
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  :					Dict						    =							logging.get_logger(__name__)







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

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

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

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


if __name__ == "__main__":
  lowercase  :					List[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).""",
  )
  lowercase  :					str						    =							parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)


import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     int       	=      a.name
       lowercase							:     Any       	=      b.name

       lowercase							:     Optional[Any]       	=      """"""
       lowercase							:     Dict       	=      """"""

       lowercase							:     int       	=      a == b

       lowercase							:     int       	=      name_a
       lowercase							:     List[str]       	=      name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       for i, input_name in enumerate(node_proto.input				):
              if input_name == name:
                     node_proto.input.insert(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     node_proto.input.pop(i + 1				)

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              _graph_replace_input_with(node_proto.attribute[1].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for n in graph_proto.node:
              _node_replace_input_with(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Any       	=      list(model.graph.initializer				)
       lowercase							:     Dict       	=      list(model_without_ext.graph.initializer				)
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              lowercase							:     Union[str, Any]       	=      inits[i].name
              lowercase							:     Dict       	=      inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]				)

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Union[str, Any]       	=      os.path.dirname(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      os.path.basename(SCREAMING_SNAKE_CASE__				)

       lowercase							:     str       	=      onnx.load(os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)				)

       lowercase							:     List[str]       	=      list(model.graph.initializer				)

       lowercase							:     Tuple       	=      set()
       lowercase							:     int       	=      {}
       lowercase							:     Optional[Any]       	=      []

       lowercase							:     Dict       	=      0

       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              if i in dup_set:
                     continue

              for j in range(i + 1					,	len(SCREAMING_SNAKE_CASE__				)				):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]					,	inits[j]				):
                            dup_set.add(SCREAMING_SNAKE_CASE__				)
                            dup_set.add(SCREAMING_SNAKE_CASE__				)

                            lowercase							:     int       	=      inits[j].data_type
                            lowercase							:     Optional[int]       	=      numpy.prod(inits[j].dims				)
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print("""unexpected data type: """					,	SCREAMING_SNAKE_CASE__				)
                            total_reduced_size += mem_size

                            lowercase							:     Tuple       	=      inits[i].name
                            lowercase							:     int       	=      inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   lowercase							:     List[str]       	=      [name_j]
                            ind_to_replace.append((j, i)				)

       print("""total reduced size: """					,	total_reduced_size / 1_024 / 1_024 / 1_024					,	"""GB"""				)

       lowercase							:     str       	=      sorted(SCREAMING_SNAKE_CASE__				)
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      """optimized_""" + model_file_name
       lowercase							:     Dict       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       onnx.save(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return new_model
