




'''simple docstring'''


from __future__ import annotations

from scipy.special import comb  # type: ignore

class       A   :







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  list[tuple[float, float]] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    list_of_points
														# Degree determines the flexibility of the curve.
														# Degree = 1 will produce a straight line.
														_a     					=    len(lowerCAmelCase_ ) - 1







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  float )      ->							list[float]:


														"""simple docstring"""


														assert 0 <= t <= 1, "Time t must be between 0 and 1."
														_a     					=    []
														for i in range(len(self.list_of_points ) ):
																					# basis function for each i
																					output_values.append(
																					    comb(self.degree							,      lowerCAmelCase_ ) * ((1 - t) ** (self.degree - i)) * (t**i) )
														# the basis must sum up to 1 for it to produce a valid Bezier curve.
														assert round(sum(lowerCAmelCase_ )							,      5 ) == 1
														return output_values







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


														"""simple docstring"""


														assert 0 <= t <= 1, "Time t must be between 0 and 1."

														_a     					=    self.basis_function(lowerCAmelCase_ )
														_a     					=    0.0
														_a     					=    0.0
														for i in range(len(self.list_of_points ) ):
																					# For all points, sum up the product of i-th basis function and i-th point.
																					x += basis_function[i] * self.list_of_points[i][0]
																					y += basis_function[i] * self.list_of_points[i][1]
														return (x, y)







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  float = 0.0_1 )      ->							Union[str, Any]:


														"""simple docstring"""


														from matplotlib import pyplot as plt  # type: ignore

														_a     					=    []  # x coordinates of points to plot
														_a     					=    []  # y coordinates of points to plot

														_a     					=    0.0
														while t <= 1:
																					_a     					=    self.bezier_curve_function(lowerCAmelCase_ )
																					to_plot_x.append(value[0] )
																					to_plot_y.append(value[1] )
																					t += step_size

														_a     					=    [i[0] for i in self.list_of_points]
														_a     					=    [i[1] for i in self.list_of_points]

														plt.plot(
														    lowerCAmelCase_							,      lowerCAmelCase_							,      color='''blue'''							,      label='''Curve of Degree ''' + str(self.degree )							,      )
														plt.scatter(lowerCAmelCase_							,      lowerCAmelCase_							,      color='''red'''							,      label='''Control Points''' )
														plt.legend()
														plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
				BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
				BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3







'''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}''')

