

'''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()
UpperCAmelCase       =     logging.get_logger(__name__)

def  __UpperCamelCase							(		lowercase__  :     Tuple,    lowercase__  :     Union[str, Any],    lowercase__  :     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  __UpperCamelCase							(		lowercase__  :     Any,    lowercase__  :     int,    lowercase__  :     Tuple       ):






     '''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__":
       UpperCAmelCase       =     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).''',
       )
       UpperCAmelCase       =     parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''
import math




class 			lowerCamelCase_   :






      def lowercase_							(					self				:    Optional[Any]						,						_A				:    list[list[float]]						,						_A				:    list[int] ):


          '''simple docstring'''


          UpperCAmelCase__			:		List[str]													=				0.0
          UpperCAmelCase__			:		Union[str, Any]													=				0.0
          for i in range(len(_A ) ):
              da += math.pow((sample[i] - weights[0][i])						,						2 )
              da += math.pow((sample[i] - weights[1][i])						,						2 )
              return 0 if da > da else 1
          return 0






      def lowercase_							(					self				:    Dict						,						_A				:    list[list[int | float]]						,						_A				:    list[int]						,						_A				:    int						,						_A				:    float ):


          '''simple docstring'''


          for i in range(len(_A ) ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
          return weights





def 					a__       (					)    ->	None:
    # Training Examples ( m, n )
    UpperCAmelCase__			:		Optional[Any]													=				[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    UpperCAmelCase__			:		str													=				[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    UpperCAmelCase__			:		Optional[Any]													=				SelfOrganizingMap()
    UpperCAmelCase__			:		Tuple													=				3
    UpperCAmelCase__			:		Dict													=				0.5

    for _ in range(lowerCAmelCase__						):
        for j in range(len(lowerCAmelCase__						)						):
            # training sample
            UpperCAmelCase__			:		Optional[int]													=				training_samples[j]

            # Compute the winning vector
            UpperCAmelCase__			:		str													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

            # Update the winning vector
            UpperCAmelCase__			:		int													=				self_organizing_map.update(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

    # classify test sample
    UpperCAmelCase__			:		str													=				[0, 0, 0, 1]
    UpperCAmelCase__			:		int													=				self_organizing_map.get_winner(lowerCAmelCase__  ,					lowerCAmelCase__						)

    # results
    print(F"""Clusters that the test sample belongs to : {winner}"""						)
    print(F"""Weights that have been trained : {weights}"""						)


# running the main() function
if __name__ == "__main__":
  main()
