
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_						,  a_						,  a_		)						->      Any:







				"""simple docstring"""



				def get_masked_lm_array(a_		):
								__A       =					F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)

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

								return torch.from_numpy(a_		)

				def get_encoder_array(a_		):
								__A       =					F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)

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

								return torch.from_numpy(a_		)

				def get_encoder_layer_array(a_						,  a_		):
								__A       =					F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
								__A       =					tf.train.load_variable(a_						,  a_		)

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

								return torch.from_numpy(a_		)

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

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

								return torch.from_numpy(a_		)

				print(F'''Loading model based on config from {config_path}...'''		)
				__A       =					BertConfig.from_json_file(a_		)
				__A       =					BertForMaskedLM(a_		)

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

								# Self-attention
								__A       =					layer.attention.self

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

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

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

								__A       =					get_encoder_layer_array(a_						,  "_attention_layer_norm/gamma"		)
								__A       =					get_encoder_layer_array(a_						,  "_attention_layer_norm/beta"		)

								# Intermediate
								__A       =					layer.intermediate

								__A       =					get_encoder_layer_array(a_						,  "_intermediate_dense/kernel"		)
								__A       =					get_encoder_layer_array(a_						,  "_intermediate_dense/bias"		)

								# Output
								__A       =					layer.output

								__A       =					get_encoder_layer_array(a_						,  "_output_dense/kernel"		)
								__A       =					get_encoder_layer_array(a_						,  "_output_dense/bias"		)

								__A       =					get_encoder_layer_array(a_						,  "_output_layer_norm/gamma"		)
								__A       =					get_encoder_layer_array(a_						,  "_output_layer_norm/beta"		)

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

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

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

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

				__A       =					get_masked_lm_array("embedding_table"		)

				# Pooling
				__A       =					BertPooler(config=a_		)
				__A       =					get_encoder_array("_pooler_layer/kernel"		)
				__A       =					get_encoder_array("_pooler_layer/bias"		)

				# Export final model
				model.save_pretrained(a_		)

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

				print("Model conversion was done sucessfully!"		)


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




import os
import sys

A			:						Tuple        =  os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A			:						Dict        =  [
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				List[Any]	)  ->    str:
		"""simple docstring"""




		return AutoConfig.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoTokenizer.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				Optional[Any]	)  ->    Optional[Any]:
		"""simple docstring"""




		return AutoTokenizer.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModel.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Optional[Any]	)  ->    Tuple:
		"""simple docstring"""




		return AutoModel.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForCausalLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				str       ,       **__magic_name__	:				Tuple	)  ->    List[str]:
		"""simple docstring"""




		return AutoModelForCausalLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForMaskedLM.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Optional[Any]       ,       **__magic_name__	:				Any	)  ->    Optional[int]:
		"""simple docstring"""




		return AutoModelForMaskedLM.from_pretrained(*__magic_name__       ,       **__magic_name__	)






@add_start_docstrings(AutoModelForSequenceClassification.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Any       ,       **__magic_name__	:				List[str]	)  ->    Union[str, Any]:
		"""simple docstring"""




		return AutoModelForSequenceClassification.from_pretrained(*__magic_name__       ,       **__magic_name__	)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__	)
def 					UpperCamelCase   (      *__magic_name__	:				Dict       ,       **__magic_name__	:				List[Any]	)  ->    int:
		"""simple docstring"""




		return AutoModelForQuestionAnswering.from_pretrained(*__magic_name__       ,       **__magic_name__	)

