





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase 				=     logging.get_logger(__name__)



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Optional[int]			      =	os.path.abspath(_SCREAMING_SNAKE_CASE     )
   logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""     )
   # Load weights from TF model
   A_     :				str			      =	tf.train.list_variables(_SCREAMING_SNAKE_CASE     )
   A_     :				Tuple			      =	[]
   A_     :				Dict			      =	[]
   A_     :				str			      =	[]
   for full_name, shape in init_vars:
      # logger.info(f"Loading TF weight {name} with shape {shape}")
      A_     :				Union[str, Any]			      =	full_name.split("""/"""     )
      if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
         logger.info(f"""Skipping non-model layer {full_name}"""     )
         continue
      if "optimizer" in full_name:
         logger.info(f"""Skipping optimization layer {full_name}"""     )
         continue
      if name[0] == "model":
         # ignore initial 'model'
         A_     :				List[str]			      =	name[1:]
      # figure out how many levels deep the name is
      A_     :				Optional[Any]			      =	0
      for _name in name:
         if _name.startswith("""layer_with_weights"""     ):
            depth += 1
         else:
            break
      layer_depth.append(_SCREAMING_SNAKE_CASE     )
      # read data
      A_     :				List[str]			      =	tf.train.load_variable(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE     )
      names.append("""/""".join(_SCREAMING_SNAKE_CASE     )     )
      arrays.append(_SCREAMING_SNAKE_CASE     )
   logger.info(f"""Read a total of {len(_SCREAMING_SNAKE_CASE     ):,} layers"""     )

   # Sanity check
   if len(set(_SCREAMING_SNAKE_CASE     )     ) != 1:
      raise ValueError(f"""Found layer names with different depths (layer depth {list(set(_SCREAMING_SNAKE_CASE     )     )})"""     )
   A_     :				Dict			      =	list(set(_SCREAMING_SNAKE_CASE     )     )[0]
   if layer_depth != 1:
      raise ValueError(
          """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
          """ heads."""     )

