"""simple docstring"""





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
 import jax
 import jax.numpy as jnp



@slow
@require_flax
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					List[Any]				):
           return F'''gaussian_noise_s={seed}_shape={"_".join([str(lowerCAmelCase__				) for s in shape]				)}.npy'''






     def      lowerCamelCase__     (	self  :					Dict				):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					List[Any]=0					,				UpperCamelCase_  :					Union[str, Any]=(4, 4, 6_4, 6_4)					,				UpperCamelCase_  :					List[Any]=False				):
           lowerCAmelCase					:	List[Any]       =   jnp.bfloataa if fpaa else jnp.floataa
           lowerCAmelCase					:	Tuple       =   jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase__					,				lowerCAmelCase__				)				)					,				dtype=lowerCAmelCase__				)
           return image






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					Union[str, Any]=False					,				UpperCamelCase_  :					Dict="CompVis/stable-diffusion-v1-4"				):
           lowerCAmelCase					:	Dict       =   jnp.bfloataa if fpaa else jnp.floataa
           lowerCAmelCase					:	Tuple       =   "bf16" if fpaa else None

           lowerCAmelCase					:	Optional[int]       =   FlaxUNetaDConditionModel.from_pretrained(
               lowerCAmelCase__					,				subfolder='''unet'''					,				dtype=lowerCAmelCase__					,				revision=lowerCAmelCase__				)
           return model, params






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Optional[Any]=0					,				UpperCamelCase_  :					Dict=(4, 7_7, 7_6_8)					,				UpperCamelCase_  :					Tuple=False				):
           lowerCAmelCase					:	int       =   jnp.bfloataa if fpaa else jnp.floataa
           lowerCAmelCase					:	Optional[Any]       =   jnp.array(load_hf_numpy(self.get_file_format(lowerCAmelCase__					,				lowerCAmelCase__				)				)					,				dtype=lowerCAmelCase__				)
           return hidden_states






     @parameterized.expand(
         [
             # fmt: off
             [8_3, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
             [1_7, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
             [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
             [3, 1_0_0_0, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
             # fmt: on
         ]				)
     def      lowerCamelCase__     (	self  :					Tuple					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	List[Any]       =   self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''					,				fpaa=lowerCAmelCase__				)
           lowerCAmelCase					:	List[Any]       =   self.get_latents(lowerCAmelCase__					,				fpaa=lowerCAmelCase__				)
           lowerCAmelCase					:	str       =   self.get_encoder_hidden_states(lowerCAmelCase__					,				fpaa=lowerCAmelCase__				)

           lowerCAmelCase					:	int       =   model.apply(
               {'''params''': params}					,				lowerCAmelCase__					,				jnp.array(lowerCAmelCase__					,				dtype=jnp.intaa				)					,				encoder_hidden_states=lowerCAmelCase__					,				).sample

           assert sample.shape == latents.shape

           lowerCAmelCase					:	Dict       =   jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)					,				dtype=jnp.floataa				)
           lowerCAmelCase					:	str       =   jnp.array(lowerCAmelCase__					,				dtype=jnp.floataa				)

           # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
           assert jnp.allclose(lowerCAmelCase__					,				lowerCAmelCase__					,				atol=1E-2				)






     @parameterized.expand(
         [
             # fmt: off
             [8_3, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
             [1_7, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
             [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
             [3, 1_0_0_0, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
             # fmt: on
         ]				)
     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	int       =   self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''					,				fpaa=lowerCAmelCase__				)
           lowerCAmelCase					:	Dict       =   self.get_latents(lowerCAmelCase__					,				shape=(4, 4, 9_6, 9_6)					,				fpaa=lowerCAmelCase__				)
           lowerCAmelCase					:	List[Any]       =   self.get_encoder_hidden_states(lowerCAmelCase__					,				shape=(4, 7_7, 1_0_2_4)					,				fpaa=lowerCAmelCase__				)

           lowerCAmelCase					:	Optional[int]       =   model.apply(
               {'''params''': params}					,				lowerCAmelCase__					,				jnp.array(lowerCAmelCase__					,				dtype=jnp.intaa				)					,				encoder_hidden_states=lowerCAmelCase__					,				).sample

           assert sample.shape == latents.shape

           lowerCAmelCase					:	int       =   jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())				)					,				dtype=jnp.floataa				)
           lowerCAmelCase					:	Optional[Any]       =   jnp.array(lowerCAmelCase__					,				dtype=jnp.floataa				)

           # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
           assert jnp.allclose(lowerCAmelCase__					,				lowerCAmelCase__					,				atol=1E-2				)






"""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				:			Tuple ,   _snake_case				:			Union[str, Any]=10			):
      lowerCAmelCase					:	Dict       =   []
      for _ in range(_snake_case			):
            lrs.append(scheduler.get_lr()[0]			)
            scheduler.step()
      return lrs


def      _snake_case						(						_snake_case				:			Optional[int] ,   _snake_case				:			int=10			):
      lowerCAmelCase					:	Optional[int]       =   []
      for step in range(_snake_case			):
            lrs.append(scheduler.get_lr()[0]			)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        lowerCAmelCase					:	List[str]       =   os.path.join(_snake_case ,   '''schedule.bin'''			)
                        torch.save(scheduler.state_dict() ,   _snake_case			)

                        lowerCAmelCase					:	List[Any]       =   torch.load(_snake_case			)
                        scheduler.load_state_dict(_snake_case			)
      return lrs



@require_torch
class 		snake_case_(       unittest.TestCase			):






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Any				):
           self.assertEqual(len(UpperCamelCase_				)					,				len(UpperCamelCase_				)				)
           for a, b in zip(UpperCamelCase_					,				UpperCamelCase_				):
                 self.assertAlmostEqual(UpperCamelCase_					,				UpperCamelCase_					,				delta=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Tuple				):
           lowerCAmelCase					:	Any       =   torch.tensor([0.1, -0.2, -0.1]					,				requires_grad=UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   torch.tensor([0.4, 0.2, -0.5]				)
           lowerCAmelCase					:	List[Any]       =   nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           lowerCAmelCase					:	Union[str, Any]       =   AdamW(params=[w]					,				lr=2E-1					,				weight_decay=0.0				)
           for _ in range(1_0_0				):
                 lowerCAmelCase					:	Union[str, Any]       =   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  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   torch.tensor([0.1, -0.2, -0.1]					,				requires_grad=UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   torch.tensor([0.4, 0.2, -0.5]				)
           lowerCAmelCase					:	Optional[int]       =   nn.MSELoss()
           # No warmup, constant schedule, no gradient clipping
           lowerCAmelCase					:	Any       =   Adafactor(
               params=[w]					,				lr=1E-2					,				eps=(1E-30, 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_0_0_0				):
                 lowerCAmelCase					:	List[Any]       =   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 		snake_case_(       unittest.TestCase			):
     __UpperCamelCase  =					nn.Linear(50							,					50			) if is_torch_available() else None
     __UpperCamelCase  =					AdamW(m.parameters()							,					lr=10.0			) if is_torch_available() else None
     __UpperCamelCase  =					10






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Union[str, 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  :					Union[str, Any]				):
           lowerCAmelCase					:	Tuple       =   {'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
           # schedulers doct format
           # function: (sched_args_dict, expected_learning_rates)
           lowerCAmelCase					:	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():
                 lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   data

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

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



class 		snake_case_:






     def __init__(	self  :					List[Any]					,				UpperCamelCase_  :					Any				):
           lowerCAmelCase					:	Tuple       =   fn






     def __call__(	self  :					Union[str, Any]					,				*UpperCamelCase_  :					Optional[Any]					,				**UpperCamelCase_  :					List[Any]				):
           return self.fn(*UpperCamelCase_					,				**UpperCamelCase_				)






     @classmethod
     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Optional[int]				):
           lowerCAmelCase					:	Union[str, Any]       =   list(map(self					,				scheduler.lr_lambdas				)				)


