



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        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     Tuple:
					def get_masked_lm_array(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Dict						=				F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
										SCREAMING_SNAKE_CASE							:     List[Any]						=				tf.train.load_variable(__lowerCAmelCase      ,	__lowerCAmelCase   )

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

										return torch.from_numpy(__lowerCAmelCase   )

					def get_encoder_array(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     List[Any]						=				F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
										SCREAMING_SNAKE_CASE							:     str						=				tf.train.load_variable(__lowerCAmelCase      ,	__lowerCAmelCase   )

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

										return torch.from_numpy(__lowerCAmelCase   )

					def get_encoder_layer_array(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     List[str]						=				F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
										SCREAMING_SNAKE_CASE							:     Any						=				tf.train.load_variable(__lowerCAmelCase      ,	__lowerCAmelCase   )

										if "kernel" in name:
															SCREAMING_SNAKE_CASE							:     List[str]						=				array.transpose()

										return torch.from_numpy(__lowerCAmelCase   )

					def get_encoder_attention_layer_array(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
										SCREAMING_SNAKE_CASE							:     List[Any]						=				tf.train.load_variable(__lowerCAmelCase      ,	__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     Tuple						=				array.reshape(__lowerCAmelCase   )

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

										return torch.from_numpy(__lowerCAmelCase   )

					print(F'''Loading model based on config from {config_path}...'''   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				BertConfig.from_json_file(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Any						=				BertForMaskedLM(__lowerCAmelCase   )

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

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

										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_query_dense/kernel'      ,	self_attn.query.weight.data.shape   )
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_query_dense/bias'      ,	self_attn.query.bias.data.shape   )
										SCREAMING_SNAKE_CASE							:     Any						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_key_dense/kernel'      ,	self_attn.key.weight.data.shape   )
										SCREAMING_SNAKE_CASE							:     int						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_key_dense/bias'      ,	self_attn.key.bias.data.shape   )
										SCREAMING_SNAKE_CASE							:     str						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_value_dense/kernel'      ,	self_attn.value.weight.data.shape   )
										SCREAMING_SNAKE_CASE							:     Optional[int]						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_value_dense/bias'      ,	self_attn.value.bias.data.shape   )

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

										SCREAMING_SNAKE_CASE							:     int						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_output_dense/kernel'      ,	self_output.dense.weight.data.shape   )
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_encoder_attention_layer_array(
										    __lowerCAmelCase      ,	'_output_dense/bias'      ,	self_output.dense.bias.data.shape   )

										SCREAMING_SNAKE_CASE							:     List[Any]						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_attention_layer_norm/gamma'   )
										SCREAMING_SNAKE_CASE							:     str						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_attention_layer_norm/beta'   )

										# Intermediate
										SCREAMING_SNAKE_CASE							:     BertIntermediate						=				layer.intermediate

										SCREAMING_SNAKE_CASE							:     Any						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_intermediate_dense/kernel'   )
										SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_intermediate_dense/bias'   )

										# Output
										SCREAMING_SNAKE_CASE							:     BertOutput						=				layer.output

										SCREAMING_SNAKE_CASE							:     Tuple						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_output_dense/kernel'   )
										SCREAMING_SNAKE_CASE							:     Dict						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_output_dense/bias'   )

										SCREAMING_SNAKE_CASE							:     Dict						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_output_layer_norm/gamma'   )
										SCREAMING_SNAKE_CASE							:     List[Any]						=				get_encoder_layer_array(__lowerCAmelCase      ,	'_output_layer_norm/beta'   )

					# Embeddings
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				get_encoder_array('_position_embedding_layer/embeddings'   )
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				get_encoder_array('_type_embedding_layer/embeddings'   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_encoder_array('_embedding_norm_layer/gamma'   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				get_encoder_array('_embedding_norm_layer/beta'   )

					# LM Head
					SCREAMING_SNAKE_CASE							:     Optional[int]						=				model.cls.predictions.transform

					SCREAMING_SNAKE_CASE							:     List[Any]						=				get_masked_lm_array('dense/kernel'   )
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				get_masked_lm_array('dense/bias'   )

					SCREAMING_SNAKE_CASE							:     List[Any]						=				get_masked_lm_array('layer_norm/gamma'   )
					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				get_masked_lm_array('layer_norm/beta'   )

					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				get_masked_lm_array('embedding_table'   )

					# Pooling
					SCREAMING_SNAKE_CASE							:     Any						=				BertPooler(config=__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     BertPooler						=				get_encoder_array('_pooler_layer/kernel'   )
					SCREAMING_SNAKE_CASE							:     BertPooler						=				get_encoder_array('_pooler_layer/bias'   )

					# Export final model
					model.save_pretrained(__lowerCAmelCase   )

					# Integration test - should load without any errors ;)
					SCREAMING_SNAKE_CASE							:     List[Any]						=				BertForMaskedLM.from_pretrained(__lowerCAmelCase   )
					print(new_model.eval()   )

					print('Model conversion was done sucessfully!'   )


if __name__ == "__main__":
			_lowerCamelCase    :		Dict		      =    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.""",
			)
			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()
			convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()