
"""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 				__magic_name__  :
					_SCREAMING_SNAKE_CASE							:       Optional[int]  				= None
					_SCREAMING_SNAKE_CASE							:       Optional[jnp.ndarray]  				= None
					_SCREAMING_SNAKE_CASE							:       Optional[jnp.ndarray]  				= None  # sigma(t_i)





					@classmethod
					def 				lowerCAmelCase   (					cls :				List[Any]     ):
												return cls()







@dataclass
class 				__magic_name__  (		UpperCAmelCase_ ):
					_SCREAMING_SNAKE_CASE							:       jnp.ndarray
					_SCREAMING_SNAKE_CASE							:       jnp.ndarray
					_SCREAMING_SNAKE_CASE							:       KarrasVeSchedulerState







class 				__magic_name__  (		UpperCAmelCase_   ,		UpperCAmelCase_ ):





					@property
					def 				lowerCAmelCase   (					self :				int     ):
												return True





					@register_to_config
					def __init__(					self :				Optional[int]				,    snake_case_ :				float = 0.02				,    snake_case_ :				float = 100				,    snake_case_ :				float = 1.007				,    snake_case_ :				float = 80				,    snake_case_ :				float = 0.05				,    snake_case_ :				float = 50				,    ):
												pass





					def 				lowerCAmelCase   (					self :				Dict     ):
												return KarrasVeSchedulerState.create()





					def 				lowerCAmelCase   (					self :				List[str]				,    snake_case_ :				KarrasVeSchedulerState				,    snake_case_ :				int				,    snake_case_ :				Tuple = ()     ):
												__snake_case        =							jnp.arange(0				,    _snake_case     )[::-1].copy()
												__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 timesteps
												]

												return state.replace(
												    num_inference_steps=_snake_case				,    schedule=jnp.array(_snake_case				,    dtype=jnp.floataa     )				,    timesteps=_snake_case				,    )





					def 				lowerCAmelCase   (					self :				Tuple				,    snake_case_ :				KarrasVeSchedulerState				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				float				,    snake_case_ :				random.KeyArray				,    ):


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

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

												return sample_hat, sigma_hat





					def 				lowerCAmelCase   (					self :				Union[str, Any]				,    snake_case_ :				KarrasVeSchedulerState				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				float				,    snake_case_ :				float				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				bool = True				,    ):
												__snake_case        =							sample_hat + sigma_hat * model_output
												__snake_case        =							(sample_hat - pred_original_sample) / sigma_hat
												__snake_case        =							sample_hat + (sigma_prev - sigma_hat) * derivative

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

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





					def 				lowerCAmelCase   (					self :				int				,    snake_case_ :				KarrasVeSchedulerState				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				float				,    snake_case_ :				float				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				jnp.ndarray				,    snake_case_ :				bool = True				,    ):
												__snake_case        =							sample_prev + sigma_prev * model_output
												__snake_case        =							(sample_prev - pred_original_sample) / sigma_prev
												__snake_case        =							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=_snake_case				,    derivative=_snake_case				,    state=_snake_case     )





					def 				lowerCAmelCase   (					self :				List[Any]				,    snake_case_ :				KarrasVeSchedulerState				,    snake_case_ :				str				,    snake_case_ :				Optional[int]				,    snake_case_ :				str     ):
												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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   			=   {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__   			=   [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__   			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)




