



'''simple docstring'''



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_SCREAMING_SNAKE_CASE					=    getLogger(__name__)


_SCREAMING_SNAKE_CASE					=    '''cuda''' if torch.cuda.is_available() else '''cpu'''




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  int = 8		,       lowerCamelCase_			:  str = DEFAULT_DEVICE		,       lowerCamelCase_			:  Optional[Any]=False		,       lowerCamelCase_			:  Union[str, Any]="summarization"		,       lowerCamelCase_			:  Tuple=None		,       **lowerCamelCase_			:  Optional[int]		,       ):
			__lowercase										=							Path(lowerCamelCase_				).open('''w'''		,       encoding='''utf-8'''				)
			__lowercase										=							str(lowerCamelCase_				)
			__lowercase										=							AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_				).to(lowerCamelCase_				)
			if fpaa:
						__lowercase										=							model.half()

			__lowercase										=							AutoTokenizer.from_pretrained(lowerCamelCase_				)
			logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"				)  # if this is wrong, check config.model_type.

			__lowercase										=							time.time()
			# update config with task specific params
			use_task_specific_params(lowerCamelCase_		,       lowerCamelCase_				)
			if prefix is None:
						__lowercase										=							prefix or getattr(model.config		,       '''prefix'''		,       ''''''				) or ''''''
			for examples_chunk in tqdm(list(chunks(lowerCamelCase_		,       lowerCamelCase_				)				)				):
						__lowercase										=							[prefix + text for text in examples_chunk]
						__lowercase										=							tokenizer(lowerCamelCase_		,       return_tensors='''pt'''		,       truncation=lowerCamelCase_		,       padding='''longest'''				).to(lowerCamelCase_				)
						__lowercase										=							model.generate(
						    input_ids=batch.input_ids		,       attention_mask=batch.attention_mask		,       **lowerCamelCase_		,       )
						__lowercase										=							tokenizer.batch_decode(lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		,       clean_up_tokenization_spaces=lowerCamelCase_				)
						for hypothesis in dec:
									fout.write(hypothesis + '''\n'''				)
									fout.flush()
			fout.close()
			__lowercase										=							int(time.time() - start_time				)  # seconds
			__lowercase										=							len(lowerCamelCase_				)
			return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,       4				)}




def    _lowerCAmelCase						(				):
			return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''				)




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]=True				):
			__lowercase										=							argparse.ArgumentParser()
			parser.add_argument('''model_name'''		,       type=lowerCamelCase_		,       help='''like facebook/bart-large-cnn,t5-base, etc.'''				)
			parser.add_argument('''input_path'''		,       type=lowerCamelCase_		,       help='''like cnn_dm/test.source'''				)
			parser.add_argument('''save_path'''		,       type=lowerCamelCase_		,       help='''where to save summaries'''				)
			parser.add_argument('''--reference_path'''		,       type=lowerCamelCase_		,       required=lowerCamelCase_		,       help='''like cnn_dm/test.target'''				)
			parser.add_argument('''--score_path'''		,       type=lowerCamelCase_		,       required=lowerCamelCase_		,       default='''metrics.json'''		,       help='''where to save metrics'''				)
			parser.add_argument('''--device'''		,       type=lowerCamelCase_		,       required=lowerCamelCase_		,       default=lowerCamelCase_		,       help='''cuda, cuda:1, cpu etc.'''				)
			parser.add_argument(
			    '''--prefix'''		,       type=lowerCamelCase_		,       required=lowerCamelCase_		,       default=lowerCamelCase_		,       help='''will be added to the begininng of src examples'''				)
			parser.add_argument('''--task'''		,       type=lowerCamelCase_		,       default='''summarization'''		,       help='''used for task_specific_params + metrics'''				)
			parser.add_argument('''--bs'''		,       type=lowerCamelCase_		,       default=8		,       required=lowerCamelCase_		,       help='''batch size'''				)
			parser.add_argument(
			    '''--n_obs'''		,       type=lowerCamelCase_		,       default=-1		,       required=lowerCamelCase_		,       help='''How many observations. Defaults to all.'''				)
			parser.add_argument('''--fp16'''		,       action='''store_true'''				)
			parser.add_argument('''--dump-args'''		,       action='''store_true'''		,       help='''print the custom hparams with the results'''				)
			parser.add_argument(
			    '''--info'''		,       nargs='''?'''		,       type=lowerCamelCase_		,       const=datetime_now()		,       help=(
			        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
			        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
			    )		,       )
			# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
			__lowercase						,		__lowercase										=							parser.parse_known_args()
			__lowercase										=							parse_numeric_n_bool_cl_kwargs(lowerCamelCase_				)
			if parsed_args and verbose:
						print(f"parsed the following generate kwargs: {parsed_args}"				)
			__lowercase										=							[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path				).readlines()]
			if args.n_obs > 0:
						__lowercase										=							examples[: args.n_obs]
			Path(args.save_path				).parent.mkdir(exist_ok=lowerCamelCase_				)

			if args.reference_path is None and Path(args.score_path				).exists():
						warnings.warn(f"score_path {args.score_path} will be overwritten unless you type ctrl-c."				)

			if args.device == "cpu" and args.fpaa:
						# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
						raise ValueError('''Can\'t mix --fp16 and --device cpu'''				)

