"""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()
_a      :		Optional[int]           =   logging.get_logger(__name__)





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





"""simple docstring"""




_a      :		List[str]           =   {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}




