




"""simple docstring"""

from ..utils import is_flax_available, is_torch_available


if is_torch_available():
       from .autoencoder_kl import AutoencoderKL
       from .controlnet import ControlNetModel
       from .dual_transformer_ad import DualTransformeraDModel
       from .modeling_utils import ModelMixin
       from .prior_transformer import PriorTransformer
       from .ta_film_transformer import TaFilmDecoder
       from .transformer_ad import TransformeraDModel
       from .unet_ad import UNetaDModel
       from .unet_ad import UNetaDModel
       from .unet_ad_condition import UNetaDConditionModel
       from .unet_ad_condition import UNetaDConditionModel
       from .vq_model import VQModel

if is_flax_available():
       from .controlnet_flax import FlaxControlNetModel
       from .unet_ad_condition_flax import FlaxUNetaDConditionModel
       from .vae_flax import FlaxAutoencoderKL







"""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 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   Optional[Any]=10				)       ->   Optional[int]:






      '''simple docstring'''


      _A     =  []
      for _ in range(_snake_case				):
            lrs.append(scheduler.get_lr()[0]				)
            scheduler.step()
      return lrs






def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   Union[str, Any]=10				)       ->   List[str]:






      '''simple docstring'''


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

                        _A     =  torch.load(_snake_case				)
                        scheduler.load_state_dict(_snake_case				)
      return lrs





@require_torch
class 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple						):
       self.assertEqual(len(_UpperCAmelCase						)       ,				len(_UpperCAmelCase						)						)
       for a, b in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             self.assertAlmostEqual(_UpperCAmelCase       ,				_UpperCAmelCase       ,				delta=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=_UpperCAmelCase						)
       _A     =  torch.tensor([0.4, 0.2, -0.5]						)
       _A     =  nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _A     =  AdamW(params=[w]       ,				lr=2E-1       ,				weight_decay=0.0						)
       for _ in range(100						):
             _A     =  criterion(_UpperCAmelCase       ,				_UpperCAmelCase						)
             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      lowerCAmelCase_				(					self			:			int						):
       _A     =  torch.tensor([0.1, -0.2, -0.1]       ,				requires_grad=_UpperCAmelCase						)
       _A     =  torch.tensor([0.4, 0.2, -0.5]						)
       _A     =  nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _A     =  Adafactor(
           params=[w]       ,				lr=1E-2       ,				eps=(1E-3_0, 1E-3)       ,				clip_threshold=1.0       ,				decay_rate=-0.8       ,				betaa=_UpperCAmelCase       ,				weight_decay=0.0       ,				relative_step=_UpperCAmelCase       ,				scale_parameter=_UpperCAmelCase       ,				warmup_init=_UpperCAmelCase       ,				)
       for _ in range(1_000						):
             _A     =  criterion(_UpperCAmelCase       ,				_UpperCAmelCase						)
             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 							lowercase_			(	unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      nn.Linear(50     ,      50				) if is_torch_available() else None
 UpperCAmelCase       :	Tuple     =      AdamW(m.parameters()     ,      lr=10.0				) if is_torch_available() else None
 UpperCAmelCase       :	Dict     =      10

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			List[Any]       ,				_UpperCAmelCase			:			List[Any]=None						):
       self.assertEqual(len(_UpperCAmelCase						)       ,				len(_UpperCAmelCase						)						)
       for a, b in zip(_UpperCAmelCase       ,				_UpperCAmelCase						):
             self.assertAlmostEqual(_UpperCAmelCase       ,				_UpperCAmelCase       ,				delta=_UpperCAmelCase       ,				msg=_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  {'num_warmup_steps': 2, 'num_training_steps': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _A     =  {
           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():
             _A  ,       _A     =  data

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

             _A     =  scheduler_func(self.optimizer       ,				**_UpperCAmelCase						)
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_UpperCAmelCase						)  # wrap to test picklability of the schedule
             _A     =  unwrap_and_save_reload_schedule(_UpperCAmelCase       ,				self.num_steps						)
             self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase       ,				msg=F'''failed for {scheduler_func} in save and reload'''						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]						):
       _A     =  fn

 def __call__(					self			:			Tuple       ,				*_UpperCAmelCase			:			List[str]       ,				**_UpperCAmelCase			:			List[str]						):
       return self.fn(*_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Any						):
       _A     =  list(map(self       ,				scheduler.lr_lambdas						)						)

