


"""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 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_=10_24				,       a_=10_24				,       a_=3.6					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				tokenizer
			__snake_case	:						List[Any]						=				tokenizer.bos_token_id
			__snake_case	:						str						=				dataset
			__snake_case	:						Union[str, Any]						=				seq_length
			__snake_case	:						Union[str, Any]						=				seq_length * chars_per_token * num_of_sequences



		def __iter__(self					):





			'''simple docstring'''

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






def 		lowercase							(     _snake_case						:      List[str]     )     ->Optional[Any]:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      Union[str, Any]     )     ->Union[str, Any]:
	"""simple docstring"""





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

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


# Setup Accelerator
SCREAMING_SNAKE_CASE :    Tuple									=				Accelerator()

# Parse configuration
SCREAMING_SNAKE_CASE :    Dict									=				HfArgumentParser(EvaluationArguments)
SCREAMING_SNAKE_CASE :    Any									=				parser.parse_args()
set_seed(args.seed)

# Logging
SCREAMING_SNAKE_CASE :    int									=				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 :    Any									=				AutoModelForCausalLM.from_pretrained(args.model_ckpt)
SCREAMING_SNAKE_CASE :    str									=				AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
SCREAMING_SNAKE_CASE :    Optional[int]									=				create_dataloader(args)

# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE :    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 :    Union[str, Any]									=				evaluate(args)

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









"""simple docstring"""
from __future__ import annotations

from scipy.special import comb  # type: ignore



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				list_of_points
			# Degree determines the flexibility of the curve.
			# Degree = 1 will produce a straight line.
			__snake_case	:						Optional[Any]						=				len(a_					) - 1



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			assert 0 <= t <= 1, "Time t must be between 0 and 1."
			__snake_case	:						list[float]						=				[]
			for i in range(len(self.list_of_points					)					):
				# basis function for each i
				output_values.append(
				    comb(self.degree				,       a_					) * ((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(a_					)				,       5					) == 1
			return output_values



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

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

			__snake_case	:						List[str]						=				self.basis_function(a_					)
			__snake_case	:						str						=				0.0
			__snake_case	:						Union[str, Any]						=				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 				SCREAMING_SNAKE_CASE  (self				,       a_ = 0.01					):





			'''simple docstring'''

			from matplotlib import pyplot as plt  # type: ignore

			__snake_case	:						list[float]						=				[]  # x coordinates of points to plot
			__snake_case	:						list[float]						=				[]  # y coordinates of points to plot

			__snake_case	:						int						=				0.0
			while t <= 1:
				__snake_case	:						Union[str, Any]						=				self.bezier_curve_function(a_					)
				to_plot_x.append(value[0]					)
				to_plot_y.append(value[1]					)
				t += step_size

			__snake_case	:						List[Any]						=				[i[0] for i in self.list_of_points]
			__snake_case	:						Any						=				[i[1] for i in self.list_of_points]

			plt.plot(
			    a_				,       a_				,       color='''blue'''				,       label='''Curve of Degree ''' + str(self.degree					)				,       )
			plt.scatter(a_				,       a_				,       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





