





'''simple docstring'''




import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
			import tensorflow as tf
			from tensorflow.python.framework.errors_impl import ResourceExhaustedError

			from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
			import pyanvml.pyanvml as nvml

a__					:			Optional[Any]          =     logging.get_logger(__name__)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	def run_func(UpperCAmelCase_							):
		@wraps(UpperCAmelCase_							)
		def run_in_eager_mode(*UpperCAmelCase_					,				**UpperCAmelCase_							):
			return func(*UpperCAmelCase_					,				**UpperCAmelCase_							)

		@wraps(UpperCAmelCase_							)
		@tf.function(experimental_compile=UpperCAmelCase_							)
		def run_in_graph_mode(*UpperCAmelCase_					,				**UpperCAmelCase_							):
			return func(*UpperCAmelCase_					,				**UpperCAmelCase_							)

		if do_eager_mode is True:
			if use_xla is not False:
				raise ValueError(
				    'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'							)
			return run_in_eager_mode
		else:
			return run_in_graph_mode

	return run_func




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->["tf.Tensor"]:
	snake_case__	       =   random.Random()
	snake_case__	       =   [rng.randint(0					,				vocab_size - 1							) for i in range(batch_size * sequence_length							)]
	return tf.constant(UpperCAmelCase_					,				shape=(batch_size, sequence_length)					,				dtype=tf.intaa							)







class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  "TensorFlow"





	@property
	def   _snake_case   (		self		)      ->		int:
		return tf.__version__





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		float:
		# initialize GPU on separate process
		snake_case__	       =   self.args.strategy
		if strategy is None:
			raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
		snake_case__	       =   self._prepare_inference_func(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		return self._measure_speed(_inference		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		float:
		snake_case__	       =   self.args.strategy
		if strategy is None:
			raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
		snake_case__	       =   self._prepare_train_func(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		return self._measure_speed(_train		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		[Memory, Optional[MemorySummary]]:
		# initialize GPU on separate process
		if self.args.is_gpu:
			tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      ,       UpperCamelCase_		)
		snake_case__	       =   self.args.strategy
		if strategy is None:
			raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
		snake_case__	       =   self._prepare_inference_func(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		return self._measure_memory(_inference		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		[Memory, Optional[MemorySummary]]:
		if self.args.is_gpu:
			tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]      ,       UpperCamelCase_		)
		snake_case__	       =   self.args.strategy
		if strategy is None:
			raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)

		snake_case__	       =   self._prepare_train_func(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)
		return self._measure_memory(_train		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Callable[[], None]:
		snake_case__	       =   self.config_dict[model_name]

		if self.args.fpaa:
			raise NotImplementedError('Mixed precision is currently not supported.'		)

		snake_case__	       =   (
		    hasattr(UpperCamelCase_      ,       'architectures'		)
		    and isinstance(config.architectures      ,       UpperCamelCase_		)
		    and len(config.architectures		) > 0
		)
		if not self.args.only_pretrain_model and has_model_class_in_config:
			try:
				snake_case__	       =   'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
				snake_case__	       =   __import__('transformers'      ,       fromlist=[model_class]		)
				snake_case__	       =   getattr(UpperCamelCase_      ,       UpperCamelCase_		)
				snake_case__	       =   model_cls(UpperCamelCase_		)
			except ImportError:
				raise ImportError(
				    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
				    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'		)
		else:
			snake_case__	       =   TF_MODEL_MAPPING[config.__class__](UpperCamelCase_		)

		# encoder-decoder has vocab size saved differently
		snake_case__	       =   config.vocab_size if hasattr(UpperCamelCase_      ,       'vocab_size'		) else config.encoder.vocab_size
		snake_case__	       =   random_input_ids(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)

		@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla		)
		def encoder_decoder_forward():
			return model(UpperCamelCase_      ,       decoder_input_ids=UpperCamelCase_      ,       training=UpperCamelCase_		)

		@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla		)
		def encoder_forward():
			return model(UpperCamelCase_      ,       training=UpperCamelCase_		)

		snake_case__	       =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

		return _inference





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Callable[[], None]:
		snake_case__	       =   self.config_dict[model_name]

		if self.args.eager_mode is not False:
			raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'		)

		if self.args.fpaa:
			raise NotImplementedError('Mixed precision is currently not supported.'		)

		snake_case__	       =   (
		    hasattr(UpperCamelCase_      ,       'architectures'		)
		    and isinstance(config.architectures      ,       UpperCamelCase_		)
		    and len(config.architectures		) > 0
		)
		if not self.args.only_pretrain_model and has_model_class_in_config:
			try:
				snake_case__	       =   'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
				snake_case__	       =   __import__('transformers'      ,       fromlist=[model_class]		)
				snake_case__	       =   getattr(UpperCamelCase_      ,       UpperCamelCase_		)
				snake_case__	       =   model_cls(UpperCamelCase_		)
			except ImportError:
				raise ImportError(
				    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
				    ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'		)
		else:
			snake_case__	       =   TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase_		)

		# encoder-decoder has vocab size saved differently
		snake_case__	       =   config.vocab_size if hasattr(UpperCamelCase_      ,       'vocab_size'		) else config.encoder.vocab_size
		snake_case__	       =   random_input_ids(UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)

		@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla		)
		def encoder_decoder_train():
			snake_case__	       =   model(UpperCamelCase_      ,       decoder_input_ids=UpperCamelCase_      ,       labels=UpperCamelCase_      ,       training=UpperCamelCase_		)[0]
			snake_case__	       =   tf.gradients(UpperCamelCase_      ,       model.trainable_variables		)
			return gradients

