

import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


A											=     ['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=None ,				__UpperCamelCase=1							):

				"""simple docstring"""


				snake_case_      		= tokenizer
				snake_case_      		= dataset
				snake_case_      		= len(__a							) if n_tasks is None else n_tasks
				snake_case_      		= n_copies







			def __iter__(    self							):

				"""simple docstring"""


				snake_case_      		= []
				for task in range(self.n_tasks							):
					# without strip, the model generate commented codes ...
					prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip()							)
				snake_case_      		= self.tokenizer(__a ,				padding=__a ,				return_tensors='pt'							)
				for task in range(self.n_tasks							):
					for _ in range(self.n_copies							):
						yield {
						    "ids": outputs.input_ids[task],
						    "task_id": task,
						    "input_len": outputs.attention_mask[task].sum(),
						}







class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= start_length
				snake_case_      		= eof_strings
				snake_case_      		= tokenizer







			def __call__(    self ,				__UpperCamelCase ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.tokenizer.batch_decode(input_ids[:, self.start_length :]							)
				snake_case_      		= []
				for decoded_generation in decoded_generations:
					done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings							)							)
				return all(__a							)







def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= re.split('(%s)' % '|'.join(__snake_case	)			, __snake_case	)
	# last string should be ""
	return "".join(string_list[:-2]	)





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__=20			, **lowercase__	):





	'''simple docstring'''
	snake_case_      		= defaultdict(__snake_case	)  # dict of list of generated tokens
	for step, batch in tqdm(enumerate(__snake_case	)	):
		with torch.no_grad():
			snake_case_      		= batch['ids'].shape[-1]
			snake_case_      		= accelerator.unwrap_model(__snake_case	).generate(
			    input_ids=batch['ids'][:, : batch['input_len']]			, num_return_sequences=__snake_case			, **__snake_case	)
			# each task is generated batch_size times
			snake_case_      		= batch['task_id'].repeat(__snake_case	)
			snake_case_      		= accelerator.pad_across_processes(
			    __snake_case			, dim=1			, pad_index=tokenizer.pad_token_id	)

			snake_case_		,       snake_case_      		= accelerator.gather((generated_tokens, generated_tasks)	)
			snake_case_      		= generated_tokens.cpu().numpy()
			snake_case_      		= generated_tasks.cpu().numpy()

			for task, generated_tokens in zip(__snake_case			, __snake_case	):
				gen_token_dict[task].append(__snake_case	)

	snake_case_      		= [[] for _ in range(__snake_case	)]
	for task, generated_tokens in gen_token_dict.items():
		for s in generated_tokens:
			snake_case_      		= tokenizer.decode(__snake_case			, skip_special_tokens=__snake_case			, clean_up_tokenization_spaces=__snake_case	)
			code_gens[task].append(remove_last_block(__snake_case	)	)
	return code_gens





def a():





	'''simple docstring'''
	snake_case_      		= HfArgumentParser(__snake_case	)
	snake_case_      		= parser.parse_args()

	transformers.logging.set_verbosity_error()
	# enables code execution in code_eval metric
	snake_case_      		= args.HF_ALLOW_CODE_EVAL
	# make sure tokenizer plays nice with multiprocessing
	snake_case_      		= 'false'

	if args.num_workers is None:
		snake_case_      		= multiprocessing.cpu_count()

	# Use dataset load to feed to accelerate
	snake_case_      		= Accelerator()
	set_seed(args.seed			, device_specific=__snake_case	)

	# Load model and tokenizer
	snake_case_      		= AutoTokenizer.from_pretrained(args.model_ckpt	)
	snake_case_      		= tokenizer.eos_token
	snake_case_      		= AutoModelForCausalLM.from_pretrained(args.model_ckpt	)

	# Generation settings
	snake_case_      		= {
	    'do_sample': args.do_sample,
	    'temperature': args.temperature,
	    'max_new_tokens': args.max_new_tokens,
	    'top_p': args.top_p,
	    'top_k': args.top_k,
	    'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0			, __snake_case			, __snake_case	)]	),
	}

	# Load evaluation dataset and metric
	snake_case_      		= load_dataset('openai_humaneval'	)
	snake_case_      		= load_metric('code_eval'	)

	snake_case_      		= args.num_tasks if args.num_tasks is not None else len(human_eval['test']	)
	snake_case_      		= args.n_samples // args.batch_size

	snake_case_      		= TokenizedDataset(__snake_case			, human_eval['test']			, n_copies=__snake_case			, n_tasks=__snake_case	)
	# do not confuse args.batch_size, which is actually the num_return_sequences
	snake_case_      		= DataLoader(__snake_case			, batch_size=1	)

	# Run a quick test to see if code evaluation is enabled
	try:
		snake_case_      		= code_eval_metric.compute(references=['']			, predictions=[['']]	)
	except ValueError as exception:
		print(
		    'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
		    ' flag to enable code evaluation.'	)
		raise exception

	snake_case_		,       snake_case_      		= accelerator.prepare(__snake_case			, __snake_case	)

	snake_case_      		= complete_code(
	    __snake_case			, __snake_case			, __snake_case			, __snake_case			, n_tasks=__snake_case			, batch_size=args.batch_size			, **__snake_case			, )

	if accelerator.is_main_process:
		snake_case_      		= []

		for task in tqdm(range(__snake_case	)	):
			snake_case_      		= human_eval['test'][task]['test']
			snake_case_      		= f"""check({human_eval['test'][task]['entry_point']})"""
			references.append('\n' + test_func + '\n' + entry_point	)

		# Evaluate completions with "code_eval" metric
		snake_case_		,       snake_case_      		= code_eval_metric.compute(
		    references=__snake_case			, predictions=__snake_case			, num_workers=args.num_workers	)
		print(f"""Results: {pass_at_k}"""	)

		# Save results to json file
		with open(args.output_file			, 'w'	) as fp:
			json.dump(__snake_case			, __snake_case	)


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
		main()



import operator as op





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= lambda lowercase__			, lowercase__	: int(x / y	)  # noqa: E731 integer division operation
	snake_case_      		= {
	    '^': op.pow,
	    '*': op.mul,
	    '/': div,
	    '+': op.add,
	    '-': op.sub,
	}  # operators & their respective operation

	# print table header
	print('Symbol'.center(8	)			, 'Action'.center(12	)			, 'Stack'			, sep=' | '	)
	print('-' * (30 + len(lowercase__	))	)

	for x in post_fix:
		if x.isdigit():  # if x in digit
			stack.append(lowercase__	)  # append x to stack
			# output in tabular format
			print(x.rjust(8	)			, ('push(' + x + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)
		else:
			snake_case_      		= stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8	)			, ('pop(' + b + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)

			snake_case_      		= stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8	)			, ('pop(' + a + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '	)

			stack.append(
			    str(opr[x](int(lowercase__	)			, int(lowercase__	)	)	)	)  # evaluate the 2 values popped from stack & push result to stack
			# output in tabular format
			print(
			    x.rjust(8	)			, ('push(' + a + x + b + ')').ljust(12	)			, ','.join(lowercase__	)			, sep=' | '			, )

	return int(stack[0]	)


if __name__ == "__main__":
		A											=     input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
		print('\n\tResult = ', solve(Postfix))
