



'''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      ):
		def get_masked_lm_array(_lowerCamelCase:     str      ):
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	F"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	tf.train.load_variable(_lowerCamelCase ,   _lowerCamelCase      )

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

				return torch.from_numpy(_lowerCamelCase      )

		def get_encoder_array(_lowerCamelCase:     str      ):
				__SCREAMING_SNAKE_CASE :	str					 =	F"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
				__SCREAMING_SNAKE_CASE :	Any					 =	tf.train.load_variable(_lowerCamelCase ,   _lowerCamelCase      )

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

				return torch.from_numpy(_lowerCamelCase      )

		def get_encoder_layer_array(_lowerCamelCase:     int ,   _lowerCamelCase:     str      ):
				__SCREAMING_SNAKE_CASE :	List[str]					 =	F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
				__SCREAMING_SNAKE_CASE :	int					 =	tf.train.load_variable(_lowerCamelCase ,   _lowerCamelCase      )

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

				return torch.from_numpy(_lowerCamelCase      )

		def get_encoder_attention_layer_array(_lowerCamelCase:     int ,   _lowerCamelCase:     str ,   _lowerCamelCase:     str      ):
				__SCREAMING_SNAKE_CASE :	Any					 =	F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
				__SCREAMING_SNAKE_CASE :	str					 =	tf.train.load_variable(_lowerCamelCase ,   _lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	array.reshape(_lowerCamelCase      )

				if "kernel" in name:
						__SCREAMING_SNAKE_CASE :	Optional[int]					 =	array.transpose()

				return torch.from_numpy(_lowerCamelCase      )

		print(F"Loading model based on config from {config_path}..."      )
		__SCREAMING_SNAKE_CASE :	str					 =	BertConfig.from_json_file(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	str					 =	BertForMaskedLM(_lowerCamelCase      )

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

				# Self-attention
				__SCREAMING_SNAKE_CASE :	BertSelfAttention					 =	layer.attention.self

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

				# Self-attention Output
				__SCREAMING_SNAKE_CASE :	BertSelfOutput					 =	layer.attention.output

				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	get_encoder_attention_layer_array(
				    _lowerCamelCase ,   """_output_dense/kernel""" ,   self_output.dense.weight.data.shape      )
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	get_encoder_attention_layer_array(
				    _lowerCamelCase ,   """_output_dense/bias""" ,   self_output.dense.bias.data.shape      )

				__SCREAMING_SNAKE_CASE :	List[Any]					 =	get_encoder_layer_array(_lowerCamelCase ,   """_attention_layer_norm/gamma"""      )
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	get_encoder_layer_array(_lowerCamelCase ,   """_attention_layer_norm/beta"""      )

				# Intermediate
				__SCREAMING_SNAKE_CASE :	BertIntermediate					 =	layer.intermediate

				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	get_encoder_layer_array(_lowerCamelCase ,   """_intermediate_dense/kernel"""      )
				__SCREAMING_SNAKE_CASE :	Dict					 =	get_encoder_layer_array(_lowerCamelCase ,   """_intermediate_dense/bias"""      )

				# Output
				__SCREAMING_SNAKE_CASE :	BertOutput					 =	layer.output

				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	get_encoder_layer_array(_lowerCamelCase ,   """_output_dense/kernel"""      )
				__SCREAMING_SNAKE_CASE :	Any					 =	get_encoder_layer_array(_lowerCamelCase ,   """_output_dense/bias"""      )

				__SCREAMING_SNAKE_CASE :	str					 =	get_encoder_layer_array(_lowerCamelCase ,   """_output_layer_norm/gamma"""      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	get_encoder_layer_array(_lowerCamelCase ,   """_output_layer_norm/beta"""      )

		# Embeddings
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	get_encoder_array("""_position_embedding_layer/embeddings"""      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	get_encoder_array("""_type_embedding_layer/embeddings"""      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	get_encoder_array("""_embedding_norm_layer/gamma"""      )
		__SCREAMING_SNAKE_CASE :	int					 =	get_encoder_array("""_embedding_norm_layer/beta"""      )

		# LM Head
		__SCREAMING_SNAKE_CASE :	Dict					 =	model.cls.predictions.transform

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

		__SCREAMING_SNAKE_CASE :	List[str]					 =	get_masked_lm_array("""layer_norm/gamma"""      )
		__SCREAMING_SNAKE_CASE :	Tuple					 =	get_masked_lm_array("""layer_norm/beta"""      )

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	get_masked_lm_array("""embedding_table"""      )

		# Pooling
		__SCREAMING_SNAKE_CASE :	Tuple					 =	BertPooler(config=_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	BertPooler					 =	get_encoder_array("""_pooler_layer/kernel"""      )
		__SCREAMING_SNAKE_CASE :	BertPooler					 =	get_encoder_array("""_pooler_layer/bias"""      )

		# Export final model
		model.save_pretrained(_lowerCamelCase      )

		# Integration test - should load without any errors ;)
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	BertForMaskedLM.from_pretrained(_lowerCamelCase      )
		print(new_model.eval()      )

		print("""Model conversion was done sucessfully!"""      )


if __name__ == "__main__":
						UpperCamelCase__ :			Dict												=				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__ :			Dict												=				parser.parse_args()
						convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__ :			List[Any]												=				{
    '''configuration_time_series_transformer''': [
        '''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TimeSeriesTransformerConfig''',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__ :			List[Any]												=				[
						    '''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TimeSeriesTransformerForPrediction''',
						    '''TimeSeriesTransformerModel''',
						    '''TimeSeriesTransformerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_time_series_transformer import (
						    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TimeSeriesTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_time_series_transformer import (
												    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TimeSeriesTransformerForPrediction,
												    TimeSeriesTransformerModel,
												    TimeSeriesTransformerPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase__ :			Dict												=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)