


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     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str       ):






							"""simple docstring"""





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

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

														return torch.from_numpy(a__       )

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

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

														return torch.from_numpy(a__       )

							def get_encoder_layer_array(a__   :int   ,			a__   :str       ):
														UpperCamelCase__				     =			f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )

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

														return torch.from_numpy(a__       )

							def get_encoder_attention_layer_array(a__   :int   ,			a__   :str   ,			a__   :List[str]       ):
														UpperCamelCase__				     =			f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														UpperCamelCase__				     =			tf.train.load_variable(a__   ,			a__       )
														UpperCamelCase__				     =			array.reshape(a__       )

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

														return torch.from_numpy(a__       )

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

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

														# Self-attention
														UpperCamelCase__				     =			layer.attention.self

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

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

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

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_attention_layer_norm/gamma"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_attention_layer_norm/beta"""       )

														# Intermediate
														UpperCamelCase__				     =			layer.intermediate

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_intermediate_dense/kernel"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_intermediate_dense/bias"""       )

														# Output
														UpperCamelCase__				     =			layer.output

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_dense/kernel"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_dense/bias"""       )

														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_layer_norm/gamma"""       )
														UpperCamelCase__				     =			get_encoder_layer_array(a__   ,			"""_output_layer_norm/beta"""       )

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

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

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

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

							UpperCamelCase__				     =			get_masked_lm_array("""embedding_table"""       )

							# Pooling
							UpperCamelCase__				     =			BertPooler(config=a__       )
							UpperCamelCase__				     =			get_encoder_array("""_pooler_layer/kernel"""       )
							UpperCamelCase__				     =			get_encoder_array("""_pooler_layer/bias"""       )

							# Export final model
							model.save_pretrained(a__       )

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

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


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











from math import ceil, sqrt





def     _UpperCamelCase     (a__   :int = 100_0000       ):






							"""simple docstring"""





							UpperCamelCase__				     =			0

							for outer_width in range(3   ,			(limit // 4) + 2       ):
														if outer_width**2 > limit:
																					UpperCamelCase__				     =			max(ceil(sqrt(outer_width**2 - limit       )       )   ,			1       )
														else:
																					UpperCamelCase__				     =			1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


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