			__lowercase										=							generate_summaries_or_translations(
			    lowerCamelCase_		,       args.save_path		,       args.model_name		,       batch_size=args.bs		,       device=args.device		,       fpaa=args.fpaa		,       task=args.task		,       prefix=args.prefix		,       **lowerCamelCase_		,       )

			if args.reference_path is None:
						return {}

			# Compute scores
			__lowercase										=							calculate_bleu if '''translation''' in args.task else calculate_rouge
			__lowercase										=							[x.rstrip() for x in open(args.save_path				).readlines()]
			__lowercase										=							[x.rstrip() for x in open(args.reference_path				).readlines()][: len(lowerCamelCase_				)]
			__lowercase										=							score_fn(lowerCamelCase_		,       lowerCamelCase_				)
			scores.update(lowerCamelCase_				)

			if args.dump_args:
						scores.update(lowerCamelCase_				)
			if args.info:
						__lowercase										=							args.info

			if verbose:
						print(lowerCamelCase_				)

			if args.score_path is not None:
						json.dump(lowerCamelCase_		,       open(args.score_path		,       '''w'''				)				)

			return scores


if __name__ == "__main__":
	# Usage for MT:
	# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
	run_generate(verbose=True)












'''simple docstring'''



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_SCREAMING_SNAKE_CASE					=    0

_SCREAMING_SNAKE_CASE					=    [
    [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],
]

_SCREAMING_SNAKE_CASE					=    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

_SCREAMING_SNAKE_CASE					=    tuple[int, int]






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							pos_x
				__lowercase										=							pos_y
				__lowercase										=							(pos_y, pos_x)
				__lowercase										=							goal_x
				__lowercase										=							goal_y
				__lowercase										=							g_cost
				__lowercase										=							parent
				__lowercase										=							self.calculate_heuristic()
				__lowercase										=							self.g_cost + self.h_cost


	def 						_UpperCAmelCase     (self )							->							float:







				'''simple docstring'''





				__lowercase										=							self.pos_x - self.goal_x
				__lowercase										=							self.pos_y - self.goal_y
				if HEURISTIC == 1:
							return abs(_lowerCamelCase ) + abs(_lowerCamelCase )
				else:
							return sqrt(dy**2 + dx**2 )


	def __lt__(self     ,_lowerCamelCase )							->							bool:







				'''simple docstring'''





				return self.f_cost < other.f_cost






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							Node(start[1]     ,start[0]     ,goal[1]     ,goal[0]     ,0     ,_lowerCamelCase )
				__lowercase										=							Node(goal[1]     ,goal[0]     ,goal[1]     ,goal[0]     ,99999     ,_lowerCamelCase )

				__lowercase										=							[self.start]
				__lowercase										=							[]

				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





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

							if current_node.pos == self.target.pos:
										return self.retrace_path(_lowerCamelCase )

							self.closed_nodes.append(_lowerCamelCase )
							__lowercase										=							self.get_successors(_lowerCamelCase )

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

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

				return [self.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[Node]:







				'''simple docstring'''





				__lowercase										=							[]
				for action in delta:
							__lowercase										=							parent.pos_x + action[1]
							__lowercase										=							parent.pos_y + action[0]
							if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_lowerCamelCase ) - 1):
										continue

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

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





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






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase )							->							None:







				'''simple docstring'''





				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							AStar(_lowerCamelCase     ,_lowerCamelCase )
				__lowercase										=							False


	def 						_UpperCAmelCase     (self )							->							list[TPosition]:







				'''simple docstring'''





				while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
							self.fwd_astar.open_nodes.sort()
							self.bwd_astar.open_nodes.sort()
							__lowercase										=							self.fwd_astar.open_nodes.pop(0 )
							__lowercase										=							self.bwd_astar.open_nodes.pop(0 )

							if current_bwd_node.pos == current_fwd_node.pos:
										return self.retrace_bidirectional_path(
										    _lowerCamelCase     ,_lowerCamelCase )

							self.fwd_astar.closed_nodes.append(_lowerCamelCase )
							self.bwd_astar.closed_nodes.append(_lowerCamelCase )

							__lowercase										=							current_bwd_node
							__lowercase										=							current_fwd_node

							__lowercase										=							{
							    self.fwd_astar: self.fwd_astar.get_successors(_lowerCamelCase ),
							    self.bwd_astar: self.bwd_astar.get_successors(_lowerCamelCase ),
							}

							for astar in [self.fwd_astar, self.bwd_astar]:
										for child_node in successors[astar]:
													if child_node in astar.closed_nodes:
																continue

													if child_node not in astar.open_nodes:
																astar.open_nodes.append(_lowerCamelCase )
													else:
																# retrieve the best current path
																__lowercase										=							astar.open_nodes.pop(
																    astar.open_nodes.index(_lowerCamelCase ) )

																if child_node.g_cost < better_node.g_cost:
																			astar.open_nodes.append(_lowerCamelCase )
																else:
																			astar.open_nodes.append(_lowerCamelCase )

				return [self.fwd_astar.start.pos]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							list[TPosition]:







				'''simple docstring'''





				__lowercase										=							self.fwd_astar.retrace_path(_lowerCamelCase )
				__lowercase										=							self.bwd_astar.retrace_path(_lowerCamelCase )
				bwd_path.pop()
				bwd_path.reverse()
				__lowercase										=							fwd_path + bwd_path
				return path


if __name__ == "__main__":
	# all coordinates are given in format [y,x]
	_SCREAMING_SNAKE_CASE					=    (0, 0)
	_SCREAMING_SNAKE_CASE					=    (len(grid) - 1, len(grid[0]) - 1)
	for elem in grid:
		print(elem)

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    AStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    a_star.search()
	_SCREAMING_SNAKE_CASE					=    time.time() - start_time
	print(f'''AStar execution time = {end_time:f} seconds''')

	_SCREAMING_SNAKE_CASE					=    time.time()
	_SCREAMING_SNAKE_CASE					=    BidirectionalAStar(init, goal)
	_SCREAMING_SNAKE_CASE					=    time.time() - bd_start_time
	print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





