
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        Adafactor,
        AdamW,
        get_constant_schedule,
        get_constant_schedule_with_warmup,
        get_cosine_schedule_with_warmup,
        get_cosine_with_hard_restarts_schedule_with_warmup,
        get_inverse_sqrt_schedule,
        get_linear_schedule_with_warmup,
        get_polynomial_decay_schedule_with_warmup,
    )


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=1_0		):
      UpperCamelCase__    :   List[str]					=				[]
      for _ in range(lowerCamelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=1_0		):
      UpperCamelCase__    :   str					=				[]
      for step in range(lowerCamelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        UpperCamelCase__    :   Optional[Any]					=				os.path.join(lowerCamelCase_ ,		'''schedule.bin'''		)
                        torch.save(scheduler.state_dict() ,		lowerCamelCase_		)

                        UpperCamelCase__    :   int					=				torch.load(lowerCamelCase_		)
                        scheduler.load_state_dict(lowerCamelCase_		)
      return lrs




@require_torch
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           self.assertEqual(len(__lowerCamelCase  )						,						len(__lowerCamelCase  )  )
           for a, b in zip(__lowerCamelCase						,						__lowerCamelCase  ):
                 self.assertAlmostEqual(__lowerCamelCase						,						__lowerCamelCase						,						delta=__lowerCamelCase  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				torch.tensor([0.1, -0.2, -0.1]						,						requires_grad=__lowerCamelCase  )
           UpperCamelCase__    :   Optional[int]					=				torch.tensor([0.4, 0.2, -0.5]  )
           UpperCamelCase__    :   List[str]					=				nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           UpperCamelCase__    :   Dict					=				AdamW(params=[w]						,						lr=2e-1						,						weight_decay=0.0  )
           for _ in range(1_0_0  ):
                 UpperCamelCase__    :   Optional[int]					=				criterion(__lowerCamelCase						,						__lowerCamelCase  )
                 loss.backward()
                 optimizer.step()
                 w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
                 w.grad.zero_()
           self.assertListAlmostEqual(w.tolist()						,						[0.4, 0.2, -0.5]						,						tol=1e-2  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				torch.tensor([0.1, -0.2, -0.1]						,						requires_grad=__lowerCamelCase  )
           UpperCamelCase__    :   Optional[Any]					=				torch.tensor([0.4, 0.2, -0.5]  )
           UpperCamelCase__    :   List[str]					=				nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           UpperCamelCase__    :   List[str]					=				Adafactor(
               params=[w]						,						lr=1e-2						,						eps=(1e-30, 1e-3)						,						clip_threshold=1.0						,						decay_rate=-0.8						,						betaa=__lowerCamelCase						,						weight_decay=0.0						,						relative_step=__lowerCamelCase						,						scale_parameter=__lowerCamelCase						,						warmup_init=__lowerCamelCase						,						)
           for _ in range(1_0_0_0  ):
                 UpperCamelCase__    :   Tuple					=				criterion(__lowerCamelCase						,						__lowerCamelCase  )
                 loss.backward()
                 optimizer.step()
                 w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
                 w.grad.zero_()
           self.assertListAlmostEqual(w.tolist()						,						[0.4, 0.2, -0.5]						,						tol=1e-2  )




@require_torch
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  nn.Linear(50	,							50 ) if is_torch_available() else None
     SCREAMING_SNAKE_CASE_		   =  AdamW(m.parameters()	,							lr=10.0 ) if is_torch_available() else None
     SCREAMING_SNAKE_CASE_		   =  10






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=None  )  ->   Any:




           """simple docstring"""







           self.assertEqual(len(__lowerCamelCase  )						,						len(__lowerCamelCase  )  )
           for a, b in zip(__lowerCamelCase						,						__lowerCamelCase  ):
                 self.assertAlmostEqual(__lowerCamelCase						,						__lowerCamelCase						,						delta=__lowerCamelCase						,						msg=__lowerCamelCase  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				{'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           UpperCamelCase__    :   Optional[Any]					=				{
               get_constant_schedule: ({}, [10.0] * self.num_steps),
               get_constant_schedule_with_warmup: (
                   {'''num_warmup_steps''': 4},
                   [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
               ),
               get_linear_schedule_with_warmup: (
                   {**common_kwargs},
                   [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
               ),
               get_cosine_schedule_with_warmup: (
                   {**common_kwargs},
                   [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
               ),
               get_cosine_with_hard_restarts_schedule_with_warmup: (
                   {**common_kwargs, '''num_cycles''': 2},
                   [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
               ),
               get_polynomial_decay_schedule_with_warmup: (
                   {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
                   [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
               ),
               get_inverse_sqrt_schedule: (
                   {'''num_warmup_steps''': 2},
                   [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
               ),
           }

           for scheduler_func, data in scheds.items():
                 UpperCamelCase__    :   List[Any]					=				data

                 UpperCamelCase__    :   str					=				scheduler_func(self.optimizer						,						**__lowerCamelCase  )
                 self.assertEqual(len([scheduler.get_lr()[0]]  )						,						1  )
                 UpperCamelCase__    :   Union[str, Any]					=				unwrap_schedule(__lowerCamelCase						,						self.num_steps  )
                 self.assertListAlmostEqual(
                     __lowerCamelCase						,						__lowerCamelCase						,						tol=1e-2						,						msg=F'''failed for {scheduler_func} in normal scheduler'''						,						)

                 UpperCamelCase__    :   Union[str, Any]					=				scheduler_func(self.optimizer						,						**__lowerCamelCase  )
                 if scheduler_func.__name__ != "get_constant_schedule":
                       LambdaScheduleWrapper.wrap_scheduler(__lowerCamelCase  )  # wrap to test picklability of the schedule
                 UpperCamelCase__    :   Optional[int]					=				unwrap_and_save_reload_schedule(__lowerCamelCase						,						self.num_steps  )
                 self.assertListEqual(__lowerCamelCase						,						__lowerCamelCase						,						msg=F'''failed for {scheduler_func} in save and reload'''  )




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				fn






     def __call__(							self						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           return self.fn(*__lowerCamelCase						,						**__lowerCamelCase  )






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				list(map(self						,						scheduler.lr_lambdas  )  )








from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          return x**3 + x**2

    print("f(x) = x^3 + x^2")
    print("The area between the curve, x = -5, x = 5 and the x axis is:")
    lowerCamelCase      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


