



"""simple docstring"""

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__=10_24   ,		lowercase__=10_24   ,		lowercase__=3.6       ):
											snake_case_					:		List[str] 					=					tokenizer
											snake_case_					:		Optional[Any] 					=					tokenizer.bos_token_id
											snake_case_					:		List[Any] 					=					dataset
											snake_case_					:		int 					=					seq_length
											snake_case_					:		Any 					=					seq_length * chars_per_token * num_of_sequences







						def __iter__(self       ):
											snake_case_					:		int 					=					iter(self.dataset       )
											snake_case_					:		Optional[int] 					=					True
											while more_examples:
																snake_case_					,			snake_case_					:		Any 					=					[], 0
																while True:
																					if buffer_len >= self.input_characters:
																										break
																					try:
																										buffer.append(next(lowercase__       )["""content"""]       )
																										buffer_len += len(buffer[-1]       )
																					except StopIteration:
																										snake_case_					:		int 					=					False
																										break
																snake_case_					:		Optional[Any] 					=					tokenizer(lowercase__   ,		truncation=lowercase__       )["""input_ids"""]
																snake_case_					:		List[Any] 					=					[]
																for tokenized_input in tokenized_inputs:
																					all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
																for i in range(0   ,		len(lowercase__       )   ,		self.seq_length       ):
																					snake_case_					:		Union[str, Any] 					=					all_token_ids[i : i + self.seq_length]
																					if len(lowercase__       ) == self.seq_length:
																										yield torch.tensor(lowercase__       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					{"""streaming""": True}
					snake_case_					:		Any 					=					load_dataset(args.dataset_name				,    split="""train"""				,    **SCREAMING_SNAKE_CASE__			)
					snake_case_					:		List[str] 					=					ConstantLengthDataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    seq_length=args.seq_length			)
					snake_case_					:		Optional[Any] 					=					DataLoader(SCREAMING_SNAKE_CASE__				,    batch_size=args.batch_size			)
					return eval_dataloader







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					model.eval()
					snake_case_					:		List[Any] 					=					[]
					for step, batch in enumerate(SCREAMING_SNAKE_CASE__			):
										with torch.no_grad():
															snake_case_					:		List[str] 					=					model(SCREAMING_SNAKE_CASE__				,    labels=SCREAMING_SNAKE_CASE__			)
										snake_case_					:		Any 					=					outputs.loss.repeat(args.batch_size			)
										losses.append(accelerator.gather(SCREAMING_SNAKE_CASE__			)			)

										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
															break
					snake_case_					:		Any 					=					torch.mean(torch.cat(SCREAMING_SNAKE_CASE__			)			)
					try:
										snake_case_					:		List[str] 					=					torch.exp(SCREAMING_SNAKE_CASE__			)
					except OverflowError:
										snake_case_					:		int 					=					float("""inf"""			)
					return loss.item(), perplexity.item()


# Setup Accelerator
a_			   =					Accelerator()

# Parse configuration
a_			   =					HfArgumentParser(EvaluationArguments)
a_			   =					parser.parse_args()
set_seed(args.seed)

# Logging
a_			   =					logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

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

# Load dataset and dataloader
a_			   =					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_   ,    a_			   =					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
a_   ,    a_			   =					evaluate(args)


logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					0
					snake_case_					:		List[Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Dict 					=					a[end]
					snake_case_					:		List[Any] 					=					a[pivot]
					snake_case_					:		Optional[Any] 					=					temp
					snake_case_					:		List[str] 					=					start - 1
					for index in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															snake_case_					:		Any 					=					new_pivot_index + 1
															snake_case_					:		Tuple 					=					a[new_pivot_index]
															snake_case_					:		Optional[int] 					=					a[index]
															snake_case_					:		Tuple 					=					temp

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)




