






'''simple docstring'''
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  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Tuple=1_0							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[]
      for _ in range(UpperCamelCase__							):
            lrs.append(scheduler.get_lr()[0]							)
            scheduler.step()
      return lrs





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Union[str, Any]=1_0							):




      '''simple docstring'''






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

                        SCREAMING_SNAKE_CASE__     :				Dict									=torch.load(UpperCamelCase__							)
                        scheduler.load_state_dict(UpperCamelCase__							)
      return lrs






@require_torch
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Optional[int]					,      __lowercase	:							List[str]      )	->						int:
       self.assertEqual(len(__lowercase      )					,      len(__lowercase      )      )
       for a, b in zip(__lowercase					,      __lowercase      ):
             self.assertAlmostEqual(__lowercase					,      __lowercase					,      delta=__lowercase      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.tensor([0.1, -0.2, -0.1]					,      requires_grad=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor([0.4, 0.2, -0.5]      )
       SCREAMING_SNAKE_CASE__     :				Dict									=nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=AdamW(params=[w]					,      lr=2e-1					,      weight_decay=0.0      )
       for _ in range(1_00      ):
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=criterion(__lowercase					,      __lowercase      )
             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      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.tensor([0.1, -0.2, -0.1]					,      requires_grad=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.tensor([0.4, 0.2, -0.5]      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE__     :				Dict									=Adafactor(
           params=[w]					,      lr=1e-2					,      eps=(1e-30, 1e-3)					,      clip_threshold=1.0					,      decay_rate=-0.8					,      betaa=__lowercase					,      weight_decay=0.0					,      relative_step=__lowercase					,      scale_parameter=__lowercase					,      warmup_init=__lowercase					,      )
       for _ in range(10_00      ):
             SCREAMING_SNAKE_CASE__     :				List[Any]									=criterion(__lowercase					,      __lowercase      )
             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      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							nn.Linear(50	,				50				) if is_torch_available() else None
 snake_case_  							=							AdamW(m.parameters()	,				lr=10.0				) if is_torch_available() else None
 snake_case_  							=							10




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[int]=None      )	->						Union[str, Any]:
       self.assertEqual(len(__lowercase      )					,      len(__lowercase      )      )
       for a, b in zip(__lowercase					,      __lowercase      ):
             self.assertAlmostEqual(__lowercase					,      __lowercase					,      delta=__lowercase					,      msg=__lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									={'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       SCREAMING_SNAKE_CASE__     :				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():
             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[int]									=data

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

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






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							str					,      __lowercase	:							Any      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				str									=fn




 def __call__(      self	:							Union[str, Any]					,      *__lowercase	:							Any					,      **__lowercase	:							Optional[Any]      )	->						int:
       return self.fn(*__lowercase					,      **__lowercase      )




 @classmethod
 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=list(map(self					,      scheduler.lr_lambdas      )      )







'''simple docstring'''
def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=len(UpperCamelCase__							) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(UpperCamelCase__							):
                  return None

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=left
                        SCREAMING_SNAKE_CASE__     :				Optional[Any]									=point
                  elif point > right:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									=right
                        SCREAMING_SNAKE_CASE__     :				Tuple									=point
                  else:
                        if item < current_item:
                              SCREAMING_SNAKE_CASE__     :				str									=point - 1
                        else:
                              SCREAMING_SNAKE_CASE__     :				Tuple									=point + 1
      return None





def  _a(  UpperCamelCase__				:						List[str],	UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						Union[str, Any]							):




      '''simple docstring'''






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      SCREAMING_SNAKE_CASE__     :				Dict									=left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(UpperCamelCase__							):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      elif point > right:
            return interpolation_search_by_recursion(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__							)
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	point - 1							)
            else:
                  return interpolation_search_by_recursion(
                      UpperCamelCase__,	UpperCamelCase__,	point + 1,	UpperCamelCase__							)





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      if collection != sorted(UpperCamelCase__							):
            raise ValueError('''Collection must be ascending sorted'''							)
      return True


if __name__ == "__main__":
  import sys
  a_					= 0
  if debug == 1:
    a_					= [1_0, 3_0, 4_0, 4_5, 5_0, 6_6, 7_7, 9_3]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit('Sequence must be ascending sorted to apply interpolation search')
    a_					= 6_7

  a_					= interpolation_search(collection, target)
  if result is not None:
    print(F'''{target} found at positions: {result}''')
  else:
    print('Not found')