






'''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__						:Optional[Any]          =			logging.get_logger(__name__)
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[int]:


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

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


     """simple docstring"""
     # Instantiate model
     logger.info(f"Loading model based on config from {config_path}..."     )
     __lowerCamelCase						:       str				   =				BertConfig.from_json_file(_lowercase     )
     __lowerCamelCase						:       str				   =				BertModel(_lowercase     )

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

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


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			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__						:Optional[int]          =			parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











'''simple docstring'''

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


     """simple docstring"""
     # Return True if there is node that has not iterated.
     __lowerCamelCase						:       Optional[Any]				   =				[False] * len(_lowercase     )
     __lowerCamelCase						:       Dict				   =				[]
     queue.append(_lowercase     )
     __lowerCamelCase						:       Optional[int]				   =				True

     while queue:
          __lowerCamelCase						:       Optional[Any]				   =				queue.pop(0     )
          for ind in range(len(graph[u]     )     ):
               if visited[ind] is False and graph[u][ind] > 0:
                    queue.append(_lowercase     )
                    __lowerCamelCase						:       List[str]				   =				True
                    __lowerCamelCase						:       str				   =				u

     return visited[t]
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     # This array is filled by BFS and to store path
     __lowerCamelCase						:       Any				   =				[-1] * (len(_lowercase     ))
     __lowerCamelCase						:       Optional[int]				   =				0
     while bfs(_lowercase,						_lowercase,						_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[Any]				   =				float("""Inf"""     )
          __lowerCamelCase						:       Union[str, Any]				   =				sink

          while s != source:
               # Find the minimum value in select path
               __lowerCamelCase						:       Optional[int]				   =				min(_lowercase,						graph[parent[s]][s]     )
               __lowerCamelCase						:       Union[str, Any]				   =				parent[s]

          max_flow += path_flow
          __lowerCamelCase						:       str				   =				sink

          while v != source:
               __lowerCamelCase						:       List[Any]				   =				parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase						:       Dict				   =				parent[v]
     return max_flow


UpperCAmelCase__						:str          =			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__       ,				UpperCAmelCase__						:Optional[Any]          =			0, 5
print(ford_fulkerson(graph, source, sink))



