
"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE      =     {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[Any]   =			'''beit'''






       def __init__(  self :							Union[str, Any]       ,      UpperCAmelCase_ :							str=8_192       ,      UpperCAmelCase_ :							Any=768       ,      UpperCAmelCase_ :							Union[str, Any]=12       ,      UpperCAmelCase_ :							List[str]=12       ,      UpperCAmelCase_ :							str=3_072       ,      UpperCAmelCase_ :							List[str]="gelu"       ,      UpperCAmelCase_ :							List[Any]=0.0       ,      UpperCAmelCase_ :							Union[str, Any]=0.0       ,      UpperCAmelCase_ :							str=0.02       ,      UpperCAmelCase_ :							Tuple=1e-12       ,      UpperCAmelCase_ :							int=224       ,      UpperCAmelCase_ :							str=16       ,      UpperCAmelCase_ :							List[str]=3       ,      UpperCAmelCase_ :							Optional[int]=False       ,      UpperCAmelCase_ :							List[Any]=False       ,      UpperCAmelCase_ :							Any=False       ,      UpperCAmelCase_ :							List[Any]=False       ,      UpperCAmelCase_ :							str=0.1       ,      UpperCAmelCase_ :							Any=0.1       ,      UpperCAmelCase_ :							List[str]=True       ,      UpperCAmelCase_ :							str=[3, 5, 7, 11]       ,      UpperCAmelCase_ :							Optional[int]=[1, 2, 3, 6]       ,      UpperCAmelCase_ :							List[str]=True       ,      UpperCAmelCase_ :							Any=0.4       ,      UpperCAmelCase_ :							Optional[int]=256       ,      UpperCAmelCase_ :							List[str]=1       ,      UpperCAmelCase_ :							Tuple=False       ,      UpperCAmelCase_ :							List[str]=255       ,      **UpperCAmelCase_ :							str       ,      )->  str:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)

              UpperCamelCase					 =		vocab_size
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		num_hidden_layers
              UpperCamelCase					 =		num_attention_heads
              UpperCamelCase					 =		intermediate_size
              UpperCamelCase					 =		hidden_act
              UpperCamelCase					 =		hidden_dropout_prob
              UpperCamelCase					 =		attention_probs_dropout_prob
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		layer_norm_eps

              UpperCamelCase					 =		image_size
              UpperCamelCase					 =		patch_size
              UpperCamelCase					 =		num_channels
              UpperCamelCase					 =		use_mask_token
              UpperCamelCase					 =		use_absolute_position_embeddings
              UpperCamelCase					 =		use_relative_position_bias
              UpperCamelCase					 =		use_shared_relative_position_bias
              UpperCamelCase					 =		layer_scale_init_value
              UpperCamelCase					 =		drop_path_rate
              UpperCamelCase					 =		use_mean_pooling
              # decode head attributes (semantic segmentation)
              UpperCamelCase					 =		out_indices
              UpperCamelCase					 =		pool_scales
              # auxiliary head attributes (semantic segmentation)
              UpperCamelCase					 =		use_auxiliary_head
              UpperCamelCase					 =		auxiliary_loss_weight
              UpperCamelCase					 =		auxiliary_channels
              UpperCamelCase					 =		auxiliary_num_convs
              UpperCamelCase					 =		auxiliary_concat_input
              UpperCamelCase					 =		semantic_loss_ignore_index





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[str, Any]   =			version.parse('''1.11'''	)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              return OrderedDict(
                  [
                      ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ]			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  float:







              """simple docstring"""
              return 1e-4







"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[List[PIL.Image.Image], np.ndarray]
       UpperCamelCase_		:					Optional[List[bool]]


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipeline_cycle_diffusion import CycleDiffusionPipeline
      from .pipeline_stable_diffusion import StableDiffusionPipeline
      from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
      from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
      from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
      from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
      from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
      from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
      from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
      from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
      from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
      from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
      from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
      from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
      from .pipeline_stable_unclip import StableUnCLIPPipeline
      from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
      from .safety_checker import StableDiffusionSafetyChecker
      from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
      from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
      if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          StableDiffusionDepthaImgPipeline,
          StableDiffusionDiffEditPipeline,
          StableDiffusionPixaPixZeroPipeline,
      )
else:
      from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
      from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
      from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
      if not (
          is_torch_available()
          and is_transformers_available()
          and is_k_diffusion_available()
          and is_k_diffusion_version(""">=""", """0.0.12""")
      ):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
      from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
      if not (is_transformers_available() and is_onnx_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_onnx_objects import *  # noqa F403
else:
      from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
      from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
      from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
      from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
      from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
      import flax



      @flax.struct.dataclass
      class   __a  (      _lowerCAmelCase	):
             UpperCamelCase_		:					np.ndarray
             UpperCamelCase_		:					List[bool]





      from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
      from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
      from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
      from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
      from .safety_checker_flax import FlaxStableDiffusionSafetyChecker





