






'''simple docstring'''





import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class 					lowercase							(				unittest.TestCase   ):


    '''simple docstring'''

    def  a__					(       self  :  Tuple  )				->				int:

     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    @property
    def  a__					(       self  :  List[str]  )				->				Optional[int]:

     '''simple docstring'''




     lowerCamelCase__	=  1
     lowerCamelCase__	=  3
     lowerCamelCase__	=  (32, 32)

     lowerCamelCase__	=  floats_tensor((batch_size, num_channels) + sizes					,  rng=random.Random(0  )  ).to(__lowerCamelCase  )
     return image

    @property
    def  a__					(       self  :  Any  )				->				str:

     '''simple docstring'''




     torch.manual_seed(0  )
     lowerCamelCase__	=  UNetaDConditionModel(
         block_out_channels=(32, 64)					,  layers_per_block=2					,  sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,  up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,  cross_attention_dim=32					,  )
     return model

    @property
    def  a__					(       self  :  Dict  )				->				Tuple:

     '''simple docstring'''




     torch.manual_seed(0  )
     lowerCamelCase__	=  AutoencoderKL(
         block_out_channels=[32, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,  up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,  latent_channels=4					,  )
     return model

    @property
    def  a__					(       self  :  int  )				->				List[str]:

     '''simple docstring'''




     torch.manual_seed(0  )
     lowerCamelCase__	=  CLIPTextConfig(
         bos_token_id=0					,  eos_token_id=2					,  hidden_size=32					,  intermediate_size=37					,  layer_norm_eps=1E-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  )
     return CLIPTextModel(__lowerCamelCase  )

    @property
    def  a__					(       self  :  Tuple  )				->				str:

     '''simple docstring'''




     def extract(*__lowerCamelCase  :  Union[str, Any]					,  **__lowerCamelCase  :  List[Any]  ):
      class 					lowercase							:


          '''simple docstring'''

          def __init__(       self  :  Optional[int]  )				->				int:

           '''simple docstring'''




           lowerCamelCase__	=  torch.ones([0]  )




          def  a__					(       self  :  List[str]					,  __lowerCamelCase  :  str  )				->				str:

           '''simple docstring'''




           self.pixel_values.to(__lowerCamelCase  )
           return self
      return Out()

     return extract

    def  a__					(       self  :  List[Any]  )				->				str:

     '''simple docstring'''




     lowerCamelCase__	=  "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowerCamelCase__	=  self.dummy_cond_unet
     lowerCamelCase__	=  DDIMScheduler(
         beta_start=0.0_0_0_8_5					,  beta_end=0.0_1_2					,  beta_schedule="scaled_linear"					,  clip_sample=__lowerCamelCase					,  set_alpha_to_one=__lowerCamelCase					,  )

     lowerCamelCase__	=  self.dummy_vae
     lowerCamelCase__	=  self.dummy_text_encoder
     lowerCamelCase__	=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

     # make sure here that pndm scheduler skips prk
     lowerCamelCase__	=  StableDiffusionPipeline(
         unet=__lowerCamelCase					,  scheduler=__lowerCamelCase					,  vae=__lowerCamelCase					,  text_encoder=__lowerCamelCase					,  tokenizer=__lowerCamelCase					,  safety_checker=__lowerCamelCase					,  feature_extractor=self.dummy_extractor					,  )
     lowerCamelCase__	=  sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "A painting of a squirrel eating a burger"

     lowerCamelCase__	=  torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     lowerCamelCase__	=  sd_pipe([prompt]					,  generator=__lowerCamelCase					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type="np"  )
     lowerCamelCase__	=  output.images

     lowerCamelCase__	=  torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     lowerCamelCase__	=  sd_pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type="np"					,  return_dict=__lowerCamelCase					,  )[0]

     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     lowerCamelCase__	=  np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]  )

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2

    def  a__					(       self  :  Any  )				->				int:

     '''simple docstring'''




     lowerCamelCase__	=  "cpu"  # ensure determinism for the device-dependent torch.Generator
     lowerCamelCase__	=  self.dummy_cond_unet
     lowerCamelCase__	=  PNDMScheduler(skip_prk_steps=__lowerCamelCase  )
     lowerCamelCase__	=  self.dummy_vae
     lowerCamelCase__	=  self.dummy_text_encoder
     lowerCamelCase__	=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

