





"""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       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


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

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

            return torch.from_numpy(_SCREAMING_SNAKE_CASE     )

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

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

            return torch.from_numpy(_SCREAMING_SNAKE_CASE     )

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

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

            return torch.from_numpy(_SCREAMING_SNAKE_CASE     )

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

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

            return torch.from_numpy(_SCREAMING_SNAKE_CASE     )

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

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

            # Self-attention
            SCREAMING_SNAKE_CASE		=						layer.attention.self

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

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

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

            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_attention_layer_norm/gamma"""     )
            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_attention_layer_norm/beta"""     )

            # Intermediate
            SCREAMING_SNAKE_CASE		=						layer.intermediate

            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_intermediate_dense/kernel"""     )
            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_intermediate_dense/bias"""     )

            # Output
            SCREAMING_SNAKE_CASE		=						layer.output

            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_output_dense/kernel"""     )
            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_output_dense/bias"""     )

            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_output_layer_norm/gamma"""     )
            SCREAMING_SNAKE_CASE		=						get_encoder_layer_array(_SCREAMING_SNAKE_CASE			,						"""_output_layer_norm/beta"""     )

      # Embeddings
      SCREAMING_SNAKE_CASE		=						get_encoder_array("""_position_embedding_layer/embeddings"""     )
      SCREAMING_SNAKE_CASE		=						get_encoder_array("""_type_embedding_layer/embeddings"""     )
      SCREAMING_SNAKE_CASE		=						get_encoder_array("""_embedding_norm_layer/gamma"""     )
      SCREAMING_SNAKE_CASE		=						get_encoder_array("""_embedding_norm_layer/beta"""     )

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

      SCREAMING_SNAKE_CASE		=						get_masked_lm_array("""dense/kernel"""     )
      SCREAMING_SNAKE_CASE		=						get_masked_lm_array("""dense/bias"""     )

      SCREAMING_SNAKE_CASE		=						get_masked_lm_array("""layer_norm/gamma"""     )
      SCREAMING_SNAKE_CASE		=						get_masked_lm_array("""layer_norm/beta"""     )

      SCREAMING_SNAKE_CASE		=						get_masked_lm_array("""embedding_table"""     )

      # Pooling
      SCREAMING_SNAKE_CASE		=						BertPooler(config=_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						get_encoder_array("""_pooler_layer/kernel"""     )
      SCREAMING_SNAKE_CASE		=						get_encoder_array("""_pooler_layer/bias"""     )

      # Export final model
      model.save_pretrained(_SCREAMING_SNAKE_CASE     )

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

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


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









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 50     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[1] * (length + 1)

      for row_length in range(3			,						length + 1     ):
            for block_length in range(3			,						row_length + 1     ):
                  for block_start in range(row_length - block_length     ):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
      print(F'''{solution() = }''')


