'''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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Optional[Any]:





       """simple docstring"""


       def get_masked_lm_array(lowerCamelCase__						):
              __UpperCAmelCase  :				str       				=		f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              __UpperCAmelCase  :				Union[str, Any]       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)

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

              return torch.from_numpy(lowerCamelCase__						)

       def get_encoder_array(lowerCamelCase__						):
              __UpperCAmelCase  :				Any       				=		f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              __UpperCAmelCase  :				Tuple       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)

              if "kernel" in name:
                     __UpperCAmelCase  :				str       				=		array.transpose()

              return torch.from_numpy(lowerCamelCase__						)

       def get_encoder_layer_array(lowerCamelCase__							,    lowerCamelCase__						):
              __UpperCAmelCase  :				List[Any]       				=		f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              __UpperCAmelCase  :				Union[str, Any]       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)

              if "kernel" in name:
                     __UpperCAmelCase  :				str       				=		array.transpose()

              return torch.from_numpy(lowerCamelCase__						)

       def get_encoder_attention_layer_array(lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						):
              __UpperCAmelCase  :				str       				=		f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              __UpperCAmelCase  :				Optional[Any]       				=		tf.train.load_variable(lowerCamelCase__							,    lowerCamelCase__						)
              __UpperCAmelCase  :				Tuple       				=		array.reshape(lowerCamelCase__						)

              if "kernel" in name:
                     __UpperCAmelCase  :				int       				=		array.transpose()

              return torch.from_numpy(lowerCamelCase__						)

       print(f"""Loading model based on config from {config_path}..."""						)
       __UpperCAmelCase  :				Union[str, Any]       				=		BertConfig.from_json_file(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		BertForMaskedLM(lowerCamelCase__						)

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

              # Self-attention
              __UpperCAmelCase  :				BertSelfAttention       				=		layer.attention.self

              __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_query_dense/kernel"							,    self_attn.query.weight.data.shape						)
              __UpperCAmelCase  :				Dict       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_query_dense/bias"							,    self_attn.query.bias.data.shape						)
              __UpperCAmelCase  :				Tuple       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_key_dense/kernel"							,    self_attn.key.weight.data.shape						)
              __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_key_dense/bias"							,    self_attn.key.bias.data.shape						)
              __UpperCAmelCase  :				List[str]       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_value_dense/kernel"							,    self_attn.value.weight.data.shape						)
              __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_value_dense/bias"							,    self_attn.value.bias.data.shape						)

              # Self-attention Output
              __UpperCAmelCase  :				BertSelfOutput       				=		layer.attention.output

              __UpperCAmelCase  :				Any       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_output_dense/kernel"							,    self_output.dense.weight.data.shape						)
              __UpperCAmelCase  :				str       				=		get_encoder_attention_layer_array(
                  lowerCamelCase__							,    "_output_dense/bias"							,    self_output.dense.bias.data.shape						)

              __UpperCAmelCase  :				List[str]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_attention_layer_norm/gamma"						)
              __UpperCAmelCase  :				Tuple       				=		get_encoder_layer_array(lowerCamelCase__							,    "_attention_layer_norm/beta"						)

              # Intermediate
              __UpperCAmelCase  :				BertIntermediate       				=		layer.intermediate

              __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_intermediate_dense/kernel"						)
              __UpperCAmelCase  :				Any       				=		get_encoder_layer_array(lowerCamelCase__							,    "_intermediate_dense/bias"						)

              # Output
              __UpperCAmelCase  :				BertOutput       				=		layer.output

              __UpperCAmelCase  :				Union[str, Any]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_dense/kernel"						)
              __UpperCAmelCase  :				Optional[int]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_dense/bias"						)

              __UpperCAmelCase  :				List[str]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_layer_norm/gamma"						)
              __UpperCAmelCase  :				List[str]       				=		get_encoder_layer_array(lowerCamelCase__							,    "_output_layer_norm/beta"						)

       # Embeddings
       __UpperCAmelCase  :				int       				=		get_encoder_array("_position_embedding_layer/embeddings"						)
       __UpperCAmelCase  :				Optional[Any]       				=		get_encoder_array("_type_embedding_layer/embeddings"						)
       __UpperCAmelCase  :				Any       				=		get_encoder_array("_embedding_norm_layer/gamma"						)
       __UpperCAmelCase  :				List[str]       				=		get_encoder_array("_embedding_norm_layer/beta"						)

       # LM Head
       __UpperCAmelCase  :				List[Any]       				=		model.cls.predictions.transform

       __UpperCAmelCase  :				List[Any]       				=		get_masked_lm_array("dense/kernel"						)
       __UpperCAmelCase  :				Optional[Any]       				=		get_masked_lm_array("dense/bias"						)

       __UpperCAmelCase  :				Optional[int]       				=		get_masked_lm_array("layer_norm/gamma"						)
       __UpperCAmelCase  :				int       				=		get_masked_lm_array("layer_norm/beta"						)

       __UpperCAmelCase  :				List[str]       				=		get_masked_lm_array("embedding_table"						)

       # Pooling
       __UpperCAmelCase  :				Union[str, Any]       				=		BertPooler(config=lowerCamelCase__						)
       __UpperCAmelCase  :				BertPooler       				=		get_encoder_array("_pooler_layer/kernel"						)
       __UpperCAmelCase  :				BertPooler       				=		get_encoder_array("_pooler_layer/bias"						)

       # Export final model
       model.save_pretrained(lowerCamelCase__						)

       # Integration test - should load without any errors ;)
       __UpperCAmelCase  :				Optional[int]       				=		BertForMaskedLM.from_pretrained(lowerCamelCase__						)
       print(new_model.eval()						)

       print("Model conversion was done sucessfully!"						)


if __name__ == "__main__":
   _a   :	int 							=   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.",
   )
   _a   :	Optional[Any] 							=   parser.parse_args()
   convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''




class 		__A       :






       def __init__(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		set_counts
              __UpperCAmelCase  :				int       				=		max(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		len(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		[1] * num_sets
              __UpperCAmelCase  :				Any       				=		list(range(UpperCamelCase_      )      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Optional[int]       				=		self.get_parent(UpperCamelCase_      )
              __UpperCAmelCase  :				List[Any]       				=		self.get_parent(UpperCamelCase_      )

              if src_parent == dst_parent:
                     return False

              if self.ranks[dst_parent] >= self.ranks[src_parent]:
                     self.set_counts[dst_parent] += self.set_counts[src_parent]
                     __UpperCAmelCase  :				Optional[Any]       				=		0
                     __UpperCAmelCase  :				List[Any]       				=		dst_parent
                     if self.ranks[dst_parent] == self.ranks[src_parent]:
                            self.ranks[dst_parent] += 1
                     __UpperCAmelCase  :				Union[str, Any]       				=		self.set_counts[dst_parent]
              else:
                     self.set_counts[src_parent] += self.set_counts[dst_parent]
                     __UpperCAmelCase  :				Union[str, Any]       				=		0
                     __UpperCAmelCase  :				Dict       				=		src_parent
                     __UpperCAmelCase  :				Dict       				=		self.set_counts[src_parent]

              __UpperCAmelCase  :				Dict       				=		max(self.max_set				,     UpperCamelCase_      )
              return True







       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              if self.parents[disj_set] == disj_set:
                     return disj_set
              __UpperCAmelCase  :				str       				=		self.get_parent(self.parents[disj_set]      )
              return self.parents[disj_set]