     # make sure here that pndm scheduler skips prk
     lowerCamelCase__	=  StableDiffusionPipeline(
         unet=__lowerCamelCase					,  scheduler=__lowerCamelCase					,  vae=__lowerCamelCase					,  text_encoder=__lowerCamelCase					,  tokenizer=__lowerCamelCase					,  safety_checker=__lowerCamelCase					,  feature_extractor=self.dummy_extractor					,  )
     lowerCamelCase__	=  sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "A painting of a squirrel eating a burger"
     lowerCamelCase__	=  torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     lowerCamelCase__	=  sd_pipe([prompt]					,  generator=__lowerCamelCase					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type="np"  )

     lowerCamelCase__	=  output.images

     lowerCamelCase__	=  torch.Generator(device=__lowerCamelCase  ).manual_seed(0  )
     lowerCamelCase__	=  sd_pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=6.0					,  num_inference_steps=2					,  output_type="np"					,  return_dict=__lowerCamelCase					,  )[0]

     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     lowerCamelCase__	=  np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]  )

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2

    def  a__					(       self  :  Any  )				->				List[str]:

     '''simple docstring'''




     lowerCamelCase__	=  StableDiffusionPipeline.from_pretrained(
         "hf-internal-testing/tiny-stable-diffusion-lms-pipe"					,  safety_checker=__lowerCamelCase  )
     assert isinstance(__lowerCamelCase					,  __lowerCamelCase  )
     assert isinstance(pipe.scheduler					,  __lowerCamelCase  )
     assert pipe.safety_checker is None

     lowerCamelCase__	=  pipe("example prompt"					,  num_inference_steps=2  ).images[0]
     assert image is not None

     # check that there's no error when saving a pipeline with one of the models being None
     with tempfile.TemporaryDirectory() as tmpdirname:
      pipe.save_pretrained(__lowerCamelCase  )
      lowerCamelCase__	=  StableDiffusionPipeline.from_pretrained(__lowerCamelCase  )

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     lowerCamelCase__	=  pipe("example prompt"					,  num_inference_steps=2  ).images[0]
     assert image is not None




    @unittest.skipIf(torch_device != "cuda"					,  "This test requires a GPU"  )
    def  a__					(       self  :  Tuple  )				->				List[Any]:

     '''simple docstring'''




     lowerCamelCase__	=  self.dummy_cond_unet
     lowerCamelCase__	=  PNDMScheduler(skip_prk_steps=__lowerCamelCase  )
     lowerCamelCase__	=  self.dummy_vae
     lowerCamelCase__	=  self.dummy_text_encoder
     lowerCamelCase__	=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )

     # put models in fp16
     lowerCamelCase__	=  unet.half()
     lowerCamelCase__	=  vae.half()
     lowerCamelCase__	=  bert.half()

     # make sure here that pndm scheduler skips prk
     lowerCamelCase__	=  StableDiffusionPipeline(
         unet=__lowerCamelCase					,  scheduler=__lowerCamelCase					,  vae=__lowerCamelCase					,  text_encoder=__lowerCamelCase					,  tokenizer=__lowerCamelCase					,  safety_checker=__lowerCamelCase					,  feature_extractor=self.dummy_extractor					,  )
     lowerCamelCase__	=  sd_pipe.to(__lowerCamelCase  )
     sd_pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "A painting of a squirrel eating a burger"
     lowerCamelCase__	=  sd_pipe([prompt]					,  num_inference_steps=2					,  output_type="np"  ).images

     assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class 					lowercase							(				unittest.TestCase   ):


    '''simple docstring'''

    def  a__					(       self  :  Dict  )				->				Any:

     '''simple docstring'''




     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

    def  a__					(       self  :  str  )				->				Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__	=  "cuda"
     lowerCamelCase__	=  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"  )
     lowerCamelCase__	=  pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "a photo of a cat"
     lowerCamelCase__	=  {
         "editing_prompt": ["sunglasses"],
         "reverse_editing_direction": [False],
         "edit_warmup_steps": 10,
         "edit_guidance_scale": 6,
         "edit_threshold": 0.9_5,
         "edit_momentum_scale": 0.5,
         "edit_mom_beta": 0.6,
     }

     lowerCamelCase__	=  3
     lowerCamelCase__	=  7

     # no sega enabled
     lowerCamelCase__	=  torch.Generator(__lowerCamelCase  )
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.3_4_6_7_3_1_1_3,
         0.3_8_4_9_2_7_3_3,
         0.3_7_5_9_7_3_5_2,
         0.3_4_0_8_6_3_3_5,
         0.3_5_6_5_0_7_4_8,
         0.3_5_5_7_9_2_0_5,
         0.3_3_8_4_7_6_3,
         0.3_4_3_4_0_2_3_6,
         0.3_5_7_3_2_7_1,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

