





'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Tuple											=       ['''image_processor''', '''tokenizer''']
      _lowercase:      str											=       '''AutoImageProcessor'''
      _lowercase:      int											=       '''AutoTokenizer'''




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]=None      ,      __snake_case      :			List[Any]=None      ,      **__snake_case      :			Tuple  )    ->				Any:
            _lowerCAmelCase												=							None
            if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""      ,      __snake_case      ,      )
                  _lowerCAmelCase												=							kwargs.pop("""feature_extractor"""  )

            _lowerCAmelCase												=							image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`."""  )
            if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`."""  )

            super().__init__(__snake_case      ,      __snake_case  )
            _lowerCAmelCase												=							self.image_processor
            _lowerCAmelCase												=							False




      def __call__(      self      :			List[Any]      ,      *__snake_case      :			Dict      ,      **__snake_case      :			Union[str, Any]  )    ->				List[str]:
            # For backward compatibility
            if self._in_target_context_manager:
                  return self.current_processor(*__snake_case      ,      **__snake_case  )

            _lowerCAmelCase												=							kwargs.pop("""images"""      ,      __snake_case  )
            _lowerCAmelCase												=							kwargs.pop("""text"""      ,      __snake_case  )
            if len(__snake_case  ) > 0:
                  _lowerCAmelCase												=							args[0]
                  _lowerCAmelCase												=							args[1:]

            if images is None and text is None:
                  raise ValueError("""You need to specify either an `images` or `text` input to process."""  )

            if images is not None:
                  _lowerCAmelCase												=							self.image_processor(__snake_case      ,      *__snake_case      ,      **__snake_case  )
            if text is not None:
                  _lowerCAmelCase												=							self.tokenizer(__snake_case      ,      **__snake_case  )

            if text is None:
                  return inputs
            elif images is None:
                  return encodings
            else:
                  _lowerCAmelCase												=							encodings["""input_ids"""]
                  return inputs




      def     lowercase__   (      self      :			Dict      ,      *__snake_case      :			Union[str, Any]      ,      **__snake_case      :			Tuple  )    ->				Union[str, Any]:
            return self.tokenizer.batch_decode(*__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Union[str, Any]      ,      *__snake_case      :			Optional[Any]      ,      **__snake_case      :			Any  )    ->				Optional[int]:
            return self.tokenizer.decode(*__snake_case      ,      **__snake_case  )




      @contextmanager
      def     lowercase__   (      self      :			int  )    ->				List[str]:
            warnings.warn(
                """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
                """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
                """your images inputs, or in a separate call."""  )
            _lowerCAmelCase												=							True
            _lowerCAmelCase												=							self.tokenizer
            yield
            _lowerCAmelCase												=							self.image_processor
            _lowerCAmelCase												=							False




      def     lowercase__   (      self      :			int      ,      __snake_case      :			List[str]      ,      __snake_case      :			int=False      ,      __snake_case      :			Optional[Any]=None  )    ->				Any:
            if added_vocab is None:
                  _lowerCAmelCase												=							self.tokenizer.get_added_vocab()

            _lowerCAmelCase												=							{}

            while tokens:
                  _lowerCAmelCase												=							re.search(R"""<s_(.*?)>"""      ,      __snake_case      ,      re.IGNORECASE  )
                  if start_token is None:
                        break
                  _lowerCAmelCase												=							start_token.group(1  )
                  _lowerCAmelCase												=							re.search(Rf"</s_{key}>"      ,      __snake_case      ,      re.IGNORECASE  )
                  _lowerCAmelCase												=							start_token.group()
                  if end_token is None:
                        _lowerCAmelCase												=							tokens.replace(__snake_case      ,      """"""  )
                  else:
                        _lowerCAmelCase												=							end_token.group()
                        _lowerCAmelCase												=							re.escape(__snake_case  )
                        _lowerCAmelCase												=							re.escape(__snake_case  )
                        _lowerCAmelCase												=							re.search(f"{start_token_escaped}(.*?){end_token_escaped}"      ,      __snake_case      ,      re.IGNORECASE  )
                        if content is not None:
                              _lowerCAmelCase												=							content.group(1  ).strip()
                              if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                    _lowerCAmelCase												=							self.tokenajson(__snake_case      ,      is_inner_value=__snake_case      ,      added_vocab=__snake_case  )
                                    if value:
                                          if len(__snake_case  ) == 1:
                                                _lowerCAmelCase												=							value[0]
                                          _lowerCAmelCase												=							value
                              else:  # leaf nodes
                                    _lowerCAmelCase												=							[]
                                    for leaf in content.split(R"""<sep/>"""  ):
                                          _lowerCAmelCase												=							leaf.strip()
                                          if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                _lowerCAmelCase												=							leaf[1:-2]  # for categorical special tokens
                                          output[key].append(__snake_case  )
                                    if len(output[key]  ) == 1:
                                          _lowerCAmelCase												=							output[key][0]

                        _lowerCAmelCase												=							tokens[tokens.find(__snake_case  ) + len(__snake_case  ) :].strip()
                        if tokens[:6] == r"<sep/>":  # non-leaf nodes
                              return [output] + self.tokenajson(tokens[6:]      ,      is_inner_value=__snake_case      ,      added_vocab=__snake_case  )

            if len(__snake_case  ):
                  return [output] if is_inner_value else output
            else:
                  return [] if is_inner_value else {"text_sequence": tokens}




      @property
      def     lowercase__   (      self      :			str  )    ->				List[str]:
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,      __snake_case      ,      )
            return self.image_processor_class




      @property
      def     lowercase__   (      self      :			Dict  )    ->				Any:
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,      __snake_case      ,      )
            return self.image_processor













'''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        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_layer_array(lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

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

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

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

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

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

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

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

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

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

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

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


if __name__ == "__main__":
      A__    :       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.''',
      )
      A__    :       Dict              =parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






