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()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					os.path.abspath(lowerCAmelCase_	)
 logger.info(F"Converting TensorFlow checkpoint from {tf_path}"	)
 # Load weights from TF model
 UpperCamelCase_					:						List[str] =					tf.train.list_variables(lowerCAmelCase_	)
 UpperCamelCase_					:						Optional[Any] =					[]
 UpperCamelCase_					:						Optional[Any] =					[]
 UpperCamelCase_					:						Union[str, Any] =					[]
 for full_name, shape in init_vars:
  # logger.info(f"Loading TF weight {name} with shape {shape}")
  UpperCamelCase_					:						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'
   UpperCamelCase_					:						Optional[int] =					name[1:]
  # figure out how many levels deep the name is
  UpperCamelCase_					:						List[str] =					0
  for _name in name:
   if _name.startswith('layer_with_weights'	):
    depth += 1
   else:
    break
  layer_depth.append(lowerCAmelCase_	)
  # read data
  UpperCamelCase_					:						Optional[int] =					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_	)	)})"	)
 UpperCamelCase_					:						Any =					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_	):
  UpperCamelCase_					:						str =					full_name.split('/'	)
  UpperCamelCase_					:						List[Any] =					model
  UpperCamelCase_					:						List[Any] =					[]
  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'	):
    UpperCamelCase_					:						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']	)
     UpperCamelCase_					:						Dict =					getattr(lowerCAmelCase_  ,  'embeddings'	)
     UpperCamelCase_					:						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	)]	)
     UpperCamelCase_					:						List[str] =					getattr(lowerCAmelCase_  ,  'encoder'	)
     UpperCamelCase_					:						Any =					getattr(lowerCAmelCase_  ,  'layer'	)
     UpperCamelCase_					:						Tuple =					pointer[layer_num - 4]
    elif layer_num == config.num_hidden_layers + 4:
     # pooler layer
     trace.extend(['pooler', 'dense']	)
     UpperCamelCase_					:						Any =					getattr(lowerCAmelCase_  ,  'pooler'	)
     UpperCamelCase_					:						int =					getattr(lowerCAmelCase_  ,  'dense'	)
   elif m_name == "embeddings":
    trace.append('embeddings'	)
    UpperCamelCase_					:						Optional[int] =					getattr(lowerCAmelCase_  ,  'embeddings'	)
    if layer_num == 0:
     trace.append('word_embeddings'	)
     UpperCamelCase_					:						List[str] =					getattr(lowerCAmelCase_  ,  'word_embeddings'	)
    elif layer_num == 1:
     trace.append('position_embeddings'	)
     UpperCamelCase_					:						Optional[Any] =					getattr(lowerCAmelCase_  ,  'position_embeddings'	)
    elif layer_num == 2:
     trace.append('token_type_embeddings'	)
     UpperCamelCase_					:						Optional[Any] =					getattr(lowerCAmelCase_  ,  'token_type_embeddings'	)
    else:
     raise ValueError(F"Unknown embedding layer with name {full_name}"	)
    trace.append('weight'	)
    UpperCamelCase_					:						List[str] =					getattr(lowerCAmelCase_  ,  'weight'	)
   elif m_name == "_attention_layer":
    # self-attention layer
    trace.extend(['attention', 'self']	)
    UpperCamelCase_					:						int =					getattr(lowerCAmelCase_  ,  'attention'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(lowerCAmelCase_  ,  'self'	)
   elif m_name == "_attention_layer_norm":
    # output attention norm
    trace.extend(['attention', 'output', 'LayerNorm']	)
    UpperCamelCase_					:						Tuple =					getattr(lowerCAmelCase_  ,  'attention'	)
    UpperCamelCase_					:						Optional[int] =					getattr(lowerCAmelCase_  ,  'output'	)
    UpperCamelCase_					:						Dict =					getattr(lowerCAmelCase_  ,  'LayerNorm'	)
   elif m_name == "_attention_output_dense":
    # output attention dense
    trace.extend(['attention', 'output', 'dense']	)
    UpperCamelCase_					:						Tuple =					getattr(lowerCAmelCase_  ,  'attention'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(lowerCAmelCase_  ,  'output'	)
    UpperCamelCase_					:						Tuple =					getattr(lowerCAmelCase_  ,  'dense'	)
   elif m_name == "_output_dense":
    # output dense
    trace.extend(['output', 'dense']	)
    UpperCamelCase_					:						Union[str, Any] =					getattr(lowerCAmelCase_  ,  'output'	)
    UpperCamelCase_					:						int =					getattr(lowerCAmelCase_  ,  'dense'	)
   elif m_name == "_output_layer_norm":
    # output dense
    trace.extend(['output', 'LayerNorm']	)
    UpperCamelCase_					:						Any =					getattr(lowerCAmelCase_  ,  'output'	)
    UpperCamelCase_					:						Optional[int] =					getattr(lowerCAmelCase_  ,  'LayerNorm'	)
   elif m_name == "_key_dense":
    # attention key
    trace.append('key'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(lowerCAmelCase_  ,  'key'	)
   elif m_name == "_query_dense":
    # attention query
    trace.append('query'	)
    UpperCamelCase_					:						Optional[int] =					getattr(lowerCAmelCase_  ,  'query'	)
   elif m_name == "_value_dense":
    # attention value
    trace.append('value'	)
    UpperCamelCase_					:						List[str] =					getattr(lowerCAmelCase_  ,  'value'	)
   elif m_name == "_intermediate_dense":
    # attention intermediate dense
    trace.extend(['intermediate', 'dense']	)
    UpperCamelCase_					:						List[str] =					getattr(lowerCAmelCase_  ,  'intermediate'	)
    UpperCamelCase_					:						int =					getattr(lowerCAmelCase_  ,  'dense'	)
   elif m_name == "_output_layer_norm":
    # output layer norm
    trace.append('output'	)
    UpperCamelCase_					:						Optional[Any] =					getattr(lowerCAmelCase_  ,  'output'	)
   # weights & biases
   elif m_name in ["bias", "beta"]:
    trace.append('bias'	)
    UpperCamelCase_					:						int =					getattr(lowerCAmelCase_  ,  'bias'	)
   elif m_name in ["kernel", "gamma"]:
    trace.append('weight'	)
    UpperCamelCase_					:						str =					getattr(lowerCAmelCase_  ,  'weight'	)
   else:
    logger.warning(F"Ignored {m_name}"	)
        # for certain layers reshape is necessary
  UpperCamelCase_					:						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_	):
   UpperCamelCase_					:						int =					array.reshape(pointer.data.shape	)
  if "kernel" in full_name:
   UpperCamelCase_					:						Any =					array.transpose()
  if pointer.shape == array.shape:
   UpperCamelCase_					:						List[Any] =					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    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     str	):
 logger.info(F"Loading model based on config from {config_path}..."	)
 UpperCamelCase_					:						Dict =					BertConfig.from_json_file(lowerCAmelCase_	)
 UpperCamelCase_					:						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__":
   a_    					=       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).',
   )
   a_    					=       parser.parse_args()
   convert_tfa_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_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



