
# 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_torch_available, is_vision_available


_snake_case      = {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = ['''EfficientNetImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = [
     '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EfficientNetForImageClassification''',
     '''EfficientNetModel''',
     '''EfficientNetPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_efficientnet import (
     EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EfficientNetConfig,
     EfficientNetOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientnet import EfficientNetImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientnet import (
      EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientNetForImageClassification,
      EfficientNetModel,
      EfficientNetPreTrainedModel,
  )

else:
 import sys

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




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       _lowerCAmelCase  :

 """simple docstring"""
 # setable values
 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			:			Optional[Any]   ):






      """simple docstring"""



      return cls()




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					jnp.ndarray
 SCREAMING_SNAKE_CASE_				:					KarrasVeSchedulerState




class       _lowerCAmelCase  (		__magic_name__			,	__magic_name__							):

 """simple docstring"""

 @property
 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return True

 @register_to_config
 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			float = 0.02					, SCREAMING_SNAKE_CASE__			:			float = 1_00					, SCREAMING_SNAKE_CASE__			:			float = 1.007					, SCREAMING_SNAKE_CASE__			:			float = 80					, SCREAMING_SNAKE_CASE__			:			float = 0.05					, SCREAMING_SNAKE_CASE__			:			float = 50					, ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      return KarrasVeSchedulerState.create()

 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Tuple = ()   ):






      """simple docstring"""



      UpperCamelCase						    =					jnp.arange(0					, SCREAMING_SNAKE_CASE__   )[::-1].copy()
      UpperCamelCase						    =					[
          (
              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=SCREAMING_SNAKE_CASE__					, schedule=jnp.array(SCREAMING_SNAKE_CASE__					, dtype=jnp.floataa   )					, timesteps=SCREAMING_SNAKE_CASE__					, )

 def      __lowerCAmelCase  (						self			:			List[Any]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			random.KeyArray					, ):






      """simple docstring"""



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

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

      return sample_hat, sigma_hat

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_hat + sigma_hat * model_output
      UpperCamelCase						    =					(sample_hat - pred_original_sample) / sigma_hat
      UpperCamelCase						    =					sample_hat + (sigma_prev - sigma_hat) * derivative

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

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

 def      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			float					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			jnp.ndarray					, SCREAMING_SNAKE_CASE__			:			bool = True					, ):






      """simple docstring"""



      UpperCamelCase						    =					sample_prev + sigma_prev * model_output
      UpperCamelCase						    =					(sample_prev - pred_original_sample) / sigma_prev
      UpperCamelCase						    =					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=SCREAMING_SNAKE_CASE__					, derivative=SCREAMING_SNAKE_CASE__					, state=SCREAMING_SNAKE_CASE__   )






 def      __lowerCAmelCase  (						self			:			Any					, SCREAMING_SNAKE_CASE__			:			KarrasVeSchedulerState					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			str					, SCREAMING_SNAKE_CASE__			:			int   ):






      """simple docstring"""



      raise NotImplementedError()


