



"""simple docstring"""






import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 			snake_case_    ( A_   :						str,							A_   :						str,							A_   :						str		):


       '''simple docstring'''





       def get_masked_lm_array(A_   :						str		):
              _lowerCamelCase		:							List[Any]            =					F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _lowerCamelCase		:							List[Any]            =					tf.train.load_variable(__lowerCAmelCase,							__lowerCAmelCase		)

              if "kernel" in name:
                     _lowerCamelCase		:							Tuple            =					array.transpose()

              return torch.from_numpy(__lowerCAmelCase		)

       def get_encoder_array(A_   :						str		):
              _lowerCamelCase		:							int            =					F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _lowerCamelCase		:							List[Any]            =					tf.train.load_variable(__lowerCAmelCase,							__lowerCAmelCase		)

              if "kernel" in name:
                     _lowerCamelCase		:							Any            =					array.transpose()

              return torch.from_numpy(__lowerCAmelCase		)

       def get_encoder_layer_array(A_   :						int,							A_   :						str		):
              _lowerCamelCase		:							List[Any]            =					F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _lowerCamelCase		:							str            =					tf.train.load_variable(__lowerCAmelCase,							__lowerCAmelCase		)

              if "kernel" in name:
                     _lowerCamelCase		:							List[Any]            =					array.transpose()

              return torch.from_numpy(__lowerCAmelCase		)

       def get_encoder_attention_layer_array(A_   :						int,							A_   :						str,							A_   :						Tuple		):
              _lowerCamelCase		:							List[str]            =					F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _lowerCamelCase		:							Union[str, Any]            =					tf.train.load_variable(__lowerCAmelCase,							__lowerCAmelCase		)
              _lowerCamelCase		:							Any            =					array.reshape(__lowerCAmelCase		)

              if "kernel" in name:
                     _lowerCamelCase		:							Union[str, Any]            =					array.transpose()

              return torch.from_numpy(__lowerCAmelCase		)

       print(F'''Loading model based on config from {config_path}...'''		)
       _lowerCamelCase		:							Any            =					BertConfig.from_json_file(__lowerCAmelCase		)
       _lowerCamelCase		:							Optional[int]            =					BertForMaskedLM(__lowerCAmelCase		)

       # Layers
       for layer_index in range(0,							config.num_hidden_layers		):
              _lowerCamelCase		:							Any            =					model.bert.encoder.layer[layer_index]

              # Self-attention
              _lowerCamelCase		:							Tuple            =					layer.attention.self

              _lowerCamelCase		:							Any            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_query_dense/kernel''',							self_attn.query.weight.data.shape		)
              _lowerCamelCase		:							Optional[Any]            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_query_dense/bias''',							self_attn.query.bias.data.shape		)
              _lowerCamelCase		:							Dict            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_key_dense/kernel''',							self_attn.key.weight.data.shape		)
              _lowerCamelCase		:							Any            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_key_dense/bias''',							self_attn.key.bias.data.shape		)
              _lowerCamelCase		:							Union[str, Any]            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_value_dense/kernel''',							self_attn.value.weight.data.shape		)
              _lowerCamelCase		:							List[str]            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_value_dense/bias''',							self_attn.value.bias.data.shape		)

              # Self-attention Output
              _lowerCamelCase		:							str            =					layer.attention.output

              _lowerCamelCase		:							List[str]            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_output_dense/kernel''',							self_output.dense.weight.data.shape		)
              _lowerCamelCase		:							Tuple            =					get_encoder_attention_layer_array(
                  __lowerCAmelCase,							'''_output_dense/bias''',							self_output.dense.bias.data.shape		)

              _lowerCamelCase		:							int            =					get_encoder_layer_array(__lowerCAmelCase,							'''_attention_layer_norm/gamma'''		)
              _lowerCamelCase		:							Optional[int]            =					get_encoder_layer_array(__lowerCAmelCase,							'''_attention_layer_norm/beta'''		)

              # Intermediate
              _lowerCamelCase		:							Dict            =					layer.intermediate

              _lowerCamelCase		:							str            =					get_encoder_layer_array(__lowerCAmelCase,							'''_intermediate_dense/kernel'''		)
              _lowerCamelCase		:							Optional[Any]            =					get_encoder_layer_array(__lowerCAmelCase,							'''_intermediate_dense/bias'''		)

