
from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload







class  UpperCamelCase__		:





	def __init__(			self				,       UpperCamelCase__ = None		):
					if components is None:
									A__  :	Optional[int]         =							[]
					A__  :	Dict         =							list(UpperCamelCase__		)





	def __len__(			self		):
					return len(self.__components		)





	def __str__(			self		):
					return "(" + ",".join(map(UpperCamelCase__				,       self.__components		)		) + ")"





	def __add__(			self				,       UpperCamelCase__		):
					A__  :	Optional[int]         =							len(self		)
					if size == len(UpperCamelCase__		):
									A__  :	Optional[int]         =							[self.__components[i] + other.component(UpperCamelCase__		) for i in range(UpperCamelCase__		)]
									return Vector(UpperCamelCase__		)
					else:
									raise Exception('''must have the same size'''		)





	def __sub__(			self				,       UpperCamelCase__		):
					A__  :	Optional[Any]         =							len(self		)
					if size == len(UpperCamelCase__		):
									A__  :	List[Any]         =							[self.__components[i] - other.component(UpperCamelCase__		) for i in range(UpperCamelCase__		)]
									return Vector(UpperCamelCase__		)
					else:  # error case
									raise Exception('''must have the same size'''		)





	@overload
	def __mul__(			self				,       UpperCamelCase__		):
					...





	@overload
	def __mul__(			self				,       UpperCamelCase__		):
					...





	def __mul__(			self				,       UpperCamelCase__		):
					if isinstance(UpperCamelCase__				,       (float, int)		):
									A__  :	Optional[Any]         =							[c * other for c in self.__components]
									return Vector(UpperCamelCase__		)
					elif isinstance(UpperCamelCase__				,       UpperCamelCase__		) and len(self		) == len(UpperCamelCase__		):
									A__  :	Optional[int]         =							len(self		)
									A__  :	List[Any]         =							[self.__components[i] * other.component(UpperCamelCase__		) for i in range(UpperCamelCase__		)]
									return sum(UpperCamelCase__		)
					else:  # error case
									raise Exception('''invalid operand!'''		)





	def   __snake_case				(			self		):
					return Vector(self.__components		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					if isinstance(UpperCamelCase__				,       UpperCamelCase__		) and -len(self.__components		) <= i < len(self.__components		):
									return self.__components[i]
					else:
									raise Exception('''index out of range'''		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					assert -len(self.__components		) <= pos < len(self.__components		)
					A__  :	Tuple         =							value





	def   __snake_case				(			self		):
					if len(self.__components		) == 0:
									raise Exception('''Vector is empty'''		)
					A__  :	Tuple         =							[c**2 for c in self.__components]
					return math.sqrt(sum(UpperCamelCase__		)		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = False		):
					A__  :	Dict         =							self * other
					A__  :	Any         =							self.euclidean_length() * other.euclidean_length()
					if deg:
									return math.degrees(math.acos(num / den		)		)
					else:
									return math.acos(num / den		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Vector:

				"""simple docstring"""







				assert isinstance(__UpperCamelCase							,						__UpperCamelCase       )
				return Vector([0] * dimension       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						int       ) ->       Vector:

				"""simple docstring"""







				assert isinstance(__UpperCamelCase							,						__UpperCamelCase       ) and (isinstance(__UpperCamelCase							,						__UpperCamelCase       ))
				A__  :	Union[str, Any]         =							[0] * dimension
				A__  :	str         =							1
				return Vector(__UpperCamelCase       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						float							,						__UpperCamelCase :						Vector							,						__UpperCamelCase :						Vector       ) ->       Vector:

				"""simple docstring"""







				assert (
				    isinstance(__UpperCamelCase							,						__UpperCamelCase       )
				    and isinstance(__UpperCamelCase							,						__UpperCamelCase       )
				    and (isinstance(__UpperCamelCase							,						(int, float)       ))
				)
				return x * scalar + y

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						int							,						__UpperCamelCase :						int       ) ->       Vector:

				"""simple docstring"""







				random.seed(__UpperCamelCase       )
				A__  :	Optional[Any]         =							[random.randint(__UpperCamelCase							,						__UpperCamelCase       ) for _ in range(__UpperCamelCase       )]
				return Vector(__UpperCamelCase       )







class  UpperCamelCase__		:





