





"""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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


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

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

				return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

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

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

				return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

		def get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
				UpperCamelCase   =	tf.train.load_variable(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

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

				return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

		def get_encoder_attention_layer_array(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
				UpperCamelCase   =	tf.train.load_variable(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	array.reshape(_SCREAMING_SNAKE_CASE    )

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

				return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

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

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

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

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

				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_attention_layer_norm/gamma"    )
				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_attention_layer_norm/beta"    )

				# Intermediate
				UpperCamelCase   =	layer.intermediate

				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_intermediate_dense/kernel"    )
				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_intermediate_dense/bias"    )

				# Output
				UpperCamelCase   =	layer.output

				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_dense/kernel"    )
				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_dense/bias"    )

				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_output_layer_norm/gamma"    )
				UpperCamelCase   =	get_encoder_layer_array(_SCREAMING_SNAKE_CASE  ,	"_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=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	get_encoder_array("_pooler_layer/kernel"    )
		UpperCamelCase   =	get_encoder_array("_pooler_layer/bias"    )

		# Export final model
		model.save_pretrained(_SCREAMING_SNAKE_CASE    )

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

		print("Model conversion was done sucessfully!"    )


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













"""simple docstring"""





from typing import List

from .keymap import KEYMAP, get_character


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		def decorator(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	[]    )
				handle += [key]
				setattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	_SCREAMING_SNAKE_CASE    )
				return func

		return decorator


def       a__					(					*_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		def decorator(_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	getattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	[]    )
				handle += keys
				setattr(_SCREAMING_SNAKE_CASE  ,	"handle_key"  ,	_SCREAMING_SNAKE_CASE    )
				return func

		return decorator





class        _lowerCamelCase      (       _lowercase ):


						def __new__(cls   ,							__a   ,							__a   ,							__a		) ->   Any:
								UpperCamelCase   =	super().__new__(cls   ,							__a   ,							__a   ,							__a		)
								if not hasattr(__a   ,							"key_handler"		):
										setattr(__a   ,							"key_handler"   ,							{}		)
								setattr(__a   ,							"handle_input"   ,							KeyHandler.handle_input		)

								for value in attrs.values():
										UpperCamelCase   =	getattr(__a   ,							"handle_key"   ,							[]		)
										for key in handled_keys:
												UpperCamelCase   =	value
								return new_cls




						@staticmethod
						def     snake_case_   (cls		) ->   Optional[Any]:
								UpperCamelCase   =	get_character()
								if char != KEYMAP["undefined"]:
										UpperCamelCase   =	ord(__a		)
								UpperCamelCase   =	cls.key_handler.get(__a		)
								if handler:
										UpperCamelCase   =	char
										return handler(cls		)
								else:
										return None



def       a__					(					cls    ):




		"""simple docstring"""


		return KeyHandler(cls.__name__  ,	cls.__bases__  ,	cls.__dict__.copy()    )






