
"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 						lowercase:
     __snake_case:  CommonSchedulerState

     # setable values
     __snake_case:  jnp.ndarray
     __snake_case:  jnp.ndarray
     __snake_case:  jnp.ndarray
     __snake_case:  Optional[int]  				=    None

     # running values
     __snake_case:  Optional[jnp.ndarray]  				=    None



     @classmethod
     def  lowercase__     (       cls    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    Dict:







         """simple docstring"""



         return cls(common=__SCREAMING_SNAKE_CASE    ,							init_noise_sigma=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE    ,							sigmas=__SCREAMING_SNAKE_CASE      )







@dataclass
class 						lowercase(_lowercase					):
     __snake_case:  LMSDiscreteSchedulerState







class 						lowercase(_lowercase						,	_lowercase					):
     __snake_case:  Any  				=    [e.name for e in FlaxKarrasDiffusionSchedulers]

     __snake_case:  jnp.dtype



     @property
     def  lowercase__     (       self      )		->    List[str]:







         """simple docstring"""



         return True



     @register_to_config
     def __init__(       self    ,							__SCREAMING_SNAKE_CASE = 1_0_0_0    ,							__SCREAMING_SNAKE_CASE = 0.00_01    ,							__SCREAMING_SNAKE_CASE = 0.02    ,							__SCREAMING_SNAKE_CASE = "linear"    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = "epsilon"    ,							__SCREAMING_SNAKE_CASE = jnp.floataa    ,							)		->    Dict:







         """simple docstring"""



         a__    = dtype



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE = None      )		->    LMSDiscreteSchedulerState:







         """simple docstring"""



         if common is None:
             a__    = CommonSchedulerState.create(self      )

         a__    = jnp.arange(0    ,							self.config.num_train_timesteps      ).round()[::-1]
         a__    = ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

         # standard deviation of the initial noise distribution
         a__    = sigmas.max()

         return LMSDiscreteSchedulerState.create(
             common=__SCREAMING_SNAKE_CASE    ,							init_noise_sigma=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE    ,							sigmas=__SCREAMING_SNAKE_CASE    ,							)



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )		->    jnp.ndarray:







         """simple docstring"""



         ((a__    )  ,        )    = jnp.where(state.timesteps == timestep    ,							size=1      )
         a__    = step_index[0]

         a__    = state.sigmas[step_index]
         a__    = sample / ((sigma**2 + 1) ** 0.5)
         return sample



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







         """simple docstring"""



         def lms_derivative(__SCREAMING_SNAKE_CASE      ):
             a__    = 1.0
             for k in range(__SCREAMING_SNAKE_CASE      ):
                 if current_order == k:
                     continue
                 prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
             return prod

         a__    = integrate.quad(__SCREAMING_SNAKE_CASE    ,							state.sigmas[t]    ,							state.sigmas[t + 1]    ,							epsrel=1e-4      )[0]

         return integrated_coeff



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = ()      )		->    LMSDiscreteSchedulerState:







         """simple docstring"""



         a__    = jnp.linspace(self.config.num_train_timesteps - 1    ,							0    ,							__SCREAMING_SNAKE_CASE    ,							dtype=self.dtype      )

         a__    = jnp.floor(__SCREAMING_SNAKE_CASE      ).astype(jnp.intaa      )
         a__    = jnp.ceil(__SCREAMING_SNAKE_CASE      ).astype(jnp.intaa      )

         a__    = jnp.mod(__SCREAMING_SNAKE_CASE    ,							1.0      )

         a__    = ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
         a__    = (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
         a__    = jnp.concatenate([sigmas, jnp.array([0.0]    ,							dtype=self.dtype      )]      )

         a__    = timesteps.astype(jnp.intaa      )

         # initial running values
         a__    = jnp.zeros((0,) + shape    ,							dtype=self.dtype      )

         return state.replace(
             timesteps=__SCREAMING_SNAKE_CASE    ,							sigmas=__SCREAMING_SNAKE_CASE    ,							num_inference_steps=__SCREAMING_SNAKE_CASE    ,							derivatives=__SCREAMING_SNAKE_CASE    ,							)



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 4    ,							__SCREAMING_SNAKE_CASE = True    ,							)		->    Union[FlaxLMSSchedulerOutput, Tuple]:







         """simple docstring"""



         if state.num_inference_steps is None:
             raise ValueError(
                 'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'      )

         a__    = state.sigmas[timestep]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
             a__    = sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             a__    = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

         # 2. Convert to an ODE derivative
         a__    = (sample - pred_original_sample) / sigma
         a__    = state.replace(derivatives=jnp.append(state.derivatives    ,							__SCREAMING_SNAKE_CASE      )      )
         if len(state.derivatives      ) > order:
             a__    = state.replace(derivatives=jnp.delete(state.derivatives    ,							0      )      )

         # 3. Compute linear multistep coefficients
         a__    = min(timestep + 1    ,							__SCREAMING_SNAKE_CASE      )
         a__    = [self.get_lms_coefficient(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      ) for curr_order in range(__SCREAMING_SNAKE_CASE      )]

         # 4. Compute previous sample based on the derivatives path
         a__    = sample + sum(
             coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE    ,							reversed(state.derivatives      )      )      )

         if not return_dict:
             return (prev_sample, state)

         return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE    ,							state=__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							)		->    jnp.ndarray:







         """simple docstring"""



         a__    = state.sigmas[timesteps].flatten()
         a__    = broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE    ,							noise.shape      )

         a__    = original_samples + noise * sigma

         return noisy_samples




     def __len__(       self      )		->    Any:







