


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()
snake_case				:    int       				=     logging.get_logger(__name__)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





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

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




      """simple docstring"""





      logger.info(F'''Loading model based on config from {config_path}...'''						)
      a						:Dict 							=       BertConfig.from_json_file(UpperCAmelCase_						)
      a						:Optional[int] 							=       BertModel(UpperCAmelCase_						)

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

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


if __name__ == "__main__":
     snake_case				:    List[Any]       				=     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).''',
     )
     snake_case				:    Union[str, Any]       				=     parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












import random




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			Optional[int]      )      ->	tuple:
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      [], [], []
    for element in data:
        if element < pivot:
            less.append(__UpperCAmelCase      )
        elif element > pivot:
            greater.append(__UpperCAmelCase      )
        else:
            equal.append(__UpperCAmelCase      )
    return less, equal, greater





def 		lowerCAmelCase_    (  __UpperCAmelCase:			list		,   __UpperCAmelCase:			int      )      ->	List[str]:
    # index = len(items) // 2 when trying to find the median
    #   (value of index when items is sorted)

    # invalid input
    if index >= len(__UpperCAmelCase      ) or index < 0:
        return None

    UpperCamelCase__							:				List[str]          =      items[random.randint(0		,   len(__UpperCAmelCase      ) - 1      )]
    UpperCamelCase__							:				List[Any]          =      0
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				int          =      _partition(__UpperCAmelCase		,   __UpperCAmelCase      )
    UpperCamelCase__							:				Union[str, Any]          =      len(__UpperCAmelCase      )
    UpperCamelCase__							:				Dict          =      len(__UpperCAmelCase      )

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(__UpperCAmelCase		,   __UpperCAmelCase      )
    # must be in larger
    else:
        return quick_select(__UpperCAmelCase		,   index - (m + count)      )

