'''simple docstring'''

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


A_        =   logging.get_logger(__name__)  # pylint: disable=invalid-name




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



   """simple docstring"""






   __magic_name__   :							Optional[Any]             =					["""safety_checker""", """feature_extractor"""]



   def __init__(       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_ = True							, ):
         super().__init__()
         if hasattr(scheduler.config							, "steps_offset"				) and scheduler.config.steps_offset != 1:
               snake_case   :					Optional[Any]							=						(
                   F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
                   F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
                   "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                   " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                   " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                   " file"
               )
               deprecate("steps_offset!=1"							, "1.0.0"							, snake_case_							, standard_warn=snake_case_				)
               snake_case   :					str							=						dict(scheduler.config				)
               snake_case   :					List[Any]							=						1
               snake_case   :					Tuple							=						FrozenDict(snake_case_				)

         self.register_modules(
             vae=snake_case_							, text_encoder=snake_case_							, tokenizer=snake_case_							, unet=snake_case_							, scheduler=snake_case_							, safety_checker=snake_case_							, feature_extractor=snake_case_							, )
         self.register_to_config(requires_safety_checker=snake_case_				)



   @property
   def      lowerCamelCase   (       self				):
         return {k: getattr(self							, snake_case_				) for k in self.config.keys() if not k.startswith("_"				)}



   def      lowerCamelCase   (       self							, snake_case_ = "auto"				):
         if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               snake_case   :					Any							=						self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(snake_case_				)



   def      lowerCamelCase   (       self				):
         self.enable_attention_slicing(snake_case_				)



   @torch.no_grad()
   def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_ = 0.8							, snake_case_ = 50							, snake_case_ = 7.5							, snake_case_ = None							, snake_case_ = 1							, snake_case_ = 0.0							, snake_case_ = None							, snake_case_ = "pil"							, snake_case_ = True							, snake_case_ = None							, snake_case_ = 1							, ):
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionInpaintPipelineLegacy(**self.components				)(
             prompt=snake_case_							, image=snake_case_							, mask_image=snake_case_							, strength=snake_case_							, num_inference_steps=snake_case_							, guidance_scale=snake_case_							, negative_prompt=snake_case_							, num_images_per_prompt=snake_case_							, eta=snake_case_							, generator=snake_case_							, output_type=snake_case_							, return_dict=snake_case_							, callback=snake_case_							, )



   @torch.no_grad()
   def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_ = 0.8							, snake_case_ = 50							, snake_case_ = 7.5							, snake_case_ = None							, snake_case_ = 1							, snake_case_ = 0.0							, snake_case_ = None							, snake_case_ = "pil"							, snake_case_ = True							, snake_case_ = None							, snake_case_ = 1							, **snake_case_							, ):
         # For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
         return StableDiffusionImgaImgPipeline(**self.components				)(
             prompt=snake_case_							, image=snake_case_							, strength=snake_case_							, num_inference_steps=snake_case_							, guidance_scale=snake_case_							, negative_prompt=snake_case_							, num_images_per_prompt=snake_case_							, eta=snake_case_							, generator=snake_case_							, output_type=snake_case_							, return_dict=snake_case_							, callback=snake_case_							, callback_steps=snake_case_							, )






   @torch.no_grad()
   def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = 512							, snake_case_ = 512							, snake_case_ = 50							, snake_case_ = 7.5							, snake_case_ = None							, snake_case_ = 1							, snake_case_ = 0.0							, snake_case_ = None							, snake_case_ = None							, snake_case_ = "pil"							, snake_case_ = True							, snake_case_ = None							, snake_case_ = 1							, ):
         # For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
         return StableDiffusionPipeline(**self.components				)(
             prompt=snake_case_							, height=snake_case_							, width=snake_case_							, num_inference_steps=snake_case_							, guidance_scale=snake_case_							, negative_prompt=snake_case_							, num_images_per_prompt=snake_case_							, eta=snake_case_							, generator=snake_case_							, latents=snake_case_							, output_type=snake_case_							, return_dict=snake_case_							, callback=snake_case_							, callback_steps=snake_case_							, )






'''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


A_        =   logging.get_logger(__name__)

A_        =   {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					"""mobilevit"""



			def __init__(       self							, snake_case_=3							, snake_case_=256							, snake_case_=2							, snake_case_=[144, 192, 240]							, snake_case_=[16, 32, 64, 96, 128, 160, 640]							, snake_case_=4							, snake_case_=2.0							, snake_case_=4.0							, snake_case_="silu"							, snake_case_=3							, snake_case_=32							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.1							, snake_case_=0.02							, snake_case_=1e-5							, snake_case_=True							, snake_case_=256							, snake_case_=[6, 12, 18]							, snake_case_=0.1							, snake_case_=255							, **snake_case_							, ):
									super().__init__(**snake_case_				)

									snake_case   :					Any							=						num_channels
									snake_case   :					Optional[Any]							=						image_size
									snake_case   :					Union[str, Any]							=						patch_size
									snake_case   :					Union[str, Any]							=						hidden_sizes
									snake_case   :					List[Any]							=						neck_hidden_sizes
									snake_case   :					Optional[Any]							=						num_attention_heads
									snake_case   :					Any							=						mlp_ratio
									snake_case   :					List[str]							=						expand_ratio
									snake_case   :					Tuple							=						hidden_act
									snake_case   :					Tuple							=						conv_kernel_size
									snake_case   :					int							=						output_stride
									snake_case   :					Dict							=						hidden_dropout_prob
									snake_case   :					List[str]							=						attention_probs_dropout_prob
									snake_case   :					int							=						classifier_dropout_prob
									snake_case   :					str							=						initializer_range
									snake_case   :					Optional[int]							=						layer_norm_eps
									snake_case   :					Dict							=						qkv_bias

									# decode head attributes for semantic segmentation
									snake_case   :					Optional[int]							=						aspp_out_channels
									snake_case   :					Dict							=						atrous_rates
									snake_case   :					Any							=						aspp_dropout_prob
									snake_case   :					Any							=						semantic_loss_ignore_index







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					version.parse("""1.11"""			)



			@property
			def      lowerCamelCase   (       self				):
									return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})]				)



			@property
			def      lowerCamelCase   (       self				):
									if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]				)
									else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]				)






			@property
			def      lowerCamelCase   (       self				):
									return 1e-4





