



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 					_snake_case      :
				# setable values
				UpperCamelCase__					:       List[str]  		=None
				UpperCamelCase__					:       Optional[int]  		=None
				UpperCamelCase__					:       List[Any]  		=None  # sigma(t_i)




				@classmethod
				def 				A__   (    cls       :    Dict    ):
							return cls()
@dataclass
class 					_snake_case      (       lowercase__):
				UpperCamelCase__					:       Optional[int]  		=4_2
				UpperCamelCase__					:       Optional[Any]  		=4_2
				UpperCamelCase__					:       Union[str, Any]  		=4_2
class 					_snake_case      (       lowercase__     ,						lowercase__):




				@property
				def 				A__   (    self       :    Optional[int]    ):
							return True




				@register_to_config
				def __init__(    self       :    Union[str, Any],       __lowercase       :    Optional[int] = 0.02,       __lowercase       :    List[Any] = 100,       __lowercase       :    Dict = 1.007,       __lowercase       :    Union[str, Any] = 80,       __lowercase       :    Dict = 0.05,       __lowercase       :    List[str] = 50,       ):
							pass




				def 				A__   (    self       :    Any    ):
							return KarrasVeSchedulerState.create()




				def 				A__   (    self       :    Optional[int],       __lowercase       :    List[str],       __lowercase       :    int,       __lowercase       :    List[str] = ()    ):
							lowercase__       	=					jnp.arange(0,       a_    )[::-1].copy()
							lowercase__       	=					[
							    (
							        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 				A__   (    self       :    Dict,       __lowercase       :    str,       __lowercase       :    Dict,       __lowercase       :    Any,       __lowercase       :    Union[str, Any],       ):
							if self.config.s_min <= sigma <= self.config.s_max:
										lowercase__       	=					min(self.config.s_churn / state.num_inference_steps,       2**0.5 - 1    )
							else:
										lowercase__       	=					0

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

							return sample_hat, sigma_hat




				def 				A__   (    self       :    List[Any],       __lowercase       :    str,       __lowercase       :    List[str],       __lowercase       :    Dict,       __lowercase       :    List[Any],       __lowercase       :    List[Any],       __lowercase       :    int = True,       ):
							lowercase__       	=					sample_hat + sigma_hat * model_output
							lowercase__       	=					(sample_hat - pred_original_sample) / sigma_hat
							lowercase__       	=					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 				A__   (    self       :    List[Any],       __lowercase       :    Union[str, Any],       __lowercase       :    Optional[int],       __lowercase       :    List[str],       __lowercase       :    int,       __lowercase       :    Union[str, Any],       __lowercase       :    Tuple,       __lowercase       :    List[str],       __lowercase       :    Union[str, Any] = True,       ):
							lowercase__       	=					sample_prev + sigma_prev * model_output
							lowercase__       	=					(sample_prev - pred_original_sample) / sigma_prev
							lowercase__       	=					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 				A__   (    self       :    str,       __lowercase       :    Optional[int],       __lowercase       :    Tuple,       __lowercase       :    Optional[int],       __lowercase       :    List[str]    ):
							raise NotImplementedError()









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCAmelCase	:   Any    =   {
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   str    =   ["ChineseCLIPFeatureExtractor"]
	_lowerCAmelCase	:   Any    =   ["ChineseCLIPImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase	:   Optional[int]    =   [
	    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ChineseCLIPModel",
	    "ChineseCLIPPreTrainedModel",
	    "ChineseCLIPTextModel",
	    "ChineseCLIPVisionModel",
	]

if TYPE_CHECKING:
	from .configuration_chinese_clip import (
	    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ChineseCLIPConfig,
	    ChineseCLIPOnnxConfig,
	    ChineseCLIPTextConfig,
	    ChineseCLIPVisionConfig,
	)
	from .processing_chinese_clip import ChineseCLIPProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ChineseCLIPModel,
		    ChineseCLIPPreTrainedModel,
		    ChineseCLIPTextModel,
		    ChineseCLIPVisionModel,
		)

else:
	import sys

	_lowerCAmelCase	:   Any    =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
