


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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:
  def get_masked_lm_array(__lowerCAmelCase				):
    snake_case__						= F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    snake_case__						= tf.train.load_variable(__lowerCAmelCase							,   __lowerCAmelCase				)

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

    return torch.from_numpy(__lowerCAmelCase				)

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

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

    return torch.from_numpy(__lowerCAmelCase				)

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

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

    return torch.from_numpy(__lowerCAmelCase				)

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

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

    return torch.from_numpy(__lowerCAmelCase				)

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

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

    # Self-attention
    snake_case__						= layer.attention.self

    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_query_dense/kernel'''							,   self_attn.query.weight.data.shape				)
    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_query_dense/bias'''							,   self_attn.query.bias.data.shape				)
    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_key_dense/kernel'''							,   self_attn.key.weight.data.shape				)
    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_key_dense/bias'''							,   self_attn.key.bias.data.shape				)
    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_value_dense/kernel'''							,   self_attn.value.weight.data.shape				)
    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_value_dense/bias'''							,   self_attn.value.bias.data.shape				)

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

    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_output_dense/kernel'''							,   self_output.dense.weight.data.shape				)
    snake_case__						= get_encoder_attention_layer_array(
        __lowerCAmelCase							,   '''_output_dense/bias'''							,   self_output.dense.bias.data.shape				)

    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_attention_layer_norm/gamma'''				)
    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_attention_layer_norm/beta'''				)

    # Intermediate
    snake_case__						= layer.intermediate

    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_intermediate_dense/kernel'''				)
    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_intermediate_dense/bias'''				)

    # Output
    snake_case__						= layer.output

    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_output_dense/kernel'''				)
    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_output_dense/bias'''				)

    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_output_layer_norm/gamma'''				)
    snake_case__						= get_encoder_layer_array(__lowerCAmelCase							,   '''_output_layer_norm/beta'''				)

  # Embeddings
  snake_case__						= get_encoder_array('''_position_embedding_layer/embeddings'''				)
  snake_case__						= get_encoder_array('''_type_embedding_layer/embeddings'''				)
  snake_case__						= get_encoder_array('''_embedding_norm_layer/gamma'''				)
  snake_case__						= get_encoder_array('''_embedding_norm_layer/beta'''				)

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

  snake_case__						= get_masked_lm_array('''dense/kernel'''				)
  snake_case__						= get_masked_lm_array('''dense/bias'''				)

  snake_case__						= get_masked_lm_array('''layer_norm/gamma'''				)
  snake_case__						= get_masked_lm_array('''layer_norm/beta'''				)

  snake_case__						= get_masked_lm_array('''embedding_table'''				)

  # Pooling
  snake_case__						= BertPooler(config=__lowerCAmelCase				)
  snake_case__						= get_encoder_array('''_pooler_layer/kernel'''				)
  snake_case__						= get_encoder_array('''_pooler_layer/bias'''				)

  # Export final model
  model.save_pretrained(__lowerCAmelCase				)

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

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


if __name__ == "__main__":
   lowerCamelCase__							:						List[str]							     =   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__							:						int							     =   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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__							:						Tuple							     =   {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Tuple							     =   ["""RobertaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						Optional[int]							     =   [
       """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RobertaForCausalLM""",
       """RobertaForMaskedLM""",
       """RobertaForMultipleChoice""",
       """RobertaForQuestionAnswering""",
       """RobertaForSequenceClassification""",
       """RobertaForTokenClassification""",
       """RobertaModel""",
       """RobertaPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						List[str]							     =   [
       """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFRobertaForCausalLM""",
       """TFRobertaForMaskedLM""",
       """TFRobertaForMultipleChoice""",
       """TFRobertaForQuestionAnswering""",
       """TFRobertaForSequenceClassification""",
       """TFRobertaForTokenClassification""",
       """TFRobertaMainLayer""",
       """TFRobertaModel""",
       """TFRobertaPreTrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__							:						str							     =   [
       """FlaxRobertaForCausalLM""",
       """FlaxRobertaForMaskedLM""",
       """FlaxRobertaForMultipleChoice""",
       """FlaxRobertaForQuestionAnswering""",
       """FlaxRobertaForSequenceClassification""",
       """FlaxRobertaForTokenClassification""",
       """FlaxRobertaModel""",
       """FlaxRobertaPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
   from .tokenization_roberta import RobertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roberta_fast import RobertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roberta import (
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          RobertaForCausalLM,
          RobertaForMaskedLM,
          RobertaForMultipleChoice,
          RobertaForQuestionAnswering,
          RobertaForSequenceClassification,
          RobertaForTokenClassification,
          RobertaModel,
          RobertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roberta import (
          TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRobertaForCausalLM,
          TFRobertaForMaskedLM,
          TFRobertaForMultipleChoice,
          TFRobertaForQuestionAnswering,
          TFRobertaForSequenceClassification,
          TFRobertaForTokenClassification,
          TFRobertaMainLayer,
          TFRobertaModel,
          TFRobertaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
          FlaxRobertaPreTrainedModel,
      )

else:
   import sys

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

