

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   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      def get_masked_lm_array(SCREAMING_SNAKE_CASE   ):
            A_							       =		f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            A_							       =		tf.train.load_variable(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )

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

            return torch.from_numpy(SCREAMING_SNAKE_CASE   )

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

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

            return torch.from_numpy(SCREAMING_SNAKE_CASE   )

      def get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
            A_							       =		f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            A_							       =		tf.train.load_variable(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )

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

            return torch.from_numpy(SCREAMING_SNAKE_CASE   )

      def get_encoder_attention_layer_array(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
            A_							       =		f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            A_							       =		tf.train.load_variable(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            A_							       =		array.reshape(SCREAMING_SNAKE_CASE   )

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

            return torch.from_numpy(SCREAMING_SNAKE_CASE   )

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

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

            # Self-attention
            A_							       =		layer.attention.self

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

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

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

            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_attention_layer_norm/gamma'''   )
            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_attention_layer_norm/beta'''   )

            # Intermediate
            A_							       =		layer.intermediate

            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_intermediate_dense/kernel'''   )
            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_intermediate_dense/bias'''   )

            # Output
            A_							       =		layer.output

            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_output_dense/kernel'''   )
            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_output_dense/bias'''   )

            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_output_layer_norm/gamma'''   )
            A_							       =		get_encoder_layer_array(SCREAMING_SNAKE_CASE		,				'''_output_layer_norm/beta'''   )

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

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

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

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

      A_							       =		get_masked_lm_array('''embedding_table'''   )

      # Pooling
      A_							       =		BertPooler(config=SCREAMING_SNAKE_CASE   )
      A_							       =		get_encoder_array('''_pooler_layer/kernel'''   )
      A_							       =		get_encoder_array('''_pooler_layer/bias'''   )

      # Export final model
      model.save_pretrained(SCREAMING_SNAKE_CASE   )

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

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


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








from math import factorial, radians







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 18		,				SCREAMING_SNAKE_CASE = 10   ):
      '''simple docstring'''







      A_							       =		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

      # Converting from degrees to radians
      A_							       =		radians(SCREAMING_SNAKE_CASE   )

      A_							       =		angle_in_radians
      A_							       =		3
      A_							       =		-1

      for _ in range(SCREAMING_SNAKE_CASE   ):
            result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE   )

            A_							       =		-b  # One positive term and the next will be negative and so on...
            a += 2  # Increased by 2 for every term.

      return round(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      __import__("""doctest""").testmod()





