



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 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					torch.FloatTensor
				UpperCamelCase__				:					torch.FloatTensor
				UpperCamelCase__				:					Optional[torch.FloatTensor]     		=None
class 				_snake_case			(					lowercase__    ,	lowercase__):
				UpperCamelCase__				:					Tuple     		=2




				@register_to_config
				def __init__(						self					:						List[str],					__lowercase					:						float = 0.02,					__lowercase					:						float = 100,					__lowercase					:						float = 1.007,					__lowercase					:						float = 80,					__lowercase					:						float = 0.05,					__lowercase					:						float = 50,					):
							# standard deviation of the initial noise distribution
							lowercase__					  =				sigma_max

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




				def 							A__      (						self					:						int,					__lowercase					:						torch.FloatTensor,					__lowercase					:						Optional[int] = None						):
							return sample




				def 							A__      (						self					:						Any,					__lowercase					:						int,					__lowercase					:						Union[str, torch.device] = None						):
							lowercase__					  =				num_inference_steps
							lowercase__					  =				np.arange(0,					self.num_inference_steps						)[::-1].copy()
							lowercase__					  =				torch.from_numpy(__lowercase						).to(__lowercase						)
							lowercase__					  =				[
							    (
							        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
							]
							lowercase__					  =				torch.tensor(__lowercase,					dtype=torch.floataa,					device=__lowercase						)




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						torch.FloatTensor,					__lowercase					:						float,					__lowercase					:						Optional[torch.Generator] = None						):
							if self.config.s_min <= sigma <= self.config.s_max:
										lowercase__					  =				min(self.config.s_churn / self.num_inference_steps,					2**0.5 - 1						)
							else:
										lowercase__					  =				0

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

							return sample_hat, sigma_hat




				def 							A__      (						self					:						Dict,					__lowercase					:						torch.FloatTensor,					__lowercase					:						float,					__lowercase					:						float,					__lowercase					:						torch.FloatTensor,					__lowercase					:						bool = 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)

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




				def 							A__      (						self					:						Tuple,					__lowercase					:						torch.FloatTensor,					__lowercase					:						float,					__lowercase					:						float,					__lowercase					:						torch.FloatTensor,					__lowercase					:						torch.FloatTensor,					__lowercase					:						torch.FloatTensor,					__lowercase					:						bool = 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)

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




				def 							A__      (						self					:						Dict,					__lowercase					:						Any,					__lowercase					:						List[str],					__lowercase					:						Union[str, Any]						):
							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


lowercase_								=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """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

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




