
"""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						(				snake_case      :Union[str, Any]				,					snake_case      :Optional[int]=1_0		)      ->	Dict:
 __UpperCamelCase				  =       []
 for _ in range(UpperCAmelCase_		):
  lrs.append(scheduler.get_lr()[0]		)
  scheduler.step()
 return lrs






def 							A						(				snake_case      :Optional[Any]				,					snake_case      :Tuple=1_0		)      ->	Dict:
 __UpperCamelCase				  =       []
 for step in range(UpperCAmelCase_		):
  lrs.append(scheduler.get_lr()[0]		)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    __UpperCamelCase				  =       os.path.join(UpperCAmelCase_				,					'schedule.bin'		)
    torch.save(scheduler.state_dict()				,					UpperCAmelCase_		)

    __UpperCamelCase				  =       torch.load(UpperCAmelCase_		)
    scheduler.load_state_dict(UpperCAmelCase_		)
 return lrs

@require_torch
class __lowerCAmelCase		(      unittest.TestCase			):





     def 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase      ):

      '''simple docstring'''




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





     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       torch.tensor([0.1, -0.2, -0.1]	,     requires_grad=_lowerCamelCase      )
      __UpperCamelCase				  =       torch.tensor([0.4, 0.2, -0.5]      )
      __UpperCamelCase				  =       nn.MSELoss()
      # No warmup, constant schedule, no gradient clipping
      __UpperCamelCase				  =       AdamW(params=[w]	,     lr=2E-1	,     weight_decay=0.0      )
      for _ in range(100      ):
       __UpperCamelCase				  =       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 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       torch.tensor([0.1, -0.2, -0.1]	,     requires_grad=_lowerCamelCase      )
      __UpperCamelCase				  =       torch.tensor([0.4, 0.2, -0.5]      )
      __UpperCamelCase				  =       nn.MSELoss()
      # No warmup, constant schedule, no gradient clipping
      __UpperCamelCase				  =       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(1000      ):
       __UpperCamelCase				  =       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			):
     lowercase						   =						nn.Linear(50			,  50			) if is_torch_available() else None
     lowercase						   =						AdamW(m.parameters()			,  lr=10.0			) if is_torch_available() else None
     lowercase						   =						10





     def 						UpperCAmelCase					(	self	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase=None      ):

      '''simple docstring'''




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







     def 						UpperCAmelCase					(	self      ):

      '''simple docstring'''




      __UpperCamelCase				  =       {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
      # schedulers doct format
      # function: (sched_args_dict, expected_learning_rates)
      __UpperCamelCase				  =       {
          get_constant_schedule: ({}, [1_0.0] * self.num_steps),
          get_constant_schedule_with_warmup: (
              {'''num_warmup_steps''': 4},
              [0.0, 2.5, 5.0, 7.5, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0, 1_0.0],
          ),
          get_linear_schedule_with_warmup: (
              {**common_kwargs},
              [0.0, 5.0, 1_0.0, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
          ),
          get_cosine_schedule_with_warmup: (
              {**common_kwargs},
              [0.0, 5.0, 1_0.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
          ),
          get_cosine_with_hard_restarts_schedule_with_warmup: (
              {**common_kwargs, '''num_cycles''': 2},
              [0.0, 5.0, 1_0.0, 8.5_3, 5.0, 1.4_6, 1_0.0, 8.5_3, 5.0, 1.4_6],
          ),
          get_polynomial_decay_schedule_with_warmup: (
              {**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
              [0.0, 5.0, 1_0.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
          ),
          get_inverse_sqrt_schedule: (
              {'''num_warmup_steps''': 2},
              [0.0, 5.0, 1_0.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
          ),
      }

      for scheduler_func, data in scheds.items():
       __UpperCamelCase				  =       data

       __UpperCamelCase				  =       scheduler_func(self.optimizer	,     **_lowerCamelCase      )
       self.assertEqual(len([scheduler.get_lr()[0]]      )	,     1      )
       __UpperCamelCase				  =       unwrap_schedule(_lowerCamelCase	,     self.num_steps      )
       self.assertListAlmostEqual(
           _lowerCamelCase	,     _lowerCamelCase	,     tol=1E-2	,     msg=F'failed for {scheduler_func} in normal scheduler'	,     )

       __UpperCamelCase				  =       scheduler_func(self.optimizer	,     **_lowerCamelCase      )
       if scheduler_func.__name__ != "get_constant_schedule":
        LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase      )  # wrap to test picklability of the schedule
       __UpperCamelCase				  =       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		:





     def __init__(	self	,     __UpperCAmelCase      ):

      '''simple docstring'''




      __UpperCamelCase				  =       fn





     def __call__(	self	,     *__UpperCAmelCase	,     **__UpperCAmelCase      ):

      '''simple docstring'''




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







     @classmethod
     def 						UpperCAmelCase					(	self	,     __UpperCAmelCase      ):

      '''simple docstring'''




      __UpperCamelCase				  =       list(map(self	,     scheduler.lr_lambdas      )      )







from __future__ import annotations

import collections
import pprint
from pathlib import Path
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return "".join(sorted(UpperCAmelCase_						)						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return word_by_signature[signature(UpperCAmelCase_						)]


snake_case				:    str       				=     Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
snake_case				:    Optional[int]       				=     sorted({word.strip().lower() for word in data.splitlines()})

snake_case				:    str       				=     collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('''anagrams.txt''', '''w''') as file:
          file.write('''all_anagrams = \n ''')
          file.write(pprint.pformat(all_anagrams))





