


'''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()
lowerCAmelCase     :  Optional[int]											=    logging.get_logger(__name__)






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







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







       """simple docstring"""







       logger.info(f'Loading model based on config from {config_path}...'       )
       _lowerCAmelCase     :				int       =							BertConfig.from_json_file(_A       )
       _lowerCAmelCase     :				List[str]       =							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__":
       lowerCAmelCase     :  Dict											=    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     :  Union[str, Any]											=    parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





'''simple docstring'''







from ..utils import DummyObject, requires_backends
class 						UpperCamelCase__       (			metaclass=SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     ["torch", "torchsde"]







     def __init__(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(self   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )







     @classmethod
     def a						(			cls   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            requires_backends(cls   ,						['torch', 'torchsde'] )

