




'''simple docstring'''


from __future__ import annotations

from typing import Any

class       A   :







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  float = 0 )      ->							None:


														"""simple docstring"""


														_a					,							_a     					=    row, column
														_a     					=    [[default_value for c in range(lowerCAmelCase_ )] for r in range(lowerCAmelCase_ )]







							def __str__(  self	:  Optional[Any] )      ->							str:


														"""simple docstring"""


														_a     					=    F'Matrix consist of {self.row} rows and {self.column} columns\n'

														# Make string identifier
														_a     					=    0
														for row_vector in self.array:
																					for obj in row_vector:
																												_a     					=    max(lowerCAmelCase_							,      len(str(lowerCAmelCase_ ) ) )
														_a     					=    F'%{max_element_length}s'

														# Make string and return
														def single_line(lowerCAmelCase_	:  list[float] ) -> str:
																					nonlocal string_format_identifier
																					_a     					=    '''['''
																					line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
																					line += "]"
																					return line

														s += "\n".join(single_line(lowerCAmelCase_ ) for row_vector in self.array )
														return s







							def __repr__(  self	:  Any )      ->							str:


														"""simple docstring"""


														return str(self )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  tuple[int, int] )      ->							bool:


														"""simple docstring"""


														if not (isinstance(lowerCAmelCase_							,      (list, tuple) ) and len(lowerCAmelCase_ ) == 2):
																					return False
														elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																					return False
														else:
																					return True







							def __getitem__(  self	:  str							,      lowerCAmelCase_	:  tuple[int, int] )      ->							Any:


														"""simple docstring"""


														assert self.validate_indicies(lowerCAmelCase_ )
														return self.array[loc[0]][loc[1]]







							def __setitem__(  self	:  List[Any]							,      lowerCAmelCase_	:  tuple[int, int]							,      lowerCAmelCase_	:  float )      ->							None:


														"""simple docstring"""


														assert self.validate_indicies(lowerCAmelCase_ )
														_a     					=    value







							def __add__(  self	:  int							,      lowerCAmelCase_	:  Matrix )      ->							Matrix:


														"""simple docstring"""


														assert isinstance(lowerCAmelCase_							,      lowerCAmelCase_ )
														assert self.row == another.row and self.column == another.column

														# Add
														_a     					=    Matrix(self.row							,      self.column )
														for r in range(self.row ):
																					for c in range(self.column ):
																												_a     					=    self[r, c] + another[r, c]
														return result







							def __neg__(  self	:  int )      ->							Matrix:


														"""simple docstring"""


														_a     					=    Matrix(self.row							,      self.column )
														for r in range(self.row ):
																					for c in range(self.column ):
																												_a     					=    -self[r, c]
														return result







							def __sub__(  self	:  Any							,      lowerCAmelCase_	:  Matrix )      ->							Matrix:


														"""simple docstring"""


														return self + (-another)







							def __mul__(  self	:  List[str]							,      lowerCAmelCase_	:  int | float | Matrix )      ->							Matrix:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_							,      (int, float) ):  # Scalar multiplication
																					_a     					=    Matrix(self.row							,      self.column )
																					for r in range(self.row ):
																												for c in range(self.column ):
																																			_a     					=    self[r, c] * another
																					return result
														elif isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ):  # Matrix multiplication
																					assert self.column == another.row
																					_a     					=    Matrix(self.row							,      another.column )
																					for r in range(self.row ):
																												for c in range(another.column ):
																																			for i in range(self.column ):
																																										result[r, c] += self[r, i] * another[i, c]
																					return result
														else:
																					_a     					=    F'Unsupported type given for another ({type(lowerCAmelCase_ )})'
																					raise TypeError(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str )      ->							Matrix:


														"""simple docstring"""


														_a     					=    Matrix(self.column							,      self.row )
														for r in range(self.row ):
																					for c in range(self.column ):
																												_a     					=    self[r, c]
														return result







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Matrix							,      lowerCAmelCase_	:  Matrix )      ->							Any:


														"""simple docstring"""


														assert isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) and isinstance(lowerCAmelCase_							,      lowerCAmelCase_ )
														assert self.row == self.column == u.row == v.row  # u, v should be column vector
														assert u.column == v.column == 1  # u, v should be column vector

														# Calculate
														_a     					=    v.transpose()
														_a     					=    (v_t * self * u)[0, 0] + 1
														if numerator_factor == 0:
																					return None  # It's not invertable
														return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
				def 		snake_case_   ():





											'''simple docstring'''


											_a     					=    Matrix(3			,						3			,						0				)
											for i in range(3				):
																		_a     					=    1
											print(f'a^(-1) is {ainv}'				)
											# u, v
											_a     					=    Matrix(3			,						1			,						0				)
											_a					,							_a					,							_a     					=    1, 2, -3
											_a     					=    Matrix(3			,						1			,						0				)
											_a					,							_a					,							_a     					=    4, -2, 5
											print(f'u is {u}'				)
											print(f'v is {v}'				)
											print(f'uv^T is {u * v.transpose()}'				)
											# Sherman Morrison
											print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(UpperCamelCase			,						UpperCamelCase				)}'				)
				def 		snake_case_   ():





											'''simple docstring'''


											import doctest

											doctest.testmod()

				testa()







'''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       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


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



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    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     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				Any       							=		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
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

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

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

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