         """simple docstring"""



         return self.config.num_train_timesteps








"""simple docstring"""




import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


a		:					Tuple    	=					namedtuple('ExampleNamedTuple', 'a b c')







class 						lowercase(unittest.TestCase					):



     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = torch.randn(5    ,							2      )
         a__    = torch.device('cuda'      ) if torch.cuda.is_available() else torch.device('cpu'      )

         a__    = send_to_device(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta.cpu()    ,							__SCREAMING_SNAKE_CASE      )      )

         a__    = send_to_device((tensor, [tensor, tensor], 1)    ,							__SCREAMING_SNAKE_CASE      )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta[0].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertIsInstance(resulta[1]    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta[1][0].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertTrue(torch.equal(resulta[1][1].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertEqual(resulta[2]    ,							1      )

         a__    = send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}    ,							__SCREAMING_SNAKE_CASE      )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta['a'].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertIsInstance(resulta['b']    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta['b'][0].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertTrue(torch.equal(resulta['b'][1].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertEqual(resulta['c']    ,							1      )

         a__    = send_to_device(ExampleNamedTuple(a=__SCREAMING_SNAKE_CASE    ,							b=[tensor, tensor]    ,							c=1      )    ,							__SCREAMING_SNAKE_CASE      )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta.a.cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertIsInstance(resulta.b    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta.b[0].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertTrue(torch.equal(resulta.b[1].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertEqual(resulta.c    ,							1      )

         a__    = send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}      )    ,							__SCREAMING_SNAKE_CASE      )
         self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta['a'].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertIsInstance(resulta['b']    ,							__SCREAMING_SNAKE_CASE      )
         self.assertTrue(torch.equal(resulta['b'][0].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertTrue(torch.equal(resulta['b'][1].cpu()    ,							__SCREAMING_SNAKE_CASE      )      )
         self.assertEqual(resulta['c']    ,							1      )



     def  lowercase__     (       self      )		->    Dict:







         """simple docstring"""



         with self.assertRaises(__SCREAMING_SNAKE_CASE      ) as cm:
             a__    = recursively_apply(torch.tensor    ,							(torch.tensor(1      ), 1)    ,							error_on_other_type=__SCREAMING_SNAKE_CASE      )
         self.assertEqual(
             str(cm.exception      )    ,							'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'    ,							)



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         a__    = torch.tensor([1, 2, 3, 4, 5]      )
         self.assertEqual(listify(__SCREAMING_SNAKE_CASE      )    ,							[1, 2, 3, 4, 5]      )

         a__    = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]      )
         self.assertEqual(listify(__SCREAMING_SNAKE_CASE      )    ,							[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]      )

         a__    = torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]      )
         self.assertEqual(
             listify(__SCREAMING_SNAKE_CASE      )    ,							[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]      )



     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         with patch_environment(aa=1    ,							BB=2      ):
             self.assertEqual(os.environ.get('AA'      )    ,							'1'      )
             self.assertEqual(os.environ.get('BB'      )    ,							'2'      )

         self.assertNotIn('AA'    ,							os.environ      )
         self.assertNotIn('BB'    ,							os.environ      )



     def  lowercase__     (       self      )		->    Tuple:







         """simple docstring"""



         a__    = RegressionModel()
         a__    = model.forward
         a__    = convert_outputs_to_fpaa(model.forward      )
         a__    = extract_model_from_parallel(__SCREAMING_SNAKE_CASE    ,							keep_fpaa_wrapper=__SCREAMING_SNAKE_CASE      )
         a__    = pickle.dumps(__SCREAMING_SNAKE_CASE      )



     @require_cuda
     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = RegressionModel()
         a__    = model.forward
         a__    = torch.cuda.amp.autocast(dtype=torch.floataa      )(model.forward      )
         a__    = convert_outputs_to_fpaa(model.forward      )
         a__    = extract_model_from_parallel(__SCREAMING_SNAKE_CASE    ,							keep_fpaa_wrapper=__SCREAMING_SNAKE_CASE      )
         a__    = pickle.dumps(__SCREAMING_SNAKE_CASE      )



     @require_cuda
     @require_torch_min_version(version='2.0'      )
     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



         a__    = RegressionModel()
         a__    = model.forward
         a__    = torch.cuda.amp.autocast(dtype=torch.floataa      )(model.forward      )
         a__    = convert_outputs_to_fpaa(model.forward      )
         a__    = torch.compile(model.forward    ,							backend='inductor'      )
         a__    = torch.randn(4    ,							1_0      ).cuda()
         a__    = model(__SCREAMING_SNAKE_CASE      )



     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         a__    = RegressionModel()
         # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
         a__    = torch.nn.parallel.DataParallel(__SCREAMING_SNAKE_CASE      )
         a__    = extract_model_from_parallel(__SCREAMING_SNAKE_CASE      )

         self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE      )



     @require_torch_min_version(version='2.0'      )
     def  lowercase__     (       self      )		->    str:







         """simple docstring"""



         a__    = RegressionModel()
         a__    = torch.compile(__SCREAMING_SNAKE_CASE      )

         # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
         a__    = torch.nn.parallel.DataParallel(__SCREAMING_SNAKE_CASE      )
         a__    = torch.compile(__SCREAMING_SNAKE_CASE      )
         a__    = extract_model_from_parallel(__SCREAMING_SNAKE_CASE      )

         self.assertEqual(compiled_model._orig_mod    ,							compiled_model_unwrapped._orig_mod      )




     def  lowercase__     (       self      )		->    Optional[Any]:







         """simple docstring"""



         self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]      )]      )    ,							torch.device('cpu'      )      )
         self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]      )}      )    ,							torch.device('cpu'      )      )
         self.assertIsNone(find_device([1, 'a']      )      )


