



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							       =			logging.get_logger(__name__)






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







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


     '''simple docstring'''







     logger.info(F"""Loading model based on config from {config_path}..."""       )
     a_       		=BertConfig.from_json_file(lowercase__       )
     a_       		=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__":
  lowercase							       =			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							       =			parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     print(F"""Vertex\tShortest Distance from vertex {src}"""       )
     for i, d in enumerate(lowercase__       ):
          print(F"""{i}\t\t{d}"""       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for j in range(lowercase__       ):
          a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])
          if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
               return True
     return False






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[float("inf"       )] * vertex_count
     a_       		=0.0

     for _ in range(vertex_count - 1       ):
          for j in range(lowercase__       ):
               a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])

               if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
                    a_       		=distance[u] + w

     a_       		=check_negative_cycle(lowercase__       ,	lowercase__       ,	lowercase__       )
     if negative_cycle_exists:
          raise Exception("Negative cycle found"       )

     return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowercase							       =			int(input('''Enter number of vertices: ''').strip())
  lowercase							       =			int(input('''Enter number of edges: ''').strip())

  lowercase							       =			[{} for _ in range(E)]

  for i in range(E):
    print('''Edge ''', i + 1)
    lowercase       ,      lowercase       ,      lowercase							       =			(
        int(x)
        for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
    )
    lowercase							       =			{'''src''': src, '''dst''': dest, '''weight''': weight}

  lowercase							       =			int(input('''\nEnter shortest path source:''').strip())
  lowercase							       =			bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)