     # with sega enabled
     # generator = torch.manual_seed(seed)
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  **__lowerCamelCase					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.4_1_8_8_7_8_2_6,
         0.3_7_7_2_8_7_6_6,
         0.3_0_1_3_8_2_7_2,
         0.4_1_4_1_6_3_3_5,
         0.4_1_6_6_4_9_8_5,
         0.3_6_2_8_3_3_9_2,
         0.3_6_1_9_1_2_4_6,
         0.4_3_3_6_4_4_6_5,
         0.4_3_0_0_1_7_3_2,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

    def  a__					(       self  :  Union[str, Any]  )				->				Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__	=  "cuda"
     lowerCamelCase__	=  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"  )
     lowerCamelCase__	=  pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "an image of a crowded boulevard, realistic, 4k"
     lowerCamelCase__	=  {
         "editing_prompt": "crowd, crowded, people",
         "reverse_editing_direction": True,
         "edit_warmup_steps": 10,
         "edit_guidance_scale": 8.3,
         "edit_threshold": 0.9,
         "edit_momentum_scale": 0.5,
         "edit_mom_beta": 0.6,
     }

     lowerCamelCase__	=  9
     lowerCamelCase__	=  7

     # no sega enabled
     lowerCamelCase__	=  torch.Generator(__lowerCamelCase  )
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.4_3_4_9_7_9_9_8,
         0.9_1_8_1_4_0_6_5,
         0.7_5_4_0_7_3_9,
         0.5_5_5_8_0_2_0_5,
         0.8_4_6_7_2_6_5,
         0.5_3_8_9_6_9_1,
         0.6_2_5_7_4_5_0_6,
         0.5_8_8_9_7_7_6_3,
         0.5_0_9_2_6_7_5_7,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

     # with sega enabled
     # generator = torch.manual_seed(seed)
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  **__lowerCamelCase					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.3_0_8_9_7_1_9,
         0.3_0_5_0_0_1_4_4,
         0.2_9_0_1_6_0_4_2,
         0.3_0_6_3_0_9_6_4,
         0.3_2_5_6_8_7,
         0.2_9_4_1_9_2_2_5,
         0.2_9_0_8_0_9_1,
         0.2_8_7_2_3_5_9_8,
         0.2_7_6_9_6_2_9_4,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

    def  a__					(       self  :  Union[str, Any]  )				->				List[str]:

     '''simple docstring'''




     lowerCamelCase__	=  "cuda"
     lowerCamelCase__	=  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"  )
     lowerCamelCase__	=  pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "a castle next to a river"
     lowerCamelCase__	=  {
         "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
         "reverse_editing_direction": False,
         "edit_warmup_steps": [15, 18],
         "edit_guidance_scale": 6,
         "edit_threshold": [0.9, 0.8],
         "edit_momentum_scale": 0.5,
         "edit_mom_beta": 0.6,
     }

     lowerCamelCase__	=  48
     lowerCamelCase__	=  7

     # no sega enabled
     lowerCamelCase__	=  torch.Generator(__lowerCamelCase  )
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.7_5_1_6_3_5_5_5,
         0.7_6_0_3_7_1_4_5,
         0.6_1_7_8_5,
         0.9_1_8_9_6_7_3,
         0.8_6_2_7_7_0_1,
         0.8_5_1_8_9_6_9_4,
         0.8_5_1_2_8_1_3,
         0.8_7_0_1_2_0_7_6,
         0.8_3_1_2_8_5_7,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

     # with sega enabled
     # generator = torch.manual_seed(seed)
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  **__lowerCamelCase					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.7_3_5_5_3_3_6_5,
         0.7_5_3_7_2_7_1,
         0.7_4_3_4_1_9_0_5,
         0.6_6_4_8_0_3_5_6,
         0.6_4_7_2_9_2_5,
         0.6_3_0_3_9_4_1_6,
         0.6_4_8_1_2_9_0_5,
         0.6_7_4_9_7_1_7,
         0.6_5_1_7_1_0_2,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2




    def  a__					(       self  :  Optional[int]  )				->				Tuple:

