



'''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_							: Dict						 =	logging.get_logger(__name__)




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _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     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       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_							: 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_							: 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'''






# using dfs for finding eulerian path traversal




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=None		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = (path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = True, True
                     _UpperCAmelCase  :						List[Any]			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       return path




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = 0
       _UpperCAmelCase  :						Optional[int]			     = -1
       for i in range(lowerCAmelCase_		):
              if i not in graph.keys():
                     continue
              if len(graph[i]		) % 2 == 1:
                     odd_degree_nodes += 1
                     _UpperCAmelCase  :						Optional[int]			     = i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = [[False for _ in range(max_node + 1		)] for _ in range(max_node + 1		)]
       _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = check_circuit_or_path(lowerCAmelCase_       ,   lowerCAmelCase_		)
       if check == 3:
              print("""graph is not Eulerian"""		)
              print("""no path"""		)
              return
       _UpperCAmelCase  :						Dict			     = 1
       if check == 2:
              _UpperCAmelCase  :						Dict			     = odd_node
              print("""graph has a Euler path"""		)
       if check == 1:
              print("""graph has a Euler cycle"""		)
       _UpperCAmelCase  :						Dict			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       print(lowerCAmelCase_		)




def     snake_case_					(     )->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _UpperCAmelCase  :						int			     = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _UpperCAmelCase  :						Tuple			     = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _UpperCAmelCase  :						List[Any]			     = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _UpperCAmelCase  :						List[str]			     = {
           1: [],
           2: []
           # all degree is zero
       }
       _UpperCAmelCase  :						Union[str, Any]			     = 10
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   main()



