


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				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:







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

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

      return torch.from_numpy(__UpperCamelCase   )

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

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

      return torch.from_numpy(__UpperCamelCase   )

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

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

      return torch.from_numpy(__UpperCamelCase   )

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

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

      return torch.from_numpy(__UpperCamelCase   )

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

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

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

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

      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_attention_layer_norm/gamma"""   )
      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_attention_layer_norm/beta"""   )

      # Intermediate
      UpperCAmelCase							=     layer.intermediate

      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_intermediate_dense/kernel"""   )
      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_intermediate_dense/bias"""   )

      # Output
      UpperCAmelCase							=     layer.output

      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_output_dense/kernel"""   )
      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_output_dense/bias"""   )

      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_output_layer_norm/gamma"""   )
      UpperCAmelCase							=     get_encoder_layer_array(__UpperCamelCase  ,						"""_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=__UpperCamelCase   )
   UpperCAmelCase							=     get_encoder_array("""_pooler_layer/kernel"""   )
   UpperCAmelCase							=     get_encoder_array("""_pooler_layer/bias"""   )

   # Export final model
   model.save_pretrained(__UpperCamelCase   )

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

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


if __name__ == "__main__":
 __a     =			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.""",
 )
 __a     =			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_torch_available


__a     =			{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __a     =			[
     """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """IBertForMaskedLM""",
     """IBertForMultipleChoice""",
     """IBertForQuestionAnswering""",
     """IBertForSequenceClassification""",
     """IBertForTokenClassification""",
     """IBertModel""",
     """IBertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ibert import (
      IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      IBertForMaskedLM,
      IBertForMultipleChoice,
      IBertForQuestionAnswering,
      IBertForSequenceClassification,
      IBertForTokenClassification,
      IBertModel,
      IBertPreTrainedModel,
  )

else:
 import sys

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



