





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:  # picklable for multiprocessing






				"""simple docstring"""


				return x.sum()






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:  # picklable for multiprocessing






				"""simple docstring"""


				return i + 1



@dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					int
			lowerCamelCase_					:					str




class  __lowerCAmelCase	(      _a					):






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  {}
							snake_case_						:   Union[str, Any]           =  []
							snake_case_						:   Dict           =  1
							snake_case_						:   Union[str, Any]           =  [1, 2]
							snake_case_						:   Tuple           =  {'''a''': 1, '''b''': 2}
							snake_case_						:   Union[str, Any]           =  {'''a''': [1, 2], '''b''': [3, 4]}
							snake_case_						:   List[Any]           =  {'''a''': {'''1''': 1}, '''b''': 2}
							snake_case_						:   Optional[Any]           =  {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
							snake_case_						:   int           =  {}
							snake_case_						:   List[Any]           =  []
							snake_case_						:   Dict           =  2
							snake_case_						:   Any           =  [2, 3]
							snake_case_						:   Any           =  {'''a''': 2, '''b''': 3}
							snake_case_						:   List[Any]           =  {'''a''': [2, 3], '''b''': [4, 5]}
							snake_case_						:   Any           =  {'''a''': {'''1''': 2}, '''b''': 3}
							snake_case_						:   List[Any]           =  {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__   ) ,							__magic_name__   )

							snake_case_						:   int           =  2
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )

							snake_case_						:   str           =  {'''a''': np.eye(2   ), '''b''': np.zeros(3   ), '''c''': np.ones(2   )}
							snake_case_						:   Union[str, Any]           =  {'''a''': 2, '''b''': 0, '''c''': 2}
							snake_case_						:   Tuple           =  {
							    '''a''': np.eye(2   ).astype(__magic_name__   ),
							    '''b''': np.zeros(3   ).astype(__magic_name__   ),
							    '''c''': np.ones(2   ).astype(__magic_name__   ),
							}
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(
							    {k: v.tolist() for k, v in map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__   ).items()} ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,							)
							self.assertEqual(map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__ ,							num_proc=__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(
							    {k: v.tolist() for k, v in map_nested(__magic_name__ ,							__magic_name__ ,							map_numpy=__magic_name__ ,							num_proc=__magic_name__   ).items()} ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,							)
							with self.assertRaises(__magic_name__   ):  # can't pickle a local lambda
											map_nested(lambda __magic_name__   : x + 1 ,							__magic_name__ ,							num_proc=__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Dict           =  {'''a''': 1, '''b''': 2}
							snake_case_						:   Any           =  {'''a''': 3, '''b''': 4}
							snake_case_						:   Tuple           =  {'''a''': 5, '''b''': 6}
							snake_case_						:   List[Any]           =  sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]   )
							self.assertEqual(sorted(zip_dict(__magic_name__ ,							__magic_name__ ,							__magic_name__   )   ) ,							__magic_name__   )






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


							'''simple docstring'''



							class  __lowerCAmelCase	:
										lowerCamelCase_					:					Union[str, Any]	      =				'''bar'''




							snake_case_						:   int           =  Foo()
							self.assertEqual(foo.my_attr ,							'''bar'''   )
							with temporary_assignment(__magic_name__ ,							'''my_attr''' ,							'''BAR'''   ):
											self.assertEqual(foo.my_attr ,							'''BAR'''   )
							self.assertEqual(foo.my_attr ,							'''bar'''   )






@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc'''			,    [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]			,    )
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				with patch('''datasets.utils.py_utils._single_map_nested'''			) as mock_single_map_nested, patch(
				    '''datasets.parallel.parallel.Pool'''			) as mock_multiprocessing_pool:
								snake_case_						:   Optional[int]           =  {f'''{i}''': i for i in range(_UpperCamelCase			)}
								snake_case_						:   Optional[Any]           =  map_nested(lambda _UpperCamelCase			: x + 10			,    _UpperCamelCase			,    num_proc=_UpperCamelCase			,    parallel_min_length=16			)
								if expected_num_proc == 1:
												assert mock_single_map_nested.called
												assert not mock_multiprocessing_pool.called
								else:
												assert not mock_single_map_nested.called
												assert mock_multiprocessing_pool.called
												assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc



class  __lowerCAmelCase	(      _a					):






			@require_tf
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							import tensorflow as tf
							from tensorflow.keras import layers

							snake_case_						:   Optional[Any]           =  layers.Dense(2   )

							def gen_random_output():
											snake_case_						:   Union[str, Any]           =  tf.random.uniform((1, 3)   )
											return model(__magic_name__   ).numpy()

							with temp_seed(42 ,							set_tensorflow=__magic_name__   ):
											snake_case_						:   Tuple           =  gen_random_output()
							with temp_seed(42 ,							set_tensorflow=__magic_name__   ):
											snake_case_						:   int           =  gen_random_output()
							snake_case_						:   Any           =  gen_random_output()

							np.testing.assert_equal(__magic_name__ ,							__magic_name__   )
							self.assertGreater(np.abs(outa - outa   ).sum() ,							0   )






