter the elements of first array: ').split()]
      lowercase__       =[float(x) for x in input('Enter the elements of second array: ').split()]
      print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
