
"""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 					lowerCAmelCase			(				snake_case_					):
     """simple docstring"""







     __lowercase						:Any          =    42
     __lowercase						:Any          =    42
     __lowercase						:List[str]          =    None





class 					lowerCAmelCase			(				snake_case_     ,						snake_case_					):
     """simple docstring"""







     __lowercase						:Tuple          =    2




     @register_to_config
     def __init__(      self ,		UpperCamelCase__ = 0.02 ,		UpperCamelCase__ = 100 ,		UpperCamelCase__ = 1.007 ,		UpperCamelCase__ = 80 ,		UpperCamelCase__ = 0.05 ,		UpperCamelCase__ = 50 ,		)					->  str:







            '''simple docstring'''





            lowerCamelCase_       							=		sigma_max

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




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ = None							)					->  torch.FloatTensor:







            '''simple docstring'''





            return sample




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ = None							)					->  Optional[int]:







            '''simple docstring'''





            lowerCamelCase_       							=		num_inference_steps
            lowerCamelCase_       							=		np.arange(0 ,		self.num_inference_steps							)[::-1].copy()
            lowerCamelCase_       							=		torch.from_numpy(UpperCamelCase__							).to(UpperCamelCase__							)
            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(UpperCamelCase__ ,		dtype=torch.floataa ,		device=UpperCamelCase__							)




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = None							)					->  Tuple[torch.FloatTensor, float]:







            '''simple docstring'''





            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=UpperCamelCase__							).to(sample.device							)
            lowerCamelCase_       							=		sigma + gamma * sigma
            lowerCamelCase_       							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

            return sample_hat, sigma_hat




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = True ,		)					->  Union[KarrasVeOutput, Tuple]:







            '''simple docstring'''





            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=UpperCamelCase__ ,		derivative=UpperCamelCase__ ,		pred_original_sample=UpperCamelCase__							)




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = True ,		)					->  Union[KarrasVeOutput, Tuple]:







            '''simple docstring'''





            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=UpperCamelCase__ ,		derivative=UpperCamelCase__ ,		pred_original_sample=UpperCamelCase__							)




     def    _lowerCAmelCase		(      self ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__							)					->  Union[str, Any]:







            '''simple docstring'''





            raise NotImplementedError()

from typing import TYPE_CHECKING

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


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

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


