


"""simple docstring"""


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    torch.FloatTensor
							__UpperCAmelCase						:    torch.FloatTensor
							__UpperCAmelCase						:    Optional[torch.FloatTensor]				      =None
class   _UpperCamelCase  (   lowerCAmelCase__       ,lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Tuple				      =2

							@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    ,   ):
										# standard deviation of the initial noise distribution
										__lowerCAmelCase				    =			sigma_max

										# setable values
										__lowerCAmelCase				    =			None
										__lowerCAmelCase				    =			None
										__lowerCAmelCase				    =			None  # sigma(t_i)

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										return sample

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			num_inference_steps
										__lowerCAmelCase				    =			np.arange(0    ,   self.num_inference_steps	)[::-1].copy()
										__lowerCAmelCase				    =			torch.from_numpy(__a	).to(__a	)
										__lowerCAmelCase				    =			[
										    (
										        self.config.sigma_max**2
										        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
										    )
										    for i in self.timesteps
										]
										__lowerCAmelCase				    =			torch.tensor(__a    ,   dtype=torch.floataa    ,   device=__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = None	):
										if self.config.s_min <= sigma <= self.config.s_max:
													__lowerCAmelCase				    =			min(self.config.s_churn / self.num_inference_steps    ,   2**0.5 - 1	)
										else:
													__lowerCAmelCase				    =			0

										# sample eps ~ N(0, S_noise^2 * I)
										__lowerCAmelCase				    =			self.config.s_noise * randn_tensor(sample.shape    ,   generator=__a	).to(sample.device	)
										__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 = 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)

										return KarrasVeOutput(
										    prev_sample=__a    ,   derivative=__a    ,   pred_original_sample=__a	)

							def 			snake_case				(     self    ,   __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)

										return KarrasVeOutput(
										    prev_sample=__a    ,   derivative=__a    ,   pred_original_sample=__a	)



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








"""simple docstring"""


from typing import TYPE_CHECKING

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


A     :   Optional[int]			    =			{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Tuple			    =			[
	    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "AltCLIPPreTrainedModel",
	    "AltCLIPModel",
	    "AltCLIPTextModel",
	    "AltCLIPVisionModel",
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	A     :   int			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