              # Output
              _lowerCamelCase		:							Any            =					layer.output

              _lowerCamelCase		:							int            =					get_encoder_layer_array(__lowerCAmelCase,							'''_output_dense/kernel'''		)
              _lowerCamelCase		:							Union[str, Any]            =					get_encoder_layer_array(__lowerCAmelCase,							'''_output_dense/bias'''		)

              _lowerCamelCase		:							int            =					get_encoder_layer_array(__lowerCAmelCase,							'''_output_layer_norm/gamma'''		)
              _lowerCamelCase		:							str            =					get_encoder_layer_array(__lowerCAmelCase,							'''_output_layer_norm/beta'''		)

       # Embeddings
       _lowerCamelCase		:							int            =					get_encoder_array('''_position_embedding_layer/embeddings'''		)
       _lowerCamelCase		:							Dict            =					get_encoder_array('''_type_embedding_layer/embeddings'''		)
       _lowerCamelCase		:							List[Any]            =					get_encoder_array('''_embedding_norm_layer/gamma'''		)
       _lowerCamelCase		:							Union[str, Any]            =					get_encoder_array('''_embedding_norm_layer/beta'''		)

       # LM Head
       _lowerCamelCase		:							List[str]            =					model.cls.predictions.transform

       _lowerCamelCase		:							Tuple            =					get_masked_lm_array('''dense/kernel'''		)
       _lowerCamelCase		:							str            =					get_masked_lm_array('''dense/bias'''		)

       _lowerCamelCase		:							int            =					get_masked_lm_array('''layer_norm/gamma'''		)
       _lowerCamelCase		:							Any            =					get_masked_lm_array('''layer_norm/beta'''		)

       _lowerCamelCase		:							Any            =					get_masked_lm_array('''embedding_table'''		)

       # Pooling
       _lowerCamelCase		:							Optional[Any]            =					BertPooler(config=__lowerCAmelCase		)
       _lowerCamelCase		:							Union[str, Any]            =					get_encoder_array('''_pooler_layer/kernel'''		)
       _lowerCamelCase		:							Union[str, Any]            =					get_encoder_array('''_pooler_layer/bias'''		)

       # Export final model
       model.save_pretrained(__lowerCAmelCase		)

       # Integration test - should load without any errors ;)
       _lowerCamelCase		:							Dict            =					BertForMaskedLM.from_pretrained(__lowerCAmelCase		)
       print(new_model.eval()		)

       print('''Model conversion was done sucessfully!'''		)


if __name__ == "__main__":
   lowerCAmelCase__              =							argparse.ArgumentParser()
   parser.add_argument(
       '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping 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.''',
   )
   lowerCAmelCase__              =							parser.parse_args()
   convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




from collections.abc import Callable






def   lowerCamelCase__							(							__lowerCAmelCase    :    Callable[[float], float]       ,				__lowerCAmelCase    :    float       ,				__lowerCAmelCase    :    float							):




			"""simple docstring"""


			lowerCAmelCase_         =      a
			lowerCAmelCase_         =      b
			if function(__lowerCAmelCase							) == 0:  # one of the a or b is a root for the function
						return a
			elif function(__lowerCAmelCase							) == 0:
						return b
			elif (
			    function(__lowerCAmelCase							) * function(__lowerCAmelCase							) > 0
			):  # if none of these are root and they are both positive or negative,
						# then this algorithm can't find the root
						raise ValueError("could not find root in given interval."							)
			else:
						lowerCAmelCase_         =      start + (end - start) / 2.0
						while abs(start - mid							) > 10**-7:  # until precisely equals to 10^-7
									if function(__lowerCAmelCase							) == 0:
												return mid
									elif function(__lowerCAmelCase							) * function(__lowerCAmelCase							) < 0:
												lowerCAmelCase_         =      mid
									else:
												lowerCAmelCase_         =      mid
									lowerCAmelCase_         =      start + (end - start) / 2.0
						return mid






def   lowerCamelCase__							(							__lowerCAmelCase    :    float							):




			"""simple docstring"""


			return x**3 - 2 * x - 5


if __name__ == "__main__":
			print(bisection(f, 1, 10_00))

			import doctest

			doctest.testmod()



