
"""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 				__a    :



							'''simple docstring'''







							_lowerCamelCase				: CommonSchedulerState

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

							# running values
							_lowerCamelCase				: Optional[jnp.ndarray]					      =      None
							@classmethod
							def      SCREAMING_SNAKE_CASE	(    cls     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Tuple:




										'''simple docstring'''
										return cls(common=_lowerCamelCase     ,						init_noise_sigma=_lowerCamelCase     ,						timesteps=_lowerCamelCase     ,						sigmas=_lowerCamelCase					)





@dataclass
class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: LMSDiscreteSchedulerState





class 				__a    (   __a				,      __a					):



							'''simple docstring'''







							_lowerCamelCase				: List[Any]					      =      [e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase				: jnp.dtype
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										return True
							@register_to_config
							def __init__(    self     ,						_lowerCamelCase = 1_000     ,						_lowerCamelCase = 0.0001     ,						_lowerCamelCase = 0.02     ,						_lowerCamelCase = "linear"     ,						_lowerCamelCase = None     ,						_lowerCamelCase = "epsilon"     ,						_lowerCamelCase = jnp.floataa     ,						) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					dtype
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase = None					) ->       LMSDiscreteSchedulerState:




										'''simple docstring'''
										if common is None:
													__lowercase						      =					CommonSchedulerState.create(self					)

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

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

										return LMSDiscreteSchedulerState.create(
										    common=_lowerCamelCase     ,						init_noise_sigma=_lowerCamelCase     ,						timesteps=_lowerCamelCase     ,						sigmas=_lowerCamelCase     ,						)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       jnp.ndarray:




										'''simple docstring'''
										((__lowercase				)      ,								)						      =					jnp.where(state.timesteps == timestep     ,						size=1					)
										__lowercase						      =					step_index[0]

										__lowercase						      =					state.sigmas[step_index]
										__lowercase						      =					sample / ((sigma**2 + 1) ** 0.5)
										return sample
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Dict:




										'''simple docstring'''

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

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

										return integrated_coeff
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = ()					) ->       LMSDiscreteSchedulerState:




										'''simple docstring'''
										__lowercase						      =					jnp.linspace(self.config.num_train_timesteps - 1     ,						0     ,						_lowerCamelCase     ,						dtype=self.dtype					)

										__lowercase						      =					jnp.floor(_lowerCamelCase					).astype(jnp.intaa					)
										__lowercase						      =					jnp.ceil(_lowerCamelCase					).astype(jnp.intaa					)

										__lowercase						      =					jnp.mod(_lowerCamelCase     ,						1.0					)

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

										__lowercase						      =					timesteps.astype(jnp.intaa					)

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

										return state.replace(
										    timesteps=_lowerCamelCase     ,						sigmas=_lowerCamelCase     ,						num_inference_steps=_lowerCamelCase     ,						derivatives=_lowerCamelCase     ,						)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = 4     ,						_lowerCamelCase = 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"					)

										__lowercase						      =					state.sigmas[timestep]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
													__lowercase						      =					sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowercase						      =					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
										__lowercase						      =					(sample - pred_original_sample) / sigma
										__lowercase						      =					state.replace(derivatives=jnp.append(state.derivatives     ,						_lowerCamelCase					)					)
										if len(state.derivatives					) > order:
													__lowercase						      =					state.replace(derivatives=jnp.delete(state.derivatives     ,						0					)					)

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

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

										if not return_dict:
													return (prev_sample, state)

										return FlaxLMSSchedulerOutput(prev_sample=_lowerCamelCase     ,						state=_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       jnp.ndarray:




										'''simple docstring'''
										__lowercase						      =					state.sigmas[timesteps].flatten()
										__lowercase						      =					broadcast_to_shape_from_left(_lowerCamelCase     ,						noise.shape					)

										__lowercase						      =					original_samples + noise * sigma

										return noisy_samples





							def __len__(    self					) ->       Tuple:




										'''simple docstring'''
										return self.config.num_train_timesteps







"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowercase							 =							logging.get_logger(__name__)

_lowercase							 =							{
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}

class 				__a    (   __a				,      __a					):



							'''simple docstring'''







							_lowerCamelCase				: List[Any]					      =      """convnext"""
							def __init__(    self     ,						_lowerCamelCase=3     ,						_lowerCamelCase=4     ,						_lowerCamelCase=4     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						_lowerCamelCase="gelu"     ,						_lowerCamelCase=0.02     ,						_lowerCamelCase=1e-12     ,						_lowerCamelCase=1e-6     ,						_lowerCamelCase=0.0     ,						_lowerCamelCase=224     ,						_lowerCamelCase=None     ,						_lowerCamelCase=None     ,						**_lowerCamelCase     ,						) ->       List[str]:




										'''simple docstring'''
										super().__init__(**_lowerCamelCase					)

										__lowercase						      =					num_channels
										__lowercase						      =					patch_size
										__lowercase						      =					num_stages
										__lowercase						      =					[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
										__lowercase						      =					[3, 3, 9, 3] if depths is None else depths
										__lowercase						      =					hidden_act
										__lowercase						      =					initializer_range
										__lowercase						      =					layer_norm_eps
										__lowercase						      =					layer_scale_init_value
										__lowercase						      =					drop_path_rate
										__lowercase						      =					image_size
										__lowercase						      =					["stem"] + [f'''stage{idx}''' for idx in range(1     ,						len(self.depths					) + 1					)]
										__lowercase      ,				__lowercase						      =					get_aligned_output_features_output_indices(
										    out_features=_lowerCamelCase     ,						out_indices=_lowerCamelCase     ,						stage_names=self.stage_names					)





class 				__a    (   __a					):



							'''simple docstring'''







							_lowerCamelCase				: str					      =      version.parse("""1.11"""					)
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										return OrderedDict(
										    [
										        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
										    ]					)





							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       float:




										'''simple docstring'''
										return 1e-5





