



"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase  :						Optional[Any]	=       {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase  :						Union[str, Any]	=       ["RoFormerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase  :						Any	=       [
           "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "RoFormerForCausalLM",
           "RoFormerForMaskedLM",
           "RoFormerForMultipleChoice",
           "RoFormerForQuestionAnswering",
           "RoFormerForSequenceClassification",
           "RoFormerForTokenClassification",
           "RoFormerLayer",
           "RoFormerModel",
           "RoFormerPreTrainedModel",
           "load_tf_weights_in_roformer",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase  :						List[str]	=       [
           "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFRoFormerForCausalLM",
           "TFRoFormerForMaskedLM",
           "TFRoFormerForMultipleChoice",
           "TFRoFormerForQuestionAnswering",
           "TFRoFormerForSequenceClassification",
           "TFRoFormerForTokenClassification",
           "TFRoFormerLayer",
           "TFRoFormerModel",
           "TFRoFormerPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase  :						Union[str, Any]	=       [
           "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FlaxRoFormerForMaskedLM",
           "FlaxRoFormerForMultipleChoice",
           "FlaxRoFormerForQuestionAnswering",
           "FlaxRoFormerForSequenceClassification",
           "FlaxRoFormerForTokenClassification",
           "FlaxRoFormerModel",
           "FlaxRoFormerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

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



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()
lowercase__       =logging.get_logger(__name__)




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

 # Sanity check
 if len(set(lowerCAmelCase__  )  ) != 1:
  raise ValueError(f"Found layer names with different depths (layer depth {list(set(lowerCAmelCase__  )  )})"  )
 __a :  int						=  list(set(lowerCAmelCase__  )  )[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(lowerCAmelCase__  ,   lowerCAmelCase__  ):
  __a :  int						=  full_name.split('''/'''  )
  __a :  Tuple						=  model
  __a :  Dict						=  []
  for i, m_name in enumerate(lowerCAmelCase__  ):
   if m_name == ".ATTRIBUTES":
    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
    break
   if m_name.startswith('''layer_with_weights'''  ):
    __a :  Union[str, Any]						=  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 :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''embeddings'''  )
     __a :  List[str]						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
     # encoder layers
     trace.extend(['''encoder''', '''layer''', str(layer_num - 4  )]  )
     __a :  Dict						=  getattr(lowerCAmelCase__  ,   '''encoder'''  )
     __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''layer'''  )
     __a :  Any						=  pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(['''pooler''', '''dense''']  )
     __a :  Any						=  getattr(lowerCAmelCase__  ,   '''pooler'''  )
     __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "embeddings":
    trace.append('''embeddings'''  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''embeddings'''  )
    if layer_num == 0:
     trace.append('''word_embeddings'''  )
     __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''word_embeddings'''  )
    elif layer_num == 1:
     trace.append('''position_embeddings'''  )
     __a :  List[str]						=  getattr(lowerCAmelCase__  ,   '''position_embeddings'''  )
    elif layer_num == 2:
     trace.append('''token_type_embeddings'''  )
     __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''token_type_embeddings'''  )
    else:
     raise ValueError(f"Unknown embedding layer with name {full_name}"  )
    trace.append('''weight'''  )
    __a :  Tuple						=  getattr(lowerCAmelCase__  ,   '''weight'''  )
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(['''attention''', '''self''']  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''self'''  )
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(['''attention''', '''output''', '''LayerNorm''']  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(['''attention''', '''output''', '''dense''']  )
    __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''attention'''  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  Any						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_dense":
    # output dense
    trace.extend(['''output''', '''dense''']  )
    __a :  Tuple						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  str						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(['''output''', '''LayerNorm''']  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''output'''  )
    __a :  str						=  getattr(lowerCAmelCase__  ,   '''LayerNorm'''  )
   elif m_name == "_key_dense":
    # attention key
    trace.append('''key'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''key'''  )
   elif m_name == "_query_dense":
    # attention query
    trace.append('''query'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''query'''  )
   elif m_name == "_value_dense":
    # attention value
    trace.append('''value'''  )
    __a :  Union[str, Any]						=  getattr(lowerCAmelCase__  ,   '''value'''  )
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(['''intermediate''', '''dense''']  )
    __a :  Optional[Any]						=  getattr(lowerCAmelCase__  ,   '''intermediate'''  )
    __a :  Optional[int]						=  getattr(lowerCAmelCase__  ,   '''dense'''  )
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append('''output'''  )
    __a :  int						=  getattr(lowerCAmelCase__  ,   '''output'''  )
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append('''bias'''  )
    __a :  Dict						=  getattr(lowerCAmelCase__  ,   '''bias'''  )
   elif m_name in ["kernel", "gamma"]:
    trace.append('''weight'''  )
    __a :  List[Any]						=  getattr(lowerCAmelCase__  ,   '''weight'''  )
   else:
    logger.warning(f"Ignored {m_name}"  )
        # for certain layers reshape is necessary
  __a :  List[str]						=  '''.'''.join(lowerCAmelCase__  )
  if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''  ,   lowerCAmelCase__  ) or re.match(
      R'''(\S+)\.attention\.output\.dense\.weight'''  ,   lowerCAmelCase__  ):
   __a :  str						=  array.reshape(pointer.data.shape  )
  if "kernel" in full_name:
   __a :  Optional[Any]						=  array.transpose()
  if pointer.shape == array.shape:
   __a :  str						=  torch.from_numpy(lowerCAmelCase__  )
  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    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Union[str, Any]  ,   lowerCAmelCase__							:				Optional[Any]  ):
 # Instantiate model
 logger.info(f"Loading model based on config from {config_path}..."  )
 __a :  Dict						=  BertConfig.from_json_file(lowerCAmelCase__  )
 __a :  int						=  BertModel(lowerCAmelCase__  )

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

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


if __name__ == "__main__":
      lowercase__       =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).',
      )
      lowercase__       =parser.parse_args()
      convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
