






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		(					lowercase__  :  int ,		lowercase__  :  str ,		lowercase__  :  int  )		-> Optional[Any]:





	def get_masked_lm_array(lowercase__  :  Union[str, Any]  ):
		UpperCamelCase__ :Tuple						=      f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		UpperCamelCase__ :Any						=      tf.train.load_variable(_A ,		_A  )

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

		return torch.from_numpy(_A  )

	def get_encoder_array(lowercase__  :  List[Any]  ):
		UpperCamelCase__ :Optional[Any]						=      f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		UpperCamelCase__ :Optional[Any]						=      tf.train.load_variable(_A ,		_A  )

		if "kernel" in name:
			UpperCamelCase__ :Optional[Any]						=      array.transpose()

		return torch.from_numpy(_A  )

	def get_encoder_layer_array(lowercase__  :  Tuple ,		lowercase__  :  Tuple  ):
		UpperCamelCase__ :str						=      f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
		UpperCamelCase__ :Optional[int]						=      tf.train.load_variable(_A ,		_A  )

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

		return torch.from_numpy(_A  )

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

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

		return torch.from_numpy(_A  )

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

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

		# Self-attention
		UpperCamelCase__ :Optional[int]						=      layer.attention.self

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

		# Self-attention Output
		UpperCamelCase__ :Optional[Any]						=      layer.attention.output

		UpperCamelCase__ :Optional[Any]						=      get_encoder_attention_layer_array(
		    _A ,		"""_output_dense/kernel""" ,		self_output.dense.weight.data.shape  )
		UpperCamelCase__ :Optional[int]						=      get_encoder_attention_layer_array(
		    _A ,		"""_output_dense/bias""" ,		self_output.dense.bias.data.shape  )

		UpperCamelCase__ :int						=      get_encoder_layer_array(_A ,		"""_attention_layer_norm/gamma"""  )
		UpperCamelCase__ :Optional[int]						=      get_encoder_layer_array(_A ,		"""_attention_layer_norm/beta"""  )

		# Intermediate
		UpperCamelCase__ :Dict						=      layer.intermediate

		UpperCamelCase__ :str						=      get_encoder_layer_array(_A ,		"""_intermediate_dense/kernel"""  )
		UpperCamelCase__ :Union[str, Any]						=      get_encoder_layer_array(_A ,		"""_intermediate_dense/bias"""  )

		# Output
		UpperCamelCase__ :List[Any]						=      layer.output

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

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

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

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

	UpperCamelCase__ :Union[str, Any]						=      get_masked_lm_array("""dense/kernel"""  )
	UpperCamelCase__ :Any						=      get_masked_lm_array("""dense/bias"""  )

	UpperCamelCase__ :List[Any]						=      get_masked_lm_array("""layer_norm/gamma"""  )
	UpperCamelCase__ :int						=      get_masked_lm_array("""layer_norm/beta"""  )

	UpperCamelCase__ :Tuple						=      get_masked_lm_array("""embedding_table"""  )

	# Pooling
	UpperCamelCase__ :Tuple						=      BertPooler(config=_A  )
	UpperCamelCase__ :List[str]						=      get_encoder_array("""_pooler_layer/kernel"""  )
	UpperCamelCase__ :List[Any]						=      get_encoder_array("""_pooler_layer/bias"""  )

	# Export final model
	model.save_pretrained(_A  )

	# Integration test - should load without any errors ;)
	UpperCamelCase__ :Any						=      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 __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

	snake_case_						       =	0
	snake_case_						       =	0
	while row < size and col < size:
		# pivoting
		snake_case_						       =	max((abs(augmented[rowa][col]				), rowa) for rowa in range(_A	,      _A				)				)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_       , snake_case_						       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	0
			for cola in range(col + 1	,      size + 1				):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1	,      _A				):
		for row in range(_A				):
			snake_case_						       =	augmented[row][col] / augmented[col][col]
			for cola in range(_A	,      size + 1				):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]	,      10				)] for row in range(_A				)
	]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for x_val, y_val in enumerate(_A				):
		for col in range(_A				):
			snake_case_						       =	(x_val + 1) ** (size - col - 1)
		snake_case_						       =	y_val

	snake_case_						       =	solve(_A	,      _A				)

	def interpolated_func(_A				) -> int:
		return sum(
		    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
		    for x_val in range(_A				)				)

	return interpolated_func


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)


def 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

	snake_case_						       =	[
	    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,      order + 1				)
	]

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


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