




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							(  __UpperCAmelCase		:						str   ,		__UpperCAmelCase		:						str   ,		__UpperCAmelCase		:						str      ) ->					Dict:
							"""simple docstring"""




							def get_masked_lm_array(__UpperCAmelCase		:						str      ):
														lowerCamelCase_			:    int   						=      f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
														lowerCamelCase_			:    Optional[Any]   						=      tf.train.load_variable(UpperCamelCase__   ,		UpperCamelCase__      )

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

														return torch.from_numpy(UpperCamelCase__      )

							def get_encoder_array(__UpperCAmelCase		:						str      ):
														lowerCamelCase_			:    Dict   						=      f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
														lowerCamelCase_			:    List[str]   						=      tf.train.load_variable(UpperCamelCase__   ,		UpperCamelCase__      )

														if "kernel" in name:
																					lowerCamelCase_			:    List[Any]   						=      array.transpose()

														return torch.from_numpy(UpperCamelCase__      )

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

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

														return torch.from_numpy(UpperCamelCase__      )

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

														if "kernel" in name:
																					lowerCamelCase_			:    List[Any]   						=      array.transpose()

														return torch.from_numpy(UpperCamelCase__      )

							print(f"Loading model based on config from {config_path}..."      )
							lowerCamelCase_			:    Optional[Any]   						=      BertConfig.from_json_file(UpperCamelCase__      )
							lowerCamelCase_			:    Dict   						=      BertForMaskedLM(UpperCamelCase__      )

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

														# Self-attention
														lowerCamelCase_			:    str   						=      layer.attention.self

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

														# Self-attention Output
														lowerCamelCase_			:    List[str]   						=      layer.attention.output

														lowerCamelCase_			:    Any   						=      get_encoder_attention_layer_array(
														    UpperCamelCase__   ,		"_output_dense/kernel"   ,		self_output.dense.weight.data.shape      )
														lowerCamelCase_			:    Tuple   						=      get_encoder_attention_layer_array(
														    UpperCamelCase__   ,		"_output_dense/bias"   ,		self_output.dense.bias.data.shape      )

														lowerCamelCase_			:    str   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_attention_layer_norm/gamma"      )
														lowerCamelCase_			:    Optional[Any]   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_attention_layer_norm/beta"      )

														# Intermediate
														lowerCamelCase_			:    Any   						=      layer.intermediate

														lowerCamelCase_			:    int   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_intermediate_dense/kernel"      )
														lowerCamelCase_			:    Optional[int]   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_intermediate_dense/bias"      )

														# Output
														lowerCamelCase_			:    Optional[int]   						=      layer.output

														lowerCamelCase_			:    List[Any]   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_output_dense/kernel"      )
														lowerCamelCase_			:    List[str]   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_output_dense/bias"      )

														lowerCamelCase_			:    Optional[int]   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_output_layer_norm/gamma"      )
														lowerCamelCase_			:    Union[str, Any]   						=      get_encoder_layer_array(UpperCamelCase__   ,		"_output_layer_norm/beta"      )

							# Embeddings
							lowerCamelCase_			:    List[str]   						=      get_encoder_array("_position_embedding_layer/embeddings"      )
							lowerCamelCase_			:    Dict   						=      get_encoder_array("_type_embedding_layer/embeddings"      )
							lowerCamelCase_			:    Any   						=      get_encoder_array("_embedding_norm_layer/gamma"      )
							lowerCamelCase_			:    Dict   						=      get_encoder_array("_embedding_norm_layer/beta"      )

							# LM Head
							lowerCamelCase_			:    int   						=      model.cls.predictions.transform

							lowerCamelCase_			:    Tuple   						=      get_masked_lm_array("dense/kernel"      )
							lowerCamelCase_			:    Any   						=      get_masked_lm_array("dense/bias"      )

							lowerCamelCase_			:    str   						=      get_masked_lm_array("layer_norm/gamma"      )
							lowerCamelCase_			:    Dict   						=      get_masked_lm_array("layer_norm/beta"      )

							lowerCamelCase_			:    List[str]   						=      get_masked_lm_array("embedding_table"      )

							# Pooling
							lowerCamelCase_			:    Dict   						=      BertPooler(config=UpperCamelCase__      )
							lowerCamelCase_			:    Dict   						=      get_encoder_array("_pooler_layer/kernel"      )
							lowerCamelCase_			:    Dict   						=      get_encoder_array("_pooler_layer/bias"      )

							# Export final model
							model.save_pretrained(UpperCamelCase__      )

							# Integration test - should load without any errors ;)
							lowerCamelCase_			:    Optional[int]   						=      BertForMaskedLM.from_pretrained(UpperCamelCase__      )
							print(new_model.eval()      )

							print("Model conversion was done sucessfully!"      )


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


'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def    lowerCAmelCase       (		UpperCamelCase__					: dict							):


	"""simple docstring"""
	return (data["data"], data["target"])





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      xgboost(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	# Error printing
	print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)
	print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()