   # convert layers
   logger.info("""Converting weights..."""     )
   for full_name, array in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE     ):
      A_     :				Optional[Any]			      =	full_name.split("""/"""     )
      A_     :				Dict			      =	model
      A_     :				Optional[Any]			      =	[]
      for i, m_name in enumerate(_SCREAMING_SNAKE_CASE     ):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith("""layer_with_weights"""     ):
            A_     :				int			      =	int(m_name.split("""-"""     )[-1]     )
            if layer_num <= 2:
               # embedding layers
               # layer_num 0: word_embeddings
               # layer_num 1: position_embeddings
               # layer_num 2: token_type_embeddings
               continue
            elif layer_num == 3:
               # embedding LayerNorm
               trace.extend(["""embeddings""", """LayerNorm"""]     )
               A_     :				Any			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""embeddings"""     )
               A_     :				Dict			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""LayerNorm"""     )
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(["""encoder""", """layer""", str(layer_num - 4     )]     )
               A_     :				List[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""encoder"""     )
               A_     :				Optional[int]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""layer"""     )
               A_     :				List[Any]			      =	pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(["""pooler""", """dense"""]     )
               A_     :				List[str]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""pooler"""     )
               A_     :				Dict			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""dense"""     )
         elif m_name == "embeddings":
            trace.append("""embeddings"""     )
            A_     :				str			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""embeddings"""     )
            if layer_num == 0:
               trace.append("""word_embeddings"""     )
               A_     :				str			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""word_embeddings"""     )
            elif layer_num == 1:
               trace.append("""position_embeddings"""     )
               A_     :				int			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""position_embeddings"""     )
            elif layer_num == 2:
               trace.append("""token_type_embeddings"""     )
               A_     :				Optional[int]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""token_type_embeddings"""     )
            else:
               raise ValueError(f"""Unknown embedding layer with name {full_name}"""     )
            trace.append("""weight"""     )
            A_     :				List[str]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""weight"""     )
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(["""attention""", """self"""]     )
            A_     :				List[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""attention"""     )
            A_     :				Any			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""self"""     )
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(["""attention""", """output""", """LayerNorm"""]     )
            A_     :				Tuple			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""attention"""     )
            A_     :				Tuple			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""output"""     )
            A_     :				Optional[int]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""LayerNorm"""     )
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(["""attention""", """output""", """dense"""]     )
            A_     :				Optional[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""attention"""     )
            A_     :				List[str]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""output"""     )
            A_     :				Union[str, Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""dense"""     )
         elif m_name == "_output_dense":
            # output dense
            trace.extend(["""output""", """dense"""]     )
            A_     :				Any			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""output"""     )
            A_     :				Tuple			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""dense"""     )
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(["""output""", """LayerNorm"""]     )
            A_     :				Dict			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""output"""     )
            A_     :				Union[str, Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""LayerNorm"""     )
         elif m_name == "_key_dense":
            # attention key
            trace.append("""key"""     )
            A_     :				List[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""key"""     )
         elif m_name == "_query_dense":
            # attention query
            trace.append("""query"""     )
            A_     :				Optional[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""query"""     )
         elif m_name == "_value_dense":
            # attention value
            trace.append("""value"""     )
            A_     :				Tuple			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""value"""     )
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(["""intermediate""", """dense"""]     )
            A_     :				Dict			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""intermediate"""     )
            A_     :				Dict			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""dense"""     )
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append("""output"""     )
            A_     :				Optional[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""output"""     )
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append("""bias"""     )
            A_     :				str			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""bias"""     )
         elif m_name in ["kernel", "gamma"]:
            trace.append("""weight"""     )
            A_     :				Optional[Any]			      =	getattr(_SCREAMING_SNAKE_CASE  ,"""weight"""     )
         else:
            logger.warning(f"""Ignored {m_name}"""     )
            # for certain layers reshape is necessary
      A_     :				Dict			      =	""".""".join(_SCREAMING_SNAKE_CASE     )
      if re.match(r"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""  ,_SCREAMING_SNAKE_CASE     ) or re.match(
          r"""(\S+)\.attention\.output\.dense\.weight"""  ,_SCREAMING_SNAKE_CASE     ):
         A_     :				str			      =	array.reshape(pointer.data.shape     )
      if "kernel" in full_name:
         A_     :				int			      =	array.transpose()
      if pointer.shape == array.shape:
         A_     :				List[Any]			      =	torch.from_numpy(_SCREAMING_SNAKE_CASE     )
      else:
         raise ValueError(
             f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
             f""" {array.shape}"""     )
      logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""     )
   return model



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   logger.info(f"""Loading model based on config from {config_path}..."""     )
   A_     :				str			      =	BertConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
   A_     :				Dict			      =	BertModel(_SCREAMING_SNAKE_CASE     )

   # Load weights from checkpoint
   logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""     )
   load_tfa_weights_in_bert(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE     )

   # Save pytorch-model
   logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""     )
   torch.save(model.state_dict()  ,_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      _lowerCAmelCase 				=     argparse.ArgumentParser()
      parser.add_argument(
          """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x 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 (must include filename).""",
      )
      _lowerCAmelCase 				=     parser.parse_args()
      convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













from ....configuration_utils import PretrainedConfig
from ....utils import logging


_lowerCAmelCase 				=     logging.get_logger(__name__)

# TODO: upload to AWS
_lowerCAmelCase 				=     {
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''retribert'''
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=8      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=True      ,       a__=128      ,       a__=0      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       **a__       )

         A_     :				Union[str, Any]			      =	vocab_size
         A_     :				Optional[Any]			      =	hidden_size
         A_     :				Optional[Any]			      =	num_hidden_layers
         A_     :				List[str]			      =	num_attention_heads
         A_     :				Optional[int]			      =	hidden_act
         A_     :				Any			      =	intermediate_size
         A_     :				Tuple			      =	hidden_dropout_prob
         A_     :				Optional[Any]			      =	attention_probs_dropout_prob
         A_     :				Dict			      =	max_position_embeddings
         A_     :				Dict			      =	type_vocab_size
         A_     :				List[str]			      =	initializer_range
         A_     :				Optional[int]			      =	layer_norm_eps
         A_     :				Optional[Any]			      =	share_encoders
         A_     :				Dict			      =	projection_dim






