
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__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    str							):




			"""simple docstring"""


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

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

						return torch.from_numpy(__lowerCAmelCase							)

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

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

						return torch.from_numpy(__lowerCAmelCase							)

			def get_encoder_layer_array(__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    str							):
						lowerCAmelCase_         =      F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						lowerCAmelCase_         =      tf.train.load_variable(__lowerCAmelCase       ,				__lowerCAmelCase							)

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

						return torch.from_numpy(__lowerCAmelCase							)

			def get_encoder_attention_layer_array(__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Tuple							):
						lowerCAmelCase_         =      F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						lowerCAmelCase_         =      tf.train.load_variable(__lowerCAmelCase       ,				__lowerCAmelCase							)
						lowerCAmelCase_         =      array.reshape(__lowerCAmelCase							)

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

						return torch.from_numpy(__lowerCAmelCase							)

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

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

						# Self-attention
						lowerCAmelCase_         =      layer.attention.self

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

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

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

						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_attention_layer_norm/gamma"							)
						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_attention_layer_norm/beta"							)

						# Intermediate
						lowerCAmelCase_         =      layer.intermediate

						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_intermediate_dense/kernel"							)
						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_intermediate_dense/bias"							)

						# Output
						lowerCAmelCase_         =      layer.output

						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_output_dense/kernel"							)
						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_output_dense/bias"							)

						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_output_layer_norm/gamma"							)
						lowerCAmelCase_         =      get_encoder_layer_array(__lowerCAmelCase       ,				"_output_layer_norm/beta"							)

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

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

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

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

			lowerCAmelCase_         =      get_masked_lm_array("embedding_table"							)

			# Pooling
			lowerCAmelCase_         =      BertPooler(config=__lowerCAmelCase							)
			lowerCAmelCase_         =      get_encoder_array("_pooler_layer/kernel"							)
			lowerCAmelCase_         =      get_encoder_array("_pooler_layer/bias"							)

			# Export final model
			model.save_pretrained(__lowerCAmelCase							)

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

			print("Model conversion was done sucessfully!"							)


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





from functools import reduce

_A  =	(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)






def   lowerCamelCase__							(							__lowerCAmelCase    :    str = N							):




			"""simple docstring"""


			return max(
			    # mypy cannot properly interpret reduce
			    int(reduce(lambda __lowerCAmelCase       ,				__lowerCAmelCase							: str(int(__lowerCAmelCase							) * int(__lowerCAmelCase							)							)       ,				n[i : i + 13]							)							)
			    for i in range(len(__lowerCAmelCase							) - 12							)							)


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



