
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 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     str			,							UpperCamelCase:     str  ):






 """simple docstring"""


 def get_masked_lm_array(UpperCamelCase:     str  ):
  __lowerCAmelCase         =							F"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  __lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )

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

  return torch.from_numpy(UpperCamelCase  )

 def get_encoder_array(UpperCamelCase:     str  ):
  __lowerCAmelCase         =							F"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  __lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )

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

  return torch.from_numpy(UpperCamelCase  )

 def get_encoder_layer_array(UpperCamelCase:     int			,							UpperCamelCase:     str  ):
  __lowerCAmelCase         =							F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  __lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )

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

  return torch.from_numpy(UpperCamelCase  )

 def get_encoder_attention_layer_array(UpperCamelCase:     int			,							UpperCamelCase:     str			,							UpperCamelCase:     Any  ):
  __lowerCAmelCase         =							F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
  __lowerCAmelCase         =							tf.train.load_variable(UpperCamelCase			,							UpperCamelCase  )
  __lowerCAmelCase         =							array.reshape(UpperCamelCase  )

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

  return torch.from_numpy(UpperCamelCase  )

 print(F"Loading model based on config from {config_path}..."  )
 __lowerCAmelCase         =							BertConfig.from_json_file(UpperCamelCase  )
 __lowerCAmelCase         =							BertForMaskedLM(UpperCamelCase  )

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

  # Self-attention
  __lowerCAmelCase         =							layer.attention.self

  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_query_dense/kernel"			,							self_attn.query.weight.data.shape  )
  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_query_dense/bias"			,							self_attn.query.bias.data.shape  )
  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_key_dense/kernel"			,							self_attn.key.weight.data.shape  )
  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_key_dense/bias"			,							self_attn.key.bias.data.shape  )
  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_value_dense/kernel"			,							self_attn.value.weight.data.shape  )
  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_value_dense/bias"			,							self_attn.value.bias.data.shape  )

  # Self-attention Output
  __lowerCAmelCase         =							layer.attention.output

  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_output_dense/kernel"			,							self_output.dense.weight.data.shape  )
  __lowerCAmelCase         =							get_encoder_attention_layer_array(
      UpperCamelCase			,							"_output_dense/bias"			,							self_output.dense.bias.data.shape  )

  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_attention_layer_norm/gamma"  )
  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_attention_layer_norm/beta"  )

  # Intermediate
  __lowerCAmelCase         =							layer.intermediate

  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_intermediate_dense/kernel"  )
  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_intermediate_dense/bias"  )

  # Output
  __lowerCAmelCase         =							layer.output

  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_dense/kernel"  )
  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_dense/bias"  )

  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_layer_norm/gamma"  )
  __lowerCAmelCase         =							get_encoder_layer_array(UpperCamelCase			,							"_output_layer_norm/beta"  )

 # Embeddings
 __lowerCAmelCase         =							get_encoder_array("_position_embedding_layer/embeddings"  )
 __lowerCAmelCase         =							get_encoder_array("_type_embedding_layer/embeddings"  )
 __lowerCAmelCase         =							get_encoder_array("_embedding_norm_layer/gamma"  )
 __lowerCAmelCase         =							get_encoder_array("_embedding_norm_layer/beta"  )

 # LM Head
 __lowerCAmelCase         =							model.cls.predictions.transform

 __lowerCAmelCase         =							get_masked_lm_array("dense/kernel"  )
 __lowerCAmelCase         =							get_masked_lm_array("dense/bias"  )

 __lowerCAmelCase         =							get_masked_lm_array("layer_norm/gamma"  )
 __lowerCAmelCase         =							get_masked_lm_array("layer_norm/beta"  )

 __lowerCAmelCase         =							get_masked_lm_array("embedding_table"  )

 # Pooling
 __lowerCAmelCase         =							BertPooler(config=UpperCamelCase  )
 __lowerCAmelCase         =							get_encoder_array("_pooler_layer/kernel"  )
 __lowerCAmelCase         =							get_encoder_array("_pooler_layer/bias"  )

 # Export final model
 model.save_pretrained(UpperCamelCase  )

 # Integration test - should load without any errors ;)
 __lowerCAmelCase         =							BertForMaskedLM.from_pretrained(UpperCamelCase  )
 print(new_model.eval()  )

 print("Model conversion was done sucessfully!"  )


if __name__ == "__main__":
     UpperCamelCase_   	=	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.",
     )
     UpperCamelCase_   	=	parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

UpperCamelCase_   	=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							pos_x
								__lowerCAmelCase         =							pos_y
								__lowerCAmelCase         =							(pos_y, pos_x)
								__lowerCAmelCase         =							goal_x
								__lowerCAmelCase         =							goal_y
								__lowerCAmelCase         =							g_cost
								__lowerCAmelCase         =							parent
								__lowerCAmelCase         =							self.calculate_heuristic()







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							abs(self.pos_x - self.goal_x )
								__lowerCAmelCase         =							abs(self.pos_y - self.goal_y )
								return dx + dy







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__lowerCAmelCase         =							self.open_nodes.pop(0 )

									if current_node.pos == self.target.pos:
										__lowerCAmelCase         =							True
										return self.retrace_path(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

									for child_node in successors:
										if child_node in self.closed_nodes:
											continue

										if child_node not in self.open_nodes:
											self.open_nodes.append(snake_case__ )
										else:
											# retrieve the best current path
											__lowerCAmelCase         =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

											if child_node.g_cost < better_node.g_cost:
												self.open_nodes.append(snake_case__ )
											else:
												self.open_nodes.append(snake_case__ )

								if not self.reached:
									return [self.start.pos]
								return None







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Node ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								for action in delta:
									__lowerCAmelCase         =							parent.pos_x + action[1]
									__lowerCAmelCase         =							parent.pos_y + action[0]

									if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case__ ) - 1):
										continue

									if grid[pos_y][pos_x] != 0:
										continue

									successors.append(
									    Node(
									        snake_case__      ,				snake_case__      ,				self.target.pos_y      ,				self.target.pos_x      ,				parent.g_cost + 1      ,				snake_case__      ,				) )
								return successors







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""simple docstring"""







								__lowerCAmelCase         =							node
								__lowerCAmelCase         =							[]
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x) )
									__lowerCAmelCase         =							current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
					UpperCamelCase_   	=	(0, 0)
					UpperCamelCase_   	=	(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("------")

					UpperCamelCase_   	=	GreedyBestFirst(init, goal)
					UpperCamelCase_   	=	greedy_bf.search()
					if path:
										for pos_x, pos_y in path:
															UpperCamelCase_   	=	2

										for elem in grid:
															print(elem)






