
"""simple docstring"""
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    str:
  def get_masked_lm_array(lowerCamelCase__							:							str				):
    _SCREAMING_SNAKE_CASE   :				List[str]							    = f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    _SCREAMING_SNAKE_CASE   :				int							    = tf.train.load_variable(lowerCamelCase__,						lowerCamelCase__				)

    if "kernel" in name:
      _SCREAMING_SNAKE_CASE   :				List[str]							    = array.transpose()

    return torch.from_numpy(lowerCamelCase__				)

  def get_encoder_array(lowerCamelCase__							:							str				):
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    _SCREAMING_SNAKE_CASE   :				Tuple							    = tf.train.load_variable(lowerCamelCase__,						lowerCamelCase__				)

    if "kernel" in name:
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = array.transpose()

    return torch.from_numpy(lowerCamelCase__				)

  def get_encoder_layer_array(lowerCamelCase__							:							int,						lowerCamelCase__							:							str				):
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    _SCREAMING_SNAKE_CASE   :				int							    = tf.train.load_variable(lowerCamelCase__,						lowerCamelCase__				)

    if "kernel" in name:
      _SCREAMING_SNAKE_CASE   :				Tuple							    = array.transpose()

    return torch.from_numpy(lowerCamelCase__				)

  def get_encoder_attention_layer_array(lowerCamelCase__							:							int,						lowerCamelCase__							:							str,						lowerCamelCase__							:							Any				):
    _SCREAMING_SNAKE_CASE   :				Tuple							    = f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.train.load_variable(lowerCamelCase__,						lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				str							    = array.reshape(lowerCamelCase__				)

    if "kernel" in name:
      _SCREAMING_SNAKE_CASE   :				List[str]							    = array.transpose()

    return torch.from_numpy(lowerCamelCase__				)

  print(f'''Loading model based on config from {config_path}...'''				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = BertConfig.from_json_file(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = BertForMaskedLM(lowerCamelCase__				)

  # Layers
  for layer_index in range(0,						config.num_hidden_layers				):
    _SCREAMING_SNAKE_CASE   :				BertLayer							    = model.bert.encoder.layer[layer_index]

    # Self-attention
    _SCREAMING_SNAKE_CASE   :				BertSelfAttention							    = layer.attention.self

    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_query_dense/kernel",						self_attn.query.weight.data.shape				)
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_query_dense/bias",						self_attn.query.bias.data.shape				)
    _SCREAMING_SNAKE_CASE   :				Dict							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_key_dense/kernel",						self_attn.key.weight.data.shape				)
    _SCREAMING_SNAKE_CASE   :				Any							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_key_dense/bias",						self_attn.key.bias.data.shape				)
    _SCREAMING_SNAKE_CASE   :				List[str]							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_value_dense/kernel",						self_attn.value.weight.data.shape				)
    _SCREAMING_SNAKE_CASE   :				Any							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_value_dense/bias",						self_attn.value.bias.data.shape				)

    # Self-attention Output
    _SCREAMING_SNAKE_CASE   :				BertSelfOutput							    = layer.attention.output

    _SCREAMING_SNAKE_CASE   :				Tuple							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_output_dense/kernel",						self_output.dense.weight.data.shape				)
    _SCREAMING_SNAKE_CASE   :				Tuple							    = get_encoder_attention_layer_array(
        lowerCamelCase__,						"_output_dense/bias",						self_output.dense.bias.data.shape				)

    _SCREAMING_SNAKE_CASE   :				Any							    = get_encoder_layer_array(lowerCamelCase__,						"_attention_layer_norm/gamma"				)
    _SCREAMING_SNAKE_CASE   :				str							    = get_encoder_layer_array(lowerCamelCase__,						"_attention_layer_norm/beta"				)

    # Intermediate
    _SCREAMING_SNAKE_CASE   :				BertIntermediate							    = layer.intermediate

    _SCREAMING_SNAKE_CASE   :				Any							    = get_encoder_layer_array(lowerCamelCase__,						"_intermediate_dense/kernel"				)
    _SCREAMING_SNAKE_CASE   :				Dict							    = get_encoder_layer_array(lowerCamelCase__,						"_intermediate_dense/bias"				)

    # Output
    _SCREAMING_SNAKE_CASE   :				BertOutput							    = layer.output

    _SCREAMING_SNAKE_CASE   :				Tuple							    = get_encoder_layer_array(lowerCamelCase__,						"_output_dense/kernel"				)
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = get_encoder_layer_array(lowerCamelCase__,						"_output_dense/bias"				)