			@require_torch
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							import torch

							def gen_random_output():
											snake_case_						:   List[Any]           =  torch.nn.Linear(3 ,							2   )
											snake_case_						:   Dict           =  torch.rand(1 ,							3   )
											return model(__magic_name__   ).detach().numpy()

							with temp_seed(42 ,							set_pytorch=__magic_name__   ):
											snake_case_						:   Optional[int]           =  gen_random_output()
							with temp_seed(42 ,							set_pytorch=__magic_name__   ):
											snake_case_						:   Any           =  gen_random_output()
							snake_case_						:   List[str]           =  gen_random_output()

							np.testing.assert_equal(__magic_name__ ,							__magic_name__   )
							self.assertGreater(np.abs(outa - outa   ).sum() ,							0   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							def gen_random_output():
											return np.random.rand(1 ,							3   )

							with temp_seed(42   ):
											snake_case_						:   int           =  gen_random_output()
							with temp_seed(42   ):
											snake_case_						:   List[str]           =  gen_random_output()
							snake_case_						:   int           =  gen_random_output()

							np.testing.assert_equal(__magic_name__ ,							__magic_name__   )
							self.assertGreater(np.abs(outa - outa   ).sum() ,							0   )






@pytest.mark.parametrize('''input_data'''			,    [{}]			)
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  NestedDataStructure(_UpperCamelCase			).data
				assert output_data == input_data






@pytest.mark.parametrize(
    '''data, expected_output'''			,    [
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ]			,    )
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   Tuple           =  NestedDataStructure(_UpperCamelCase			).flatten()
				assert output == expected_output






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






				"""simple docstring"""


				snake_case_						:   Tuple           =  A(x=1			,    y='''foobar'''			)
				snake_case_						:   List[Any]           =  {'''x''': 1, '''y''': '''foobar'''}
				assert asdict(_UpperCamelCase			) == expected_output

				snake_case_						:   Tuple           =  {'''a''': {'''b''': A(x=10			,    y='''foo'''			)}, '''c''': [A(x=20			,    y='''bar'''			)]}
				snake_case_						:   List[str]           =  {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
				assert asdict(_UpperCamelCase			) == expected_output

				with pytest.raises(_UpperCamelCase			):
								asdict([1, A(x=10			,    y='''foo'''			)]			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return text.split()






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				yield (time.time(), content)
				time.sleep(2			)
				yield (time.time(), content)






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






				"""simple docstring"""


				with Pool(2			) as pool:
								snake_case_						:   int           =  list(iflatmap_unordered(_UpperCamelCase			,    _split_text			,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
								assert out.count('''hello'''			) == 10
								assert out.count('''there'''			) == 10
								assert len(_UpperCamelCase			) == 20

				# check multiprocess from pathos (uses dill for pickling)
				with multiprocess.Pool(2			) as pool:
								snake_case_						:   Any           =  list(iflatmap_unordered(_UpperCamelCase			,    _split_text			,    kwargs_iterable=[{'''text''': '''hello there'''}] * 10			)			)
								assert out.count('''hello'''			) == 10
								assert out.count('''there'''			) == 10
								assert len(_UpperCamelCase			) == 20

				# check that we get items as fast as possible
				with Pool(2			) as pool:
								snake_case_						:   Union[str, Any]           =  []
								for yield_time, content in iflatmap_unordered(
								    _UpperCamelCase			,    _aseconds_generator_of_aitems_with_timing			,    kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]			):
												assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
												out.append(_UpperCamelCase			)
								assert out.count('''a'''			) == 2
								assert out.count('''b'''			) == 2
								assert len(_UpperCamelCase			) == 4







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

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



@flax.struct.dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					CommonSchedulerState

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






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							return cls(common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__   )




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



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

							# standard deviation of the initial noise distribution
							snake_case_						:   Any           =  jnp.array(1.0 ,							dtype=self.dtype   )

							snake_case_						:   Optional[int]           =  jnp.arange(0 ,							self.config.num_train_timesteps   ).round()[::-1]

							return DDPMSchedulerState.create(
							    common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__ ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							snake_case_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=__magic_name__ ,							timesteps=__magic_name__ ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  state.common.alphas_cumprod[t]
							snake_case_						:   Dict           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							snake_case_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											snake_case_						:   Any           =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											snake_case_						:   int           =  jnp.clip(__magic_name__ ,							a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											snake_case_						:   List[str]           =  jnp.log(jnp.clip(__magic_name__ ,							a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											snake_case_						:   str           =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											snake_case_						:   int           =  jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											snake_case_						:   str           =  variance
											snake_case_						:   Optional[Any]           =  state.common.betas[t]
											snake_case_						:   Optional[Any]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[Any]           =  frac * max_log + (1 - frac) * min_log

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = True ,							)					->       Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[Any]           =  jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											snake_case_      ,							snake_case_						:   Any           =  jnp.split(__magic_name__ ,							sample.shape[1] ,							axis=1   )
							else:
											snake_case_						:   Optional[Any]           =  None

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   str           =  1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											snake_case_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[Any]           =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
											    ''' for the FlaxDDPMScheduler.'''   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											snake_case_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-1 ,							1   )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											snake_case_						:   List[Any]           =  jax.random.split(__magic_name__ ,							num=1   )
											snake_case_						:   Tuple           =  jax.random.normal(__magic_name__ ,							shape=model_output.shape ,							dtype=self.dtype   )
											return (self._get_variance(__magic_name__ ,							__magic_name__ ,							predicted_variance=__magic_name__   ) ** 0.5) * noise

							snake_case_						:   List[str]           =  jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype   )   )

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return add_noise_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return get_velocity_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
