





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 lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  str       ,      snake_case_	:  str       )      ->				Dict:
   def get_masked_lm_array(snake_case_	:  str       ):
      __snake_case							       =   f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      __snake_case							       =   tf.train.load_variable(snake_case_       ,      snake_case_       )

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

      return torch.from_numpy(snake_case_       )

   def get_encoder_array(snake_case_	:  str       ):
      __snake_case							       =   f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      __snake_case							       =   tf.train.load_variable(snake_case_       ,      snake_case_       )

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

      return torch.from_numpy(snake_case_       )

   def get_encoder_layer_array(snake_case_	:  int       ,      snake_case_	:  str       ):
      __snake_case							       =   f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      __snake_case							       =   tf.train.load_variable(snake_case_       ,      snake_case_       )

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

      return torch.from_numpy(snake_case_       )

   def get_encoder_attention_layer_array(snake_case_	:  int       ,      snake_case_	:  str       ,      snake_case_	:  Any       ):
      __snake_case							       =   f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
      __snake_case							       =   tf.train.load_variable(snake_case_       ,      snake_case_       )
      __snake_case							       =   array.reshape(snake_case_       )

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

      return torch.from_numpy(snake_case_       )

   print(f"""Loading model based on config from {config_path}..."""       )
   __snake_case							       =   BertConfig.from_json_file(snake_case_       )
   __snake_case							       =   BertForMaskedLM(snake_case_       )

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

      # Self-attention
      __snake_case							       =   layer.attention.self

      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_query_dense/kernel'''       ,      self_attn.query.weight.data.shape       )
      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_query_dense/bias'''       ,      self_attn.query.bias.data.shape       )
      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_key_dense/kernel'''       ,      self_attn.key.weight.data.shape       )
      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_key_dense/bias'''       ,      self_attn.key.bias.data.shape       )
      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_value_dense/kernel'''       ,      self_attn.value.weight.data.shape       )
      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_value_dense/bias'''       ,      self_attn.value.bias.data.shape       )

      # Self-attention Output
      __snake_case							       =   layer.attention.output

      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_output_dense/kernel'''       ,      self_output.dense.weight.data.shape       )
      __snake_case							       =   get_encoder_attention_layer_array(
          snake_case_       ,      '''_output_dense/bias'''       ,      self_output.dense.bias.data.shape       )

      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_attention_layer_norm/gamma'''       )
      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_attention_layer_norm/beta'''       )

      # Intermediate
      __snake_case							       =   layer.intermediate

      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_intermediate_dense/kernel'''       )
      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_intermediate_dense/bias'''       )

      # Output
      __snake_case							       =   layer.output

      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_output_dense/kernel'''       )
      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_output_dense/bias'''       )

      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_output_layer_norm/gamma'''       )
      __snake_case							       =   get_encoder_layer_array(snake_case_       ,      '''_output_layer_norm/beta'''       )

   # Embeddings
   __snake_case							       =   get_encoder_array('''_position_embedding_layer/embeddings'''       )
   __snake_case							       =   get_encoder_array('''_type_embedding_layer/embeddings'''       )
   __snake_case							       =   get_encoder_array('''_embedding_norm_layer/gamma'''       )
   __snake_case							       =   get_encoder_array('''_embedding_norm_layer/beta'''       )

   # LM Head
   __snake_case							       =   model.cls.predictions.transform

   __snake_case							       =   get_masked_lm_array('''dense/kernel'''       )
   __snake_case							       =   get_masked_lm_array('''dense/bias'''       )

   __snake_case							       =   get_masked_lm_array('''layer_norm/gamma'''       )
   __snake_case							       =   get_masked_lm_array('''layer_norm/beta'''       )

   __snake_case							       =   get_masked_lm_array('''embedding_table'''       )

   # Pooling
   __snake_case							       =   BertPooler(config=snake_case_       )
   __snake_case							       =   get_encoder_array('''_pooler_layer/kernel'''       )
   __snake_case							       =   get_encoder_array('''_pooler_layer/bias'''       )

   # Export final model
   model.save_pretrained(snake_case_       )

   # Integration test - should load without any errors ;)
   __snake_case							       =   BertForMaskedLM.from_pretrained(snake_case_       )
   print(new_model.eval()       )

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


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








from math import pi






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  int       )      ->				float:
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
    print(arc_length(90, 10))