		@run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla		)
		def encoder_train():
			snake_case__	       =   model(UpperCamelCase_      ,       labels=UpperCamelCase_      ,       training=UpperCamelCase_		)[0]
			snake_case__	       =   tf.gradients(UpperCamelCase_      ,       model.trainable_variables		)
			return gradients

		snake_case__	       =   encoder_decoder_train if config.is_encoder_decoder else encoder_train

		return _train





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		float:
		with self.args.strategy.scope():
			try:
				if self.args.is_tpu or self.args.use_xla:
					# run additional 10 times to stabilize compilation for tpu
					logger.info('Do inference on TPU. Running model 5 times to stabilize compilation'		)
					timeit.repeat(UpperCamelCase_      ,       repeat=1      ,       number=5		)

				# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
				snake_case__	       =   timeit.repeat(
				    UpperCamelCase_      ,       repeat=self.args.repeat      ,       number=10      ,       )

				return min(UpperCamelCase_		) / 1_0.0
			except ResourceExhaustedError as e:
				self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		[Memory, MemorySummary]:
		logger.info(
		    'Note that TensorFlow allocates more memory than '
		    'it might need to speed up computation. '
		    'The memory reported here corresponds to the memory '
		    'reported by `nvidia-smi`, which can vary depending '
		    'on total available memory on the GPU that is used.'		)
		with self.args.strategy.scope():
			try:
				if self.args.trace_memory_line_by_line:
					if not self.args.eager_mode:
						raise ValueError(
						    '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
						    ' consumption line by line.'		)
					snake_case__	       =   start_memory_tracing('transformers'		)

				if self.args.is_tpu:
					# tpu
					raise NotImplementedError(
					    'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
					    ' with `args.memory=False`'		)
				elif self.args.is_gpu:
					# gpu
					if not is_pyanvml_available():
						logger.warning(
						    'py3nvml not installed, we won\'t log GPU memory usage. '
						    'Install py3nvml (pip install py3nvml) to log information about GPU.'		)
						snake_case__	       =   'N/A'
					else:
						logger.info(
						    'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
						    ' running on the same GPU.'		)
						# init nvml
						nvml.nvmlInit()
						func()
						snake_case__	       =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
						snake_case__	       =   nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase_		)
						snake_case__	       =   meminfo.used
						snake_case__	       =   Memory(UpperCamelCase_		)
						# shutdown nvml
						nvml.nvmlShutdown()
				else:
					# cpu
					if self.args.trace_memory_line_by_line:
						logger.info(
						    'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
						    ' TensorFlow.'		)
						snake_case__	       =   None
					else:
						snake_case__	       =   measure_peak_memory_cpu(UpperCamelCase_		)
						snake_case__	       =   Memory(UpperCamelCase_		) if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) else memory_bytes
				if self.args.trace_memory_line_by_line:
					snake_case__	       =   stop_memory_tracing(UpperCamelCase_		)
					if memory is None:
						snake_case__	       =   summary.total
				else:
					snake_case__	       =   None

				return memory, summary
			except ResourceExhaustedError as e:
				self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)
				return "N/A", None










'''simple docstring'''




from __future__ import annotations

a__					:			Optional[int]          =     list[tuple[int, int]]

a__					:			List[Any]          =     [
    [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],
]

a__					:			Optional[int]          =     ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       )      ->		Any:
		snake_case__	       =   pos_x
		snake_case__	       =   pos_y
		snake_case__	       =   (pos_y, pos_x)
		snake_case__	       =   goal_x
		snake_case__	       =   goal_y
		snake_case__	       =   g_cost
		snake_case__	       =   parent
		snake_case__	       =   self.calculate_heuristic()





	def   _snake_case   (		self		)      ->		float:
		snake_case__	       =   abs(self.pos_x - self.goal_x		)
		snake_case__	       =   abs(self.pos_y - self.goal_y		)
		return dx + dy





	def __lt__(		self      ,       UpperCamelCase_		)      ->		bool:
		return self.f_cost < other.f_cost






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Any:
		snake_case__	       =   Node(start[1]      ,       start[0]      ,       goal[1]      ,       goal[0]      ,       0      ,       UpperCamelCase_		)
		snake_case__	       =   Node(goal[1]      ,       goal[0]      ,       goal[1]      ,       goal[0]      ,       9_9999      ,       UpperCamelCase_		)

		snake_case__	       =   [self.start]
		snake_case__	       =   []

		snake_case__	       =   False





	def   _snake_case   (		self		)      ->		Path | None:
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			snake_case__	       =   self.open_nodes.pop(0		)

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

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

			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(UpperCamelCase_		)
				else:
					# retrieve the best current path
					snake_case__	       =   self.open_nodes.pop(self.open_nodes.index(UpperCamelCase_		)		)

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		list[Node]:
		snake_case__	       =   []
		for action in delta:
			snake_case__	       =   parent.pos_x + action[1]
			snake_case__	       =   parent.pos_y + action[0]

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

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

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





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Path:
		snake_case__	       =   node
		snake_case__	       =   []
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)		)
			snake_case__	       =   current_node.parent
		path.reverse()
		return path


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

			print('''------''')

			a__					:			Optional[int]          =     GreedyBestFirst(init, goal)
			a__					:			Optional[int]          =     greedy_bf.search()
			if path:
						for pos_x, pos_y in path:
									a__					:			Tuple          =     2

						for elem in grid:
									print(elem)



