



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__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







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

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

								return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

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

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

								return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

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

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

								return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

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

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

								return torch.from_numpy(_SCREAMING_SNAKE_CASE    )

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

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

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

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

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

								# Intermediate
								__a       =		layer.intermediate

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

								# Output
								__a       =		layer.output

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

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

				# Export final model
				model.save_pretrained(_SCREAMING_SNAKE_CASE    )

				# Integration test - should load without any errors ;)
				__a       =		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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
