
"""simple docstring"""







import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = True	,  _UpperCamelCase = math.inf	,  _UpperCamelCase = -math.inf	,  _UpperCamelCase = math.inf	,  _UpperCamelCase = -math.inf	,  _UpperCamelCase = False	,  _UpperCamelCase = 100	,  _UpperCamelCase = 0.01	,  _UpperCamelCase = 1	,  ):
	__lowerCAmelCase      :						Any						      =				False
	__lowerCAmelCase      :						Dict						      =				search_prob
	__lowerCAmelCase      :						List[Any]						      =				start_temperate
	__lowerCAmelCase      :						Dict						      =				[]
	__lowerCAmelCase      :						Optional[int]						      =				0
	__lowerCAmelCase      :						Union[str, Any]						      =				None

	while not search_end:
		__lowerCAmelCase      :						Tuple						      =				current_state.score()
		if best_state is None or current_score > best_state.score():
			__lowerCAmelCase      :						Optional[Any]						      =				current_state
		scores.append(_UpperCamelCase		)
		iterations += 1
		__lowerCAmelCase      :						int						      =				None
		__lowerCAmelCase      :						Union[str, Any]						      =				current_state.get_neighbors()
		while (
		    next_state is None and neighbors
		):  # till we do not find a neighbor that we can move to
			__lowerCAmelCase      :						str						      =				random.randint(0	,  len(_UpperCamelCase		) - 1		)  # picking a random neighbor
			__lowerCAmelCase      :						Optional[int]						      =				neighbors.pop(_UpperCamelCase		)
			__lowerCAmelCase      :						int						      =				picked_neighbor.score() - current_score

			if (
			    picked_neighbor.x > max_x
			    or picked_neighbor.x < min_x
			    or picked_neighbor.y > max_y
			    or picked_neighbor.y < min_y
			):
				continue  # neighbor outside our bounds

			if not find_max:
				__lowerCAmelCase      :						int						      =				change * -1  # in case we are finding minimum
			if change > 0:  # improves the solution
				__lowerCAmelCase      :						List[str]						      =				picked_neighbor
			else:
				__lowerCAmelCase      :						Union[str, Any]						      =				(math.e) ** (
				    change / current_temp
				)  # probability generation function
				if random.random() < probability:  # random number within probability
					__lowerCAmelCase      :						Optional[Any]						      =				picked_neighbor
		__lowerCAmelCase      :						Any						      =				current_temp - (current_temp * rate_of_decrease)

		if current_temp < threshold_temp or next_state is None:
			# temperature below threshold, or could not find a suitable neighbor
			__lowerCAmelCase      :						List[str]						      =				True
		else:
			__lowerCAmelCase      :						str						      =				next_state

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(_UpperCamelCase		)	,  _UpperCamelCase		)
		plt.xlabel('Iterations'		)
		plt.ylabel('Function values'		)
		plt.show()
	return best_state


if __name__ == "__main__":
							def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
								return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							lowerCamelCase__        =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(
							    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
							)
							print(
							    "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
							    f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
							)

							# starting the problem with initial coordinates (12, 47)
							lowerCamelCase__        =		SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(
							    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
							)
							print(
							    "The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
							    f'and 50 > y > - 5 found via hill climbing: {local_min.score()}'
							)
							def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
								return (3 * x**2) - (6 * y)

							lowerCamelCase__        =		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(prob, find_max=False, visualization=True)
							print(
							    "The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
							    f'{local_min.score()}'
							)

							lowerCamelCase__        =		SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							lowerCamelCase__        =		simulated_annealing(prob, find_max=True, visualization=True)
							print(
							    "The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
							    f'{local_min.score()}'
							)

"""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


lowerCamelCase__        =		getLogger(__name__)


lowerCamelCase__        =		"""cuda""" if torch.cuda.is_available() else """cpu"""
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = DEFAULT_DEVICE	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						str						      =				Path(_UpperCamelCase		).open('w'	,  encoding='utf-8'		)
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	__lowerCAmelCase      :						List[str]						      =				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase		).to(_UpperCamelCase		)
	if fpaa:
		__lowerCAmelCase      :						Optional[Any]						      =				model.half()

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	__lowerCAmelCase      :						List[Any]						      =				time.time()
	# update config with task specific params
	use_task_specific_params(_UpperCamelCase	,  _UpperCamelCase		)
	if prefix is None:
		__lowerCAmelCase      :						Optional[int]						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	for examples_chunk in tqdm(list(chunks(_UpperCamelCase	,  _UpperCamelCase		)		)		):
		__lowerCAmelCase      :						List[str]						      =				[prefix + text for text in examples_chunk]
		__lowerCAmelCase      :						List[str]						      =				tokenizer(_UpperCamelCase	,  return_tensors='pt'	,  truncation=_UpperCamelCase	,  padding='longest'		).to(_UpperCamelCase		)
		__lowerCAmelCase      :						str						      =				model.generate(
		    input_ids=batch.input_ids	,  attention_mask=batch.attention_mask	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		for hypothesis in dec:
			fout.write(hypothesis + '\n'		)
			fout.flush()
	fout.close()
	__lowerCAmelCase      :						Optional[int]						      =				int(time.time() - start_time		)  # seconds
	__lowerCAmelCase      :						List[Any]						      =				len(_UpperCamelCase		)
	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    (_UpperCamelCase=True		):
	__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser()
	parser.add_argument('model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'		)
	parser.add_argument('input_path'	,  type=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument('save_path'	,  type=_UpperCamelCase	,  help='where to save summaries'		)
	parser.add_argument('--reference_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  help='like cnn_dm/test.target'		)
	parser.add_argument('--score_path'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default='metrics.json'	,  help='where to save metrics'		)
	parser.add_argument('--device'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='cuda, cuda:1, cpu etc.'		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--task'	,  type=_UpperCamelCase	,  default='summarization'	,  help='used for task_specific_params + metrics'		)
	parser.add_argument('--bs'	,  type=_UpperCamelCase	,  default=8	,  required=_UpperCamelCase	,  help='batch size'		)
	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  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=_UpperCamelCase	,  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
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Optional[int]						      =				parser.parse_known_args()
	__lowerCAmelCase      :						Optional[int]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if parsed_args and verbose:
		print(F"parsed the following generate kwargs: {parsed_args}"		)
	__lowerCAmelCase      :						Dict						      =				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path		).readlines()]
	if args.n_obs > 0:
		__lowerCAmelCase      :						int						      =				examples[: args.n_obs]
	Path(args.save_path		).parent.mkdir(exist_ok=_UpperCamelCase		)

	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'		)

	__lowerCAmelCase      :						Optional[Any]						      =				generate_summaries_or_translations(
	    _UpperCamelCase	,  args.save_path	,  args.model_name	,  batch_size=args.bs	,  device=args.device	,  fpaa=args.fpaa	,  task=args.task	,  prefix=args.prefix	,  **_UpperCamelCase	,  )

	if args.reference_path is None:
		return {}

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

	if args.dump_args:
		scores.update(_UpperCamelCase		)
	if args.info:
		__lowerCAmelCase      :						Optional[Any]						      =				args.info

	if verbose:
		print(_UpperCamelCase		)

	if args.score_path is not None:
		json.dump(_UpperCamelCase	,  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)