





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


UpperCamelCase__      ='true'






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=82,       __lowerCamelCase=16	):
     set_seed(42	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							RegressionModel()
     _SCREAMING_SNAKE_CASE       :	Dict						    =							deepcopy(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							RegressionDataset(length=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	str						    =							DataLoader(__lowerCamelCase,       batch_size=__lowerCamelCase	)
     model.to(accelerator.device	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							accelerator.prepare(__lowerCamelCase,       __lowerCamelCase	)
     return model, ddp_model, dataloader






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=False	):
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							load_dataset("glue",       "mrpc",       split="validation"	)

     def tokenize_function(__lowerCamelCase	):
          _SCREAMING_SNAKE_CASE       :	int						    =							tokenizer(examples["sentence1"],       examples["sentence2"],       truncation=__lowerCamelCase,       max_length=__lowerCamelCase	)
          return outputs

     with accelerator.main_process_first():
          _SCREAMING_SNAKE_CASE       :	Any						    =							dataset.map(
              __lowerCamelCase,       batched=__lowerCamelCase,       remove_columns=["idx", "sentence1", "sentence2"],       )

     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							tokenized_datasets.rename_column("label",       "labels"	)

     def collate_fn(__lowerCamelCase	):
          if use_longest:
               return tokenizer.pad(__lowerCamelCase,       padding="longest",       return_tensors="pt"	)
          return tokenizer.pad(__lowerCamelCase,       padding="max_length",       max_length=128,       return_tensors="pt"	)

     return DataLoader(__lowerCamelCase,       shuffle=__lowerCamelCase,       collate_fn=__lowerCamelCase,       batch_size=16	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							Accelerator(dispatch_batches=__lowerCamelCase,       split_batches=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							get_dataloader(__lowerCamelCase,       not dispatch_batches	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							AutoModelForSequenceClassification.from_pretrained(
         "hf-internal-testing/mrpc-bert-base-cased",       return_dict=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	str						    =							accelerator.prepare(__lowerCamelCase,       __lowerCamelCase	)
     return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							[]
     for batch in dataloader:
          _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							batch.values()
          with torch.no_grad():
               _SCREAMING_SNAKE_CASE       :	int						    =							model(__lowerCamelCase	)
               _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							accelerator.gather_for_metrics((logit, target)	)
               logits_and_targets.append((logit, target)	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	str						    =							[], []
     for logit, targ in logits_and_targets:
          logits.append(__lowerCamelCase	)
          targs.append(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.cat(__lowerCamelCase	), torch.cat(__lowerCamelCase	)
     return logits, targs






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=82,       __lowerCamelCase=False,       __lowerCamelCase=False,       __lowerCamelCase=16	):
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Tuple						    =							get_basic_setup(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							generate_predictions(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)
     assert (
         len(__lowerCamelCase	) == num_samples
     ), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCamelCase	)}"""






def     lowerCamelCase__			(__lowerCamelCase = False,       __lowerCamelCase = False	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							evaluate.load("glue",       "mrpc"	)
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							get_mrpc_setup(__lowerCamelCase,       __lowerCamelCase	)
     # First do baseline
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							setup["no"]
     model.to(__lowerCamelCase	)
     model.eval()
     for batch in dataloader:
          batch.to(__lowerCamelCase	)
          with torch.inference_mode():
               _SCREAMING_SNAKE_CASE       :	Dict						    =							model(**__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							outputs.logits.argmax(dim=-1	)
          metric.add_batch(predictions=__lowerCamelCase,       references=batch["labels"]	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							metric.compute()

     # Then do distributed
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							setup["ddp"]
     model.eval()
     for batch in dataloader:
          with torch.inference_mode():
               _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(**__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	str						    =							outputs.logits.argmax(dim=-1	)
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							batch["labels"]
          _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							accelerator.gather_for_metrics((preds, references)	)
          metric.add_batch(predictions=__lowerCamelCase,       references=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							metric.compute()

     for key in "accuracy f1".split():
          assert math.isclose(
              baseline[key],       distributed[key]	), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	int						    =							Accelerator(split_batches=__lowerCamelCase,       dispatch_batches=__lowerCamelCase	)
     if accelerator.is_local_main_process:
          datasets.utils.logging.set_verbosity_warning()
          transformers.utils.logging.set_verbosity_warning()
     else:
          datasets.utils.logging.set_verbosity_error()
          transformers.utils.logging.set_verbosity_error()
     # These are a bit slower so they should only be ran on the GPU or TPU
     if torch.cuda.is_available() or is_tpu_available():
          if accelerator.is_local_main_process:
               print("**Testing gather_for_metrics**"	)
          for split_batches in [True, False]:
               for dispatch_batches in [True, False]:
                    if accelerator.is_local_main_process:
                         print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""	)
                    test_mrpc(__lowerCamelCase,       __lowerCamelCase	)
                    accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print("**Test torch metrics**"	)
     for split_batches in [True, False]:
          for dispatch_batches in [True, False]:
               _SCREAMING_SNAKE_CASE       :	str						    =							Accelerator(split_batches=__lowerCamelCase,       dispatch_batches=__lowerCamelCase	)
               if accelerator.is_local_main_process:
                    print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""	)
               test_torch_metrics(__lowerCamelCase,       99	)
               accelerator.state._reset_state()
     if accelerator.is_local_main_process:
          print("**Test last batch is not dropped when perfectly divisible**"	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							Accelerator()
     test_torch_metrics(__lowerCamelCase,       512	)
     accelerator.state._reset_state()






def     lowerCamelCase__			(__lowerCamelCase	):
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
       main()






from __future__ import annotations

from scipy.special import comb  # type: ignore





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							list_of_points
            # Degree determines the flexibility of the curve.
            # Degree = 1 will produce a straight line.
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							len(__lowerCamelCase							) - 1




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     list[float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."
            _SCREAMING_SNAKE_CASE       :	list[float]						    =							[]
            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     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     tuple[float, float]:
            assert 0 <= t <= 1, "Time t must be between 0 and 1."

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.basis_function(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							0.0
            _SCREAMING_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     UpperCamelCase_			(      self    ,						__lowerCamelCase = 0.01							)			->     int:
            from matplotlib import pyplot as plt  # type: ignore

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

            _SCREAMING_SNAKE_CASE       :	Dict						    =							0.0
            while t <= 1:
                 _SCREAMING_SNAKE_CASE       :	str						    =							self.bezier_curve_function(__lowerCamelCase							)
                 to_plot_x.append(value[0]							)
                 to_plot_y.append(value[1]							)
                 t += step_size

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[i[0] for i in self.list_of_points]
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[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