





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_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42
							lowerCamelCase_           =					None




class   UpperCamelCase_       (     UpperCamelCase__				,  UpperCamelCase__    ):
							lowerCamelCase_           =					2


							@register_to_config
							def __init__(							self    :Any							,				__A    :float = 0.0_2							,				__A    :float = 100							,				__A    :float = 1.0_0_7							,				__A    :float = 80							,				__A    :float = 0.0_5							,				__A    :float = 50							,				)   ->   Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      sigma_max

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


							def      _snake_case  (							self    :Optional[int]							,				__A    :torch.FloatTensor							,				__A    :Optional[int] = None		)   ->   torch.FloatTensor:



									"""simple docstring"""



									return sample


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :int							,				__A    :Union[str, torch.device] = None		)   ->   Any:



									"""simple docstring"""



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


							def      _snake_case  (							self    :List[Any]							,				__A    :torch.FloatTensor							,				__A    :float							,				__A    :Optional[torch.Generator] = None		)   ->   Tuple[torch.FloatTensor, float]:



									"""simple docstring"""



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

									# sample eps ~ N(0, S_noise^2 * I)
									SCREAMING_SNAKE_CASE__			=      self.config.s_noise * randn_tensor(sample.shape							,				generator=__A		).to(sample.device		)
									SCREAMING_SNAKE_CASE__			=      sigma + gamma * sigma
									SCREAMING_SNAKE_CASE__			=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat


							def      _snake_case  (							self    :Optional[int]							,				__A    :torch.FloatTensor							,				__A    :float							,				__A    :float							,				__A    :torch.FloatTensor							,				__A    :bool = True							,				)   ->   Union[KarrasVeOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      sample_hat + sigma_hat * model_output
									SCREAMING_SNAKE_CASE__			=      (sample_hat - pred_original_sample) / sigma_hat
									SCREAMING_SNAKE_CASE__			=      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    :List[Any]							,				__A    :torch.FloatTensor							,				__A    :float							,				__A    :float							,				__A    :torch.FloatTensor							,				__A    :torch.FloatTensor							,				__A    :torch.FloatTensor							,				__A    :bool = True							,				)   ->   Union[KarrasVeOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE__			=      (sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE__			=      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    :str							,				__A    :str							,				__A    :Dict							,				__A    :Tuple		)   ->   Any:



									"""simple docstring"""



									raise NotImplementedError()









# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XmodForCausalLM',
	    'XmodForMaskedLM',
	    'XmodForMultipleChoice',
	    'XmodForQuestionAnswering',
	    'XmodForSequenceClassification',
	    'XmodForTokenClassification',
	    'XmodModel',
	    'XmodPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

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