
"""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						=			logging.get_logger(__name__)
def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      int          =   os.path.abspath(a_      )
		logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""      )
		# Load weights from TF model
		lowerCamelCase					:      Any          =   tf.train.list_variables(a_      )
		lowerCamelCase					:      Dict          =   []
		lowerCamelCase					:      List[str]          =   []
		lowerCamelCase					:      List[Any]          =   []
		for full_name, shape in init_vars:
				# logger.info(f"Loading TF weight {name} with shape {shape}")
				lowerCamelCase					:      List[str]          =   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					:      Optional[Any]          =   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(a_      )
				# read data
				lowerCamelCase					:      str          =   tf.train.load_variable(a_,						a_      )
				names.append('/'.join(a_      )      )
				arrays.append(a_      )
		logger.info(F"""Read a total of {len(a_      ):,} layers"""      )

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





		'''simple docstring'''


		logger.info(F"""Loading model based on config from {config_path}..."""      )
		lowerCamelCase					:      List[str]          =   BertConfig.from_json_file(a_      )
		lowerCamelCase					:      Optional[Any]          =   BertModel(a_      )

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

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


if __name__ == "__main__":
					_A						=			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						=			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

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

		lowerCamelCase					:      List[str]          =   init[0]
		lowerCamelCase					:      Optional[Any]          =   init[1]
		lowerCamelCase					:      List[Any]          =   0
		lowerCamelCase					:      List[str]          =   g + heuristic[x][y]  # cost from starting cell to destination cell
		lowerCamelCase					:      Union[str, Any]          =   [[f, g, x, y]]

		lowerCamelCase					:      Union[str, Any]          =   False  # flag that is set when search is complete
		lowerCamelCase					:      str          =   False  # flag set if we can't find expand

		while not found and not resign:
				if len(a_      ) == 0:
						raise ValueError('Algorithm is unable to find solution'      )
				else:  # to choose the least costliest action so as to move closer to the goal
						cell.sort()
						cell.reverse()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

						if x == goal[0] and y == goal[1]:
								lowerCamelCase					:      Any          =   True
						else:
								for i in range(len(a_      )      ):  # to try out different valid actions
										lowerCamelCase					:      Tuple          =   x + DIRECTIONS[i][0]
										lowerCamelCase					:      Union[str, Any]          =   y + DIRECTIONS[i][1]
										if xa >= 0 and xa < len(a_      ) and ya >= 0 and ya < len(grid[0]      ):
												if closed[xa][ya] == 0 and grid[xa][ya] == 0:
														lowerCamelCase					:      str          =   g + cost
														lowerCamelCase					:      Tuple          =   ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]      )
														lowerCamelCase					:      Union[str, Any]          =   1
														lowerCamelCase					:      Any          =   i
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Optional[int]          =   goal[0]
		lowerCamelCase					:      Dict          =   goal[1]
		invpath.append([x, y]      )  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				lowerCamelCase					:      Dict          =   x - DIRECTIONS[action[x][y]][0]
				lowerCamelCase					:      Dict          =   y - DIRECTIONS[action[x][y]][1]
				lowerCamelCase					:      Optional[Any]          =   xa
				lowerCamelCase					:      Union[str, Any]          =   ya
				invpath.append([x, y]      )

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 1, 0],
					    [0, 0, 0, 0, 1, 0],
					]

					_A						=			[0, 0]
					# all coordinates are given in format [y,x]
					_A						=			[len(grid) - 1, len(grid[0]) - 1]
					_A						=			1

					# the cost map which pushes the path closer to the goal
					_A						=			[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
					for i in range(len(grid)):
										for j in range(len(grid[0])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			search(grid, init, goal, cost, heuristic)

					print('ACTION MAP')
					for i in range(len(action)):
										print(action[i])

					for i in range(len(path)):
										print(path[i])






