

'''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					(						_UpperCamelCase						:     str							, _UpperCamelCase						:     str							, _UpperCamelCase						:     str				)      ->			Union[str, Any]:
     """simple docstring"""




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

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

          return torch.from_numpy(_UpperCamelCase				)

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

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

          return torch.from_numpy(_UpperCamelCase				)

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

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

          return torch.from_numpy(_UpperCamelCase				)

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

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

          return torch.from_numpy(_UpperCamelCase				)

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

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

          # Self-attention
          _SCREAMING_SNAKE_CASE							    =layer.attention.self

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

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

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

          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_attention_layer_norm/gamma'				)
          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_attention_layer_norm/beta'				)

          # Intermediate
          _SCREAMING_SNAKE_CASE							    =layer.intermediate

          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_intermediate_dense/kernel'				)
          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_intermediate_dense/bias'				)

          # Output
          _SCREAMING_SNAKE_CASE							    =layer.output

          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_output_dense/kernel'				)
          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_output_dense/bias'				)

          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_output_layer_norm/gamma'				)
          _SCREAMING_SNAKE_CASE							    =get_encoder_layer_array(_UpperCamelCase							, '_output_layer_norm/beta'				)

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

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

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

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

     _SCREAMING_SNAKE_CASE							    =get_masked_lm_array('embedding_table'				)

     # Pooling
     _SCREAMING_SNAKE_CASE							    =BertPooler(config=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =get_encoder_array('_pooler_layer/kernel'				)
     _SCREAMING_SNAKE_CASE							    =get_encoder_array('_pooler_layer/bias'				)

     # Export final model
     model.save_pretrained(_UpperCamelCase				)

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

     print('Model conversion was done sucessfully!'				)


if __name__ == "__main__":
     lowerCamelCase :      Optional[Any]					 =     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 :      Union[str, Any]					 =     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,
)


lowerCamelCase :      Any					 =     {
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     ["FunnelTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[str]					 =     [
         "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FunnelBaseModel",
         "FunnelForMaskedLM",
         "FunnelForMultipleChoice",
         "FunnelForPreTraining",
         "FunnelForQuestionAnswering",
         "FunnelForSequenceClassification",
         "FunnelForTokenClassification",
         "FunnelModel",
         "FunnelPreTrainedModel",
         "load_tf_weights_in_funnel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFunnelBaseModel",
         "TFFunnelForMaskedLM",
         "TFFunnelForMultipleChoice",
         "TFFunnelForPreTraining",
         "TFFunnelForQuestionAnswering",
         "TFFunnelForSequenceClassification",
         "TFFunnelForTokenClassification",
         "TFFunnelModel",
         "TFFunnelPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
     from .tokenization_funnel import FunnelTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_funnel_fast import FunnelTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_funnel import (
              FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              FunnelBaseModel,
              FunnelForMaskedLM,
              FunnelForMultipleChoice,
              FunnelForPreTraining,
              FunnelForQuestionAnswering,
              FunnelForSequenceClassification,
              FunnelForTokenClassification,
              FunnelModel,
              FunnelPreTrainedModel,
              load_tf_weights_in_funnel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_funnel import (
              TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFunnelBaseModel,
              TFFunnelForMaskedLM,
              TFFunnelForMultipleChoice,
              TFFunnelForPreTraining,
              TFFunnelForQuestionAnswering,
              TFFunnelForSequenceClassification,
              TFFunnelForTokenClassification,
              TFFunnelModel,
              TFFunnelPreTrainedModel,
          )

else:
     import sys

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