



'''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 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    str				,   __lowerCamelCase	:    str	)  ->						Dict:
					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	:    List[str]	):
										__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__":
				lowercase_	       =			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.''',
				)
				lowercase_	       =			parser.parse_args()
				convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