     '''simple docstring'''




     lowerCamelCase__	=  "cuda"
     lowerCamelCase__	=  StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"					,  torch_dtype=torch.floataa  )
     lowerCamelCase__	=  pipe.to(__lowerCamelCase  )
     pipe.set_progress_bar_config(disable=__lowerCamelCase  )

     lowerCamelCase__	=  "a photo of a cat"
     lowerCamelCase__	=  {
         "editing_prompt": ["sunglasses"],
         "reverse_editing_direction": [False],
         "edit_warmup_steps": 10,
         "edit_guidance_scale": 6,
         "edit_threshold": 0.9_5,
         "edit_momentum_scale": 0.5,
         "edit_mom_beta": 0.6,
     }

     lowerCamelCase__	=  3
     lowerCamelCase__	=  7

     # no sega enabled
     lowerCamelCase__	=  torch.Generator(__lowerCamelCase  )
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.3_4_8_8_7_6_9_5,
         0.3_8_7_6_9_5_3,
         0.3_7_5,
         0.3_4_4_2_3_8_2_8,
         0.3_5_8_1_5_4_3,
         0.3_5_7_1_7_7_7_3,
         0.3_3_8_3_7_8_9,
         0.3_4_5_7_0_3_1_2,
         0.3_5_9_3_7_5,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

     # with sega enabled
     # generator = torch.manual_seed(seed)
     generator.manual_seed(__lowerCamelCase  )
     lowerCamelCase__	=  pipe(
         [prompt]					,  generator=__lowerCamelCase					,  guidance_scale=__lowerCamelCase					,  num_inference_steps=50					,  output_type="np"					,  width=512					,  height=512					,  **__lowerCamelCase					,  )

     lowerCamelCase__	=  output.images
     lowerCamelCase__	=  image[0, -3:, -3:, -1]
     lowerCamelCase__	=  [
         0.4_2_2_8_5_1_5_6,
         0.3_6_9_1_4_0_6_2,
         0.2_9_0_7_7_1_4_8,
         0.4_2_0_4_1_0_1_6,
         0.4_1_9_1_8_9_4_5,
         0.3_5_4_9_8_0_4_7,
         0.3_6_1_8_1_6_4,
         0.4_4_2_3_8_2_8,
         0.4_3_1_1_5_2_3_4,
     ]

     assert image.shape == (1, 512, 512, 3)

     assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2









'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__A				:				Optional[int]					=		logging.get_logger(__name__)

__A				:				Tuple					=		{
    """facebook/convnext-tiny-224""": """https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json""",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class 					lowercase							(				_lowerCamelCase			,							_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				"convnext"

    def __init__(       self  :  Any					,  __lowerCamelCase  :  Dict=3					,  __lowerCamelCase  :  int=4					,  __lowerCamelCase  :  Any=4					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  str=None					,  __lowerCamelCase  :  Optional[int]="gelu"					,  __lowerCamelCase  :  List[str]=0.0_2					,  __lowerCamelCase  :  int=1E-12					,  __lowerCamelCase  :  Optional[Any]=1E-6					,  __lowerCamelCase  :  Any=0.0					,  __lowerCamelCase  :  Union[str, Any]=224					,  __lowerCamelCase  :  Dict=None					,  __lowerCamelCase  :  List[str]=None					,  **__lowerCamelCase  :  Tuple					,  )				->				Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     lowerCamelCase__	=  num_channels
     lowerCamelCase__	=  patch_size
     lowerCamelCase__	=  num_stages
     lowerCamelCase__	=  [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     lowerCamelCase__	=  [3, 3, 9, 3] if depths is None else depths
     lowerCamelCase__	=  hidden_act
     lowerCamelCase__	=  initializer_range
     lowerCamelCase__	=  layer_norm_eps
     lowerCamelCase__	=  layer_scale_init_value
     lowerCamelCase__	=  drop_path_rate
     lowerCamelCase__	=  image_size
     lowerCamelCase__	=  ["stem"] + [f'''stage{idx}''' for idx in range(1					,  len(self.depths  ) + 1  )]
     lowerCamelCase__	,						lowerCamelCase__	=  get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase					,  out_indices=__lowerCamelCase					,  stage_names=self.stage_names  )
class 					lowercase							(				_lowerCamelCase   ):


    '''simple docstring'''

    lowerCAmelCase__   					=				version.parse("1.11"   )

    @property
    def  a__					(       self  :  List[Any]  )				->				Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




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




    @property
    def  a__					(       self  :  Tuple  )				->				float:

     '''simple docstring'''




     return 1E-5

