


"""simple docstring"""


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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class 						_UpperCamelCase						:
							'''simple docstring'''







							__UpperCAmelCase		:					Optional[int]          =None
							__UpperCAmelCase		:					Optional[jnp.ndarray]          =None
							__UpperCAmelCase		:					Optional[jnp.ndarray]          =None  # sigma(t_i)

							@classmethod
							def     snake_case       (  cls			):
										return cls()
@dataclass
class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					jnp.ndarray
							__UpperCAmelCase		:					jnp.ndarray
							__UpperCAmelCase		:					KarrasVeSchedulerState
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							@property
							def     snake_case       (  self			):
										return True

							@register_to_config
							def __init__(  self					,     __a = 0.0_2					,     __a = 1_00					,     __a = 1.0_0_7					,     __a = 80					,     __a = 0.0_5					,     __a = 50					,     ):
										pass

							def     snake_case       (  self			):
										return KarrasVeSchedulerState.create()

							def     snake_case       (  self					,     __a					,     __a					,     __a = ()			):
										__lowerCAmelCase       					=    jnp.arange(0					,     __a			)[::-1].copy()
										__lowerCAmelCase       					=    [
										    (
										        self.config.sigma_max**2
										        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
										    )
										    for i in timesteps
										]

										return state.replace(
										    num_inference_steps=__a					,     schedule=jnp.array(__a					,     dtype=jnp.floataa			)					,     timesteps=__a					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     ):
										if self.config.s_min <= sigma <= self.config.s_max:
													__lowerCAmelCase       					=    min(self.config.s_churn / state.num_inference_steps					,     2**0.5 - 1			)
										else:
													__lowerCAmelCase       					=    0

										# sample eps ~ N(0, S_noise^2 * I)
										__lowerCAmelCase       					=    random.split(__a					,     num=1			)
										__lowerCAmelCase       					=    self.config.s_noise * random.normal(key=__a					,     shape=sample.shape			)
										__lowerCAmelCase       					=    sigma + gamma * sigma
										__lowerCAmelCase       					=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

										return sample_hat, sigma_hat

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a = True					,     ):
										__lowerCAmelCase       					=    sample_hat + sigma_hat * model_output
										__lowerCAmelCase       					=    (sample_hat - pred_original_sample) / sigma_hat
										__lowerCAmelCase       					=    sample_hat + (sigma_prev - sigma_hat) * derivative

										if not return_dict:
													return (sample_prev, derivative, state)

										return FlaxKarrasVeOutput(prev_sample=__a					,     derivative=__a					,     state=__a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a = True					,     ):
										__lowerCAmelCase       					=    sample_prev + sigma_prev * model_output
										__lowerCAmelCase       					=    (sample_prev - pred_original_sample) / sigma_prev
										__lowerCAmelCase       					=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

										if not return_dict:
													return (sample_prev, derivative, state)

										return FlaxKarrasVeOutput(prev_sample=__a					,     derivative=__a					,     state=__a			)



							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										raise NotImplementedError()








"""simple docstring"""


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


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




