






"""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_ :
	# setable values
	__magic_name__			   =       None
	__magic_name__			   =       None
	__magic_name__			   =       None  # sigma(t_i)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Any		)		->    Tuple:
				return cls()







@dataclass
class     UpperCamelCase_ (lowerCAmelCase__  ):
	__magic_name__			   =       42
	__magic_name__			   =       42
	__magic_name__			   =       42







class     UpperCamelCase_ (lowerCAmelCase__       ,		lowerCAmelCase__  ):





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				return True





	@register_to_config
	def __init__(   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Dict = 0.0_2			,			lowerCAmelCase_      :				Dict = 100			,			lowerCAmelCase_      :				Dict = 1.0_0_7			,			lowerCAmelCase_      :				List[Any] = 80			,			lowerCAmelCase_      :				Optional[int] = 0.0_5			,			lowerCAmelCase_      :				Tuple = 50			,			)		->    int:
				pass





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				return KarrasVeSchedulerState.create()





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				int = ()		)		->    KarrasVeSchedulerState:
				UpperCAmelCase_  :				Any      =      jnp.arange(0			,			_SCREAMING_SNAKE_CASE		)[::-1].copy()
				UpperCAmelCase_  :				Any      =      [
				    (
				        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=_SCREAMING_SNAKE_CASE			,			schedule=jnp.array(_SCREAMING_SNAKE_CASE			,			dtype=jnp.floataa		)			,			timesteps=_SCREAMING_SNAKE_CASE			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				str			,			lowerCAmelCase_      :				Union[str, Any]			,			)		->    Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_  :				List[Any]      =      min(self.config.s_churn / state.num_inference_steps			,			2**0.5 - 1		)
				else:
							UpperCAmelCase_  :				Optional[Any]      =      0

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

				return sample_hat, sigma_hat





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				Union[str, Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				int = True			,			)		->    Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_  :				Optional[Any]      =      sample_hat + sigma_hat * model_output
				UpperCAmelCase_  :				Tuple      =      (sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_  :				int      =      sample_hat + (sigma_prev - sigma_hat) * derivative

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

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Dict			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				List[str] = True			,			)		->    Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_  :				Dict      =      sample_prev + sigma_prev * model_output
				UpperCAmelCase_  :				str      =      (sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_  :				List[str]      =      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=_SCREAMING_SNAKE_CASE			,			derivative=_SCREAMING_SNAKE_CASE			,			state=_SCREAMING_SNAKE_CASE		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Dict		)		->    Optional[Any]:
				raise NotImplementedError()











"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       			=   {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''CLIPTokenizerFast''']

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''CLIPFeatureExtractor''']
			lowerCamelCase_       			=   ['''CLIPImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CLIPModel''',
			    '''CLIPPreTrainedModel''',
			    '''CLIPTextModel''',
			    '''CLIPTextModelWithProjection''',
			    '''CLIPVisionModel''',
			    '''CLIPVisionModelWithProjection''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFCLIPModel''',
			    '''TFCLIPPreTrainedModel''',
			    '''TFCLIPTextModel''',
			    '''TFCLIPVisionModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''FlaxCLIPModel''',
			    '''FlaxCLIPPreTrainedModel''',
			    '''FlaxCLIPTextModel''',
			    '''FlaxCLIPTextPreTrainedModel''',
			    '''FlaxCLIPVisionModel''',
			    '''FlaxCLIPVisionPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_clip import (
			    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    CLIPConfig,
			    CLIPOnnxConfig,
			    CLIPTextConfig,
			    CLIPVisionConfig,
			)
			from .processing_clip import CLIPProcessor
			from .tokenization_clip import CLIPTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_clip_fast import CLIPTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_clip import CLIPFeatureExtractor
						from .image_processing_clip import CLIPImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_clip import (
						    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CLIPModel,
						    CLIPPreTrainedModel,
						    CLIPTextModel,
						    CLIPTextModelWithProjection,
						    CLIPVisionModel,
						    CLIPVisionModelWithProjection,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_clip import (
						    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCLIPModel,
						    TFCLIPPreTrainedModel,
						    TFCLIPTextModel,
						    TFCLIPVisionModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_clip import (
						    FlaxCLIPModel,
						    FlaxCLIPPreTrainedModel,
						    FlaxCLIPTextModel,
						    FlaxCLIPTextPreTrainedModel,
						    FlaxCLIPVisionModel,
						    FlaxCLIPVisionPreTrainedModel,
						)


else:
			import sys

			lowerCamelCase_       			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