    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = get_encoder_layer_array(lowerCamelCase__,						"_output_layer_norm/gamma"				)
    _SCREAMING_SNAKE_CASE   :				Tuple							    = get_encoder_layer_array(lowerCamelCase__,						"_output_layer_norm/beta"				)

  # Embeddings
  _SCREAMING_SNAKE_CASE   :				int							    = get_encoder_array("_position_embedding_layer/embeddings"				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = get_encoder_array("_type_embedding_layer/embeddings"				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = get_encoder_array("_embedding_norm_layer/gamma"				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = get_encoder_array("_embedding_norm_layer/beta"				)

  # LM Head
  _SCREAMING_SNAKE_CASE   :				Dict							    = model.cls.predictions.transform

  _SCREAMING_SNAKE_CASE   :				int							    = get_masked_lm_array("dense/kernel"				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = get_masked_lm_array("dense/bias"				)

  _SCREAMING_SNAKE_CASE   :				List[str]							    = get_masked_lm_array("layer_norm/gamma"				)
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = get_masked_lm_array("layer_norm/beta"				)

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = get_masked_lm_array("embedding_table"				)

  # Pooling
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = BertPooler(config=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				BertPooler							    = get_encoder_array("_pooler_layer/kernel"				)
  _SCREAMING_SNAKE_CASE   :				BertPooler							    = get_encoder_array("_pooler_layer/bias"				)

  # Export final model
  model.save_pretrained(lowerCamelCase__				)

  # Integration test - should load without any errors ;)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = BertForMaskedLM.from_pretrained(lowerCamelCase__				)
  print(new_model.eval()				)

  print("Model conversion was done sucessfully!"				)


if __name__ == "__main__":
      lowercase_       :						int     					=	argparse.ArgumentParser()
      parser.add_argument(
          '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow Token Dropping 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.''',
      )
      lowercase_       :						List[str]     					=	parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





"""simple docstring"""
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Union[str, Any]:
  print("\nThe shortest path matrix using Floyd Warshall algorithm\n"				)
  for i in range(lowerCamelCase__				):
    for j in range(lowerCamelCase__				):
      if dist[i][j] != float("inf"				):
        print(int(dist[i][j]				),						end="\t"				)
      else:
        print("INF",						end="\t"				)
    print()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Any				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = [[float("inf"				) for _ in range(lowerCamelCase__				)] for _ in range(lowerCamelCase__				)]

  for i in range(lowerCamelCase__				):
    for j in range(lowerCamelCase__				):
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = graph[i][j]

      # check vertex k against all other vertices (i, j)
  for k in range(lowerCamelCase__				):
    # looping through rows of graph array
    for i in range(lowerCamelCase__				):
      # looping through columns of graph array
      for j in range(lowerCamelCase__				):
        if (
            dist[i][k] != float("inf"				)
            and dist[k][j] != float("inf"				)
            and dist[i][k] + dist[k][j] < dist[i][j]
        ):
          _SCREAMING_SNAKE_CASE   :				List[Any]							    = dist[i][k] + dist[k][j]

  _print_dist(lowerCamelCase__,						lowerCamelCase__				)
  return dist, v


if __name__ == "__main__":
      lowercase_       :						Tuple     					=	int(input('''Enter number of vertices: '''))
      lowercase_       :						List[Any]     					=	int(input('''Enter number of edges: '''))

      lowercase_       :						Optional[Any]     					=	[[float('''inf''') for i in range(v)] for j in range(v)]

      for i in range(v):
            lowercase_       :						Tuple     					=	0.0

            # src and dst are indices that must be within the array size graph[e][v]
            # failure to follow this will result in an error
      for i in range(e):
            print('''\nEdge ''', i + 1)
            lowercase_       :						str     					=	int(input('''Enter source:'''))
            lowercase_       :						Optional[Any]     					=	int(input('''Enter destination:'''))
            lowercase_       :						Union[str, Any]     					=	float(input('''Enter weight:'''))
            lowercase_       :						str     					=	weight

      floyd_warshall(graph, v)

      # Example Input
      # Enter number of vertices: 3
      # Enter number of edges: 2

      # # generated graph from vertex and edge inputs
      # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
      # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

      # specify source, destination and weight for edge #1
      # Edge  1
      # Enter source:1
      # Enter destination:2
      # Enter weight:2

      # specify source, destination and weight for edge #2
      # Edge  2
      # Enter source:2
      # Enter destination:1
      # Enter weight:1

      # # Expected Output from the vertice, edge and src, dst, weight inputs!!
      # 0		INF	INF
      # INF	0	2
      # INF	1	0