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Optional[Any]         =							matrix
					A__  :	Optional[int]         =							w
					A__  :	Any         =							h





	def __str__(			self		):
					A__  :	Optional[int]         =							''''''
					for i in range(self.__height		):
									ans += "|"
									for j in range(self.__width		):
													if j < self.__width - 1:
																	ans += str(self.__matrix[i][j]		) + ","
													else:
																	ans += str(self.__matrix[i][j]		) + "|\n"
					return ans





	def __add__(			self				,       UpperCamelCase__		):
					if self.__width == other.width() and self.__height == other.height():
									A__  :	Dict         =							[]
									for i in range(self.__height		):
													A__  :	Any         =							[
													    self.__matrix[i][j] + other.component(UpperCamelCase__				,       UpperCamelCase__		)
													    for j in range(self.__width		)
													]
													matrix.append(UpperCamelCase__		)
									return Matrix(UpperCamelCase__				,       self.__width				,       self.__height		)
					else:
									raise Exception('''matrix must have the same dimension!'''		)





	def __sub__(			self				,       UpperCamelCase__		):
					if self.__width == other.width() and self.__height == other.height():
									A__  :	List[str]         =							[]
									for i in range(self.__height		):
													A__  :	int         =							[
													    self.__matrix[i][j] - other.component(UpperCamelCase__				,       UpperCamelCase__		)
													    for j in range(self.__width		)
													]
													matrix.append(UpperCamelCase__		)
									return Matrix(UpperCamelCase__				,       self.__width				,       self.__height		)
					else:
									raise Exception('''matrices must have the same dimension!'''		)





	@overload
	def __mul__(			self				,       UpperCamelCase__		):
					...





	@overload
	def __mul__(			self				,       UpperCamelCase__		):
					...





	def __mul__(			self				,       UpperCamelCase__		):
					if isinstance(UpperCamelCase__				,       UpperCamelCase__		):  # matrix-vector
									if len(UpperCamelCase__		) == self.__width:
													A__  :	Optional[Any]         =							zero_vector(self.__height		)
													for i in range(self.__height		):
																	A__  :	Optional[Any]         =							[
																	    self.__matrix[i][j] * other.component(UpperCamelCase__		)
																	    for j in range(self.__width		)
																	]
																	ans.change_component(UpperCamelCase__				,       sum(UpperCamelCase__		)		)
													return ans
									else:
													raise Exception(
													    '''vector must have the same size as the '''
													    '''number of columns of the matrix!'''		)
					elif isinstance(UpperCamelCase__				,       (int, float)		):  # matrix-scalar
									A__  :	List[str]         =							[
									    [self.__matrix[i][j] * other for j in range(self.__width		)]
									    for i in range(self.__height		)
									]
									return Matrix(UpperCamelCase__				,       self.__width				,       self.__height		)
					return None





	def   __snake_case				(			self		):
					return self.__height





	def   __snake_case				(			self		):
					return self.__width





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					if 0 <= x < self.__height and 0 <= y < self.__width:
									return self.__matrix[x][y]
					else:
									raise Exception('''change_component: indices out of bounds'''		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					if 0 <= x < self.__height and 0 <= y < self.__width:
									A__  :	Any         =							value
					else:
									raise Exception('''change_component: indices out of bounds'''		)





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					if self.__height != self.__width:
									raise Exception('''Matrix is not square'''		)
					A__  :	int         =							self.__matrix[:x] + self.__matrix[x + 1 :]
					for i in range(len(UpperCamelCase__		)		):
									A__  :	Any         =							minor[i][:y] + minor[i][y + 1 :]
					return Matrix(UpperCamelCase__				,       self.__width - 1				,       self.__height - 1		).determinant()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					if self.__height != self.__width:
									raise Exception('''Matrix is not square'''		)
					if 0 <= x < self.__height and 0 <= y < self.__width:
									return (-1) ** (x + y) * self.minor(UpperCamelCase__				,       UpperCamelCase__		)
					else:
									raise Exception('''Indices out of bounds'''		)





	def   __snake_case				(			self		):
					if self.__height != self.__width:
									raise Exception('''Matrix is not square'''		)
					if self.__height < 1:
									raise Exception('''Matrix has no element'''		)
					elif self.__height == 1:
									return self.__matrix[0][0]
					elif self.__height == 2:
									return (
									    self.__matrix[0][0] * self.__matrix[1][1]
									    - self.__matrix[0][1] * self.__matrix[1][0]
									)
					else:
									A__  :	Dict         =							[
									    self.__matrix[0][y] * self.cofactor(0				,       UpperCamelCase__		) for y in range(self.__width		)
									]
									return sum(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       Matrix:

				"""simple docstring"""







				A__  :	list[list[float]]         =							[[0] * n for _ in range(__UpperCamelCase       )]
				return Matrix(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )



def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int							,						__UpperCamelCase :						int							,						__UpperCamelCase :						int							,						__UpperCamelCase :						int       ) ->       Matrix:

				"""simple docstring"""







				random.seed(__UpperCamelCase       )
				A__  :	list[list[float]]         =							[
				    [random.randint(__UpperCamelCase							,						__UpperCamelCase       ) for _ in range(__UpperCamelCase       )] for _ in range(__UpperCamelCase       )
				]
				return Matrix(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )

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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





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




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							outputs.loss.repeat(args.batch_size       )
								losses.append(accelerator.gather(__UpperCamelCase       )       )

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				A__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			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
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[int]          =			evaluate(args)



logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")