

'''simple docstring'''


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


lowerCAmelCase__										= ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]



class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case=None    ,			_snake_case=1)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					tokenizer
								UpperCAmelCase_		:      Optional[int]       	=					dataset
								UpperCAmelCase_		:      Optional[int]       	=					len(__a) if n_tasks is None else n_tasks
								UpperCAmelCase_		:      Tuple       	=					n_copies





							def __iter__(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					[]
								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())
								UpperCAmelCase_		:      List[str]       	=					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 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Optional[Any]:
								UpperCAmelCase_		:      Any       	=					start_length
								UpperCAmelCase_		:      Union[str, Any]       	=					eof_strings
								UpperCAmelCase_		:      Any       	=					tokenizer





							def __call__(			self    ,			_snake_case    ,			_snake_case    ,			**_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer.batch_decode(input_ids[:, self.start_length :])
								UpperCAmelCase_		:      Union[str, Any]       	=					[]
								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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Dict:

	'''simple docstring'''





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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=2_0   ,**UpperCamelCase  )    ->    Any:

	'''simple docstring'''





	UpperCAmelCase_		:      Union[str, Any]       	=					defaultdict(__snake_case  )  # dict of list of generated tokens
	for step, batch in tqdm(enumerate(__snake_case  )  ):
		with torch.no_grad():
			UpperCAmelCase_		:      Dict       	=					batch['ids'].shape[-1]
			UpperCAmelCase_		:      Tuple       	=					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
			UpperCAmelCase_		:      int       	=					batch['task_id'].repeat(__snake_case  )
			UpperCAmelCase_		:      str       	=					accelerator.pad_across_processes(
			    __snake_case   ,dim=1   ,pad_index=tokenizer.pad_token_id  )

			UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					accelerator.gather((generated_tokens, generated_tasks)  )
			UpperCAmelCase_		:      int       	=					generated_tokens.cpu().numpy()
			UpperCAmelCase_		:      List[str]       	=					generated_tasks.cpu().numpy()

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

	UpperCAmelCase_		:      str       	=					[[] for _ in range(__snake_case  )]
	for task, generated_tokens in gen_token_dict.items():
		for s in generated_tokens:
			UpperCAmelCase_		:      Optional[int]       	=					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 						SCREAMING_SNAKE_CASE(  )    ->    Optional[Any]:

	'''simple docstring'''





	UpperCAmelCase_		:      Optional[int]       	=					HfArgumentParser(__snake_case  )
	UpperCAmelCase_		:      Dict       	=					parser.parse_args()

	transformers.logging.set_verbosity_error()
	# enables code execution in code_eval metric
	UpperCAmelCase_		:      Any       	=					args.HF_ALLOW_CODE_EVAL
	# make sure tokenizer plays nice with multiprocessing
	UpperCAmelCase_		:      List[Any]       	=					'false'

	if args.num_workers is None:
		UpperCAmelCase_		:      Optional[Any]       	=					multiprocessing.cpu_count()

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

	# Load model and tokenizer
	UpperCAmelCase_		:      str       	=					AutoTokenizer.from_pretrained(args.model_ckpt  )
	UpperCAmelCase_		:      Tuple       	=					tokenizer.eos_token
	UpperCAmelCase_		:      Dict       	=					AutoModelForCausalLM.from_pretrained(args.model_ckpt  )

	# Generation settings
	UpperCAmelCase_		:      Tuple       	=					{
	    '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
	UpperCAmelCase_		:      Union[str, Any]       	=					load_dataset('openai_humaneval'  )
	UpperCAmelCase_		:      Any       	=					load_metric('code_eval'  )

	UpperCAmelCase_		:      Tuple       	=					args.num_tasks if args.num_tasks is not None else len(human_eval['test']  )
	UpperCAmelCase_		:      Optional[Any]       	=					args.n_samples // args.batch_size

	UpperCAmelCase_		:      Optional[Any]       	=					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
	UpperCAmelCase_		:      Dict       	=					DataLoader(__snake_case   ,batch_size=1  )

	# Run a quick test to see if code evaluation is enabled
	try:
		UpperCAmelCase_		:      Dict       	=					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

	UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					accelerator.prepare(__snake_case   ,__snake_case  )

	UpperCAmelCase_		:      Optional[Any]       	=					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:
		UpperCAmelCase_		:      List[Any]       	=					[]

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

		# Evaluate completions with "code_eval" metric
		UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[int]       	=					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()









'''simple docstring'''


import numpy



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Optional[Any]       	=					input_array

								# Random initial weights are assigned where first argument is the
								# number of nodes in previous layer and second argument is the
								# number of nodes in the next layer.

