
"""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     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			str    )					->							Dict:
       def get_masked_lm_array(__snake_case							:			str    ):
              lowercase       :							Optional[int]									=				f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              lowercase       :							List[Any]									=				tf.train.load_variable(lowercase__	,					lowercase__    )

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

              return torch.from_numpy(lowercase__    )

       def get_encoder_array(__snake_case							:			str    ):
              lowercase       :							Dict									=				f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              lowercase       :							Union[str, Any]									=				tf.train.load_variable(lowercase__	,					lowercase__    )

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

              return torch.from_numpy(lowercase__    )

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

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

              return torch.from_numpy(lowercase__    )

       def get_encoder_attention_layer_array(__snake_case							:			int	,					__snake_case							:			str	,					__snake_case							:			Dict    ):
              lowercase       :							Union[str, Any]									=				f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
              lowercase       :							Union[str, Any]									=				tf.train.load_variable(lowercase__	,					lowercase__    )
              lowercase       :							List[str]									=				array.reshape(lowercase__    )

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

              return torch.from_numpy(lowercase__    )

       print(f"""Loading model based on config from {config_path}..."""    )
       lowercase       :							Optional[int]									=				BertConfig.from_json_file(lowercase__    )
       lowercase       :							str									=				BertForMaskedLM(lowercase__    )

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

              # Self-attention
              lowercase       :							Any									=				layer.attention.self

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

              # Self-attention Output
              lowercase       :							Any									=				layer.attention.output

              lowercase       :							Dict									=				get_encoder_attention_layer_array(
                  lowercase__	,					"_output_dense/kernel"	,					self_output.dense.weight.data.shape    )
              lowercase       :							List[str]									=				get_encoder_attention_layer_array(
                  lowercase__	,					"_output_dense/bias"	,					self_output.dense.bias.data.shape    )

              lowercase       :							Tuple									=				get_encoder_layer_array(lowercase__	,					"_attention_layer_norm/gamma"    )
              lowercase       :							str									=				get_encoder_layer_array(lowercase__	,					"_attention_layer_norm/beta"    )

              # Intermediate
              lowercase       :							List[str]									=				layer.intermediate

              lowercase       :							List[str]									=				get_encoder_layer_array(lowercase__	,					"_intermediate_dense/kernel"    )
              lowercase       :							str									=				get_encoder_layer_array(lowercase__	,					"_intermediate_dense/bias"    )

              # Output
              lowercase       :							List[str]									=				layer.output

              lowercase       :							Tuple									=				get_encoder_layer_array(lowercase__	,					"_output_dense/kernel"    )
              lowercase       :							Any									=				get_encoder_layer_array(lowercase__	,					"_output_dense/bias"    )

              lowercase       :							Optional[int]									=				get_encoder_layer_array(lowercase__	,					"_output_layer_norm/gamma"    )
              lowercase       :							Tuple									=				get_encoder_layer_array(lowercase__	,					"_output_layer_norm/beta"    )

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

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

       lowercase       :							Optional[int]									=				get_masked_lm_array("dense/kernel"    )
       lowercase       :							Dict									=				get_masked_lm_array("dense/bias"    )

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

       lowercase       :							Optional[int]									=				get_masked_lm_array("embedding_table"    )

       # Pooling
       lowercase       :							str									=				BertPooler(config=lowercase__    )
       lowercase       :							Any									=				get_encoder_array("_pooler_layer/kernel"    )
       lowercase       :							Dict									=				get_encoder_array("_pooler_layer/bias"    )

       # Export final model
       model.save_pretrained(lowercase__    )

       # Integration test - should load without any errors ;)
       lowercase       :							List[str]									=				BertForMaskedLM.from_pretrained(lowercase__    )
       print(new_model.eval()    )

       print("Model conversion was done sucessfully!"    )


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






"""simple docstring"""




from __future__ import annotations





def       a_ (	lowercase__ :list[float]     ):
			if len(lowercase__     ) < 2:
						raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""     )
			if any(i <= 0 for i in nums     ):
						raise ValueError("""All values must be greater than 0"""     )
			__lowerCamelCase          =      nums.copy()
			copy_nums.sort()
			return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




