



"""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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


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

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

    return torch.from_numpy(__lowerCAmelCase							)

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

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

    return torch.from_numpy(__lowerCAmelCase							)

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

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

    return torch.from_numpy(__lowerCAmelCase							)

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

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

    return torch.from_numpy(__lowerCAmelCase							)

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

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

    # Self-attention
    lowercase_								= layer.attention.self

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

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

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

    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_attention_layer_norm/gamma"""							)
    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_attention_layer_norm/beta"""							)

    # Intermediate
    lowercase_								= layer.intermediate

    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_intermediate_dense/kernel"""							)
    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_intermediate_dense/bias"""							)

    # Output
    lowercase_								= layer.output

    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_output_dense/kernel"""							)
    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_output_dense/bias"""							)

    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_output_layer_norm/gamma"""							)
    lowercase_								= get_encoder_layer_array(__lowerCAmelCase ,  """_output_layer_norm/beta"""							)

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

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

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

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

  lowercase_								= get_masked_lm_array("""embedding_table"""							)

  # Pooling
  lowercase_								= BertPooler(config=__lowerCAmelCase							)
  lowercase_								= get_encoder_array("""_pooler_layer/kernel"""							)
  lowercase_								= get_encoder_array("""_pooler_layer/bias"""							)

  # Export final model
  model.save_pretrained(__lowerCAmelCase							)

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

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


if __name__ == "__main__":
       UpperCAmelCase						:		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.",
       )
       UpperCAmelCase						:		int			= 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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

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

