"""simple docstring"""


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()
UpperCAmelCase__     						=  logging.get_logger(__name__)






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







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

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







  """simple docstring"""
  logger.info(f'''Loading model based on config from {config_path}...'''						)
  _UpperCAmelCase         =					BertConfig.from_json_file(A__						)
  _UpperCAmelCase         =					BertModel(A__						)

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

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


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




'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A					:  Dict											=      {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A					:  str											=      ["TimmBackbone"]


if TYPE_CHECKING:
  from .configuration_timm_backbone import TimmBackboneConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timm_backbone import TimmBackbone

else:
  import sys

  __A					:  Dict											=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