								# Random initial weights are assigned.
								# self.input_array.shape[1] is used to represent number of nodes in input layer.
								# First hidden layer consists of 4 nodes.
								UpperCAmelCase_		:      Tuple       	=					numpy.random.rand(
								    self.input_array.shape[1]    ,			4)

								# Random initial values for the first hidden layer.
								# First hidden layer has 4 nodes.
								# Second hidden layer has 3 nodes.
								UpperCAmelCase_		:      List[str]       	=					numpy.random.rand(
								    4    ,			3)

								# Random initial values for the second hidden layer.
								# Second hidden layer has 3 nodes.
								# Output layer has 1 node.
								UpperCAmelCase_		:      Dict       	=					numpy.random.rand(3    ,			1)

								# Real output values provided.
								UpperCAmelCase_		:      str       	=					output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase_		:      Union[str, Any]       	=					numpy.zeros(output_array.shape)


							def 						_snake_case							(			self)							->				numpy.ndarray:
								UpperCAmelCase_		:      Any       	=					sigmoid(
								    numpy.dot(self.input_array    ,			self.input_layer_and_first_hidden_layer_weights))

								# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
								# connecting the first hidden set of nodes with the second hidden set of nodes.
								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								# layer_between_second_hidden_layer_and_output is the layer connecting
								# second hidden layer with the output node.
								UpperCAmelCase_		:      int       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,			self.second_hidden_layer_and_output_layer_weights    ,			))

								return self.layer_between_second_hidden_layer_and_output


							def 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      Optional[int]       	=					numpy.dot(
								    self.layer_between_first_hidden_layer_and_second_hidden_layer.T    ,			2
								    * (self.output_array - self.predicted_output)
								    * sigmoid_derivative(self.predicted_output)    ,			)
								UpperCAmelCase_		:      Any       	=					numpy.dot(
								    self.layer_between_input_and_first_hidden_layer.T    ,			numpy.dot(
								        2
								        * (self.output_array - self.predicted_output)
								        * sigmoid_derivative(self.predicted_output)    ,			self.second_hidden_layer_and_output_layer_weights.T    ,			)
								    * sigmoid_derivative(
								        self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,			)
								UpperCAmelCase_		:      Tuple       	=					numpy.dot(
								    self.input_array.T    ,			numpy.dot(
								        numpy.dot(
								            2
								            * (self.output_array - self.predicted_output)
								            * sigmoid_derivative(self.predicted_output)    ,			self.second_hidden_layer_and_output_layer_weights.T    ,			)
								        * sigmoid_derivative(
								            self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,			self.first_hidden_layer_and_second_hidden_layer_weights.T    ,			)
								    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer)    ,			)

								self.input_layer_and_first_hidden_layer_weights += (
								    updated_input_layer_and_first_hidden_layer_weights
								)
								self.first_hidden_layer_and_second_hidden_layer_weights += (
								    updated_first_hidden_layer_and_second_hidden_layer_weights
								)
								self.second_hidden_layer_and_output_layer_weights += (
								    updated_second_hidden_layer_and_output_layer_weights
								)


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				None:
								for iteration in range(1    ,			iterations + 1):
									UpperCAmelCase_		:      int       	=					self.feedforward()
									self.back_propagation()
									if give_loss:
										UpperCAmelCase_		:      List[Any]       	=					numpy.mean(numpy.square(output - self.feedforward()))
										print(F"""Iteration {iteration} Loss: {loss}""")





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					input_arr

								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(self.array    ,			self.input_layer_and_first_hidden_layer_weights))

								UpperCAmelCase_		:      Optional[int]       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								UpperCAmelCase_		:      int       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,			self.second_hidden_layer_and_output_layer_weights    ,			))

								return int(self.layer_between_second_hidden_layer_and_output > 0.6)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return 1 / (1 + numpy.exp(-value  ))







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return (value) * (1 - (value))







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					numpy.array(
	    (
	        [0, 0, 0],
	        [0, 0, 1],
	        [0, 1, 0],
	        [0, 1, 1],
	        [1, 0, 0],
	        [1, 0, 1],
	        [1, 1, 0],
	        [1, 1, 1],
	    )   ,dtype=numpy.floataa   ,)

	# True output values for the given input values.
	UpperCAmelCase_		:      Dict       	=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,dtype=numpy.floataa  )

	# Calling neural network class.
	UpperCAmelCase_		:      List[str]       	=					TwoHiddenLayerNeuralNetwork(
	    input_array=UpperCamelCase   ,output_array=UpperCamelCase  )

	# Calling training function.
	# Set give_loss to True if you want to see loss in every iteration.
	neural_network.train(output=UpperCamelCase   ,iterations=1_0   ,give_loss=UpperCamelCase  )

	return neural_network.predict(numpy.array(([1, 1, 1])   ,dtype=numpy.floataa  )  )


if __name__ == "__main__":
						example()






