






'''simple docstring'''





import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   int							=						StableDiffusionInstructPixaPixPipeline
    snake_case__							:   List[str]							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
    snake_case__							:   List[Any]							=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    snake_case__							:   Union[str, Any]							=						IMAGE_TO_IMAGE_IMAGE_PARAMS
    snake_case__							:   Any							=						IMAGE_TO_IMAGE_IMAGE_PARAMS






    def       _UpperCamelCase    (       self )							-> str:
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			str    	=       UNetaDConditionModel(
             block_out_channels=(32, 64)    ,							layers_per_block=2    ,							sample_size=32    ,							in_channels=8    ,							out_channels=4    ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,							cross_attention_dim=32    ,							)
         SCREAMING_SNAKE_CASE					:			Tuple    	=       PNDMScheduler(skip_prk_steps=lowercase_ )
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       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    ,							)
         torch.manual_seed(0 )
         SCREAMING_SNAKE_CASE					:			Any    	=       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=1_000    ,							)
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       CLIPTextModel(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=0 )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(lowercase_ ) ).to(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 )[0]
         SCREAMING_SNAKE_CASE					:			Dict    	=       Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' )
         if str(lowercase_ ).startswith('mps' ):
              SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.manual_seed(lowercase_ )
         else:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       {
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'image_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       'cpu'  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_dummy_components()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       StableDiffusionInstructPixaPixPipeline(**lowercase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       sd_pipe.to(lowercase_ )
         sd_pipe.set_progress_bar_config(disable=lowercase_ )

         SCREAMING_SNAKE_CASE					:			str    	=       self.get_dummy_inputs(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       sd_pipe(**lowercase_ ).images
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         SCREAMING_SNAKE_CASE					:			Dict    	=       np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       'cpu'  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.get_dummy_components()
         SCREAMING_SNAKE_CASE					:			str    	=       StableDiffusionInstructPixaPixPipeline(**lowercase_ )
         SCREAMING_SNAKE_CASE					:			Any    	=       sd_pipe.to(lowercase_ )
         sd_pipe.set_progress_bar_config(disable=lowercase_ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_dummy_inputs(lowercase_ )
         SCREAMING_SNAKE_CASE					:			str    	=       'french fries'
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       sd_pipe(**lowercase_    ,							negative_prompt=lowercase_ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       output.images
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       'cpu'  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_dummy_components()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       StableDiffusionInstructPixaPixPipeline(**lowercase_ )
         SCREAMING_SNAKE_CASE					:			int    	=       sd_pipe.to(lowercase_ )
         sd_pipe.set_progress_bar_config(disable=lowercase_ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_dummy_inputs(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       [inputs['prompt']] * 2

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array(inputs['image'] ).astype(np.floataa ) / 255.0
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.from_numpy(lowercase_ ).unsqueeze(0 ).to(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image / 2 + 0.5
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image.permute(0    ,							3    ,							1    ,							2 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       image.repeat(2    ,							1    ,							1    ,							1 )

         SCREAMING_SNAKE_CASE					:			Dict    	=       sd_pipe(**lowercase_ ).images
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			int    	=       'cpu'  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_dummy_components()
         SCREAMING_SNAKE_CASE					:			int    	=       EulerAncestralDiscreteScheduler(
             beta_start=0.0_0_0_8_5    ,							beta_end=0.0_1_2    ,							beta_schedule='scaled_linear' )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       StableDiffusionInstructPixaPixPipeline(**lowercase_ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       sd_pipe.to(lowercase_ )
         sd_pipe.set_progress_bar_config(disable=lowercase_ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       self.get_dummy_inputs(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Any    	=       sd_pipe(**lowercase_ ).images
         SCREAMING_SNAKE_CASE					:			int    	=       image[0, -3:, -3:, -1]

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [round(lowercase_    ,							4 ) for x in image_slice.flatten().tolist()]
         print(','.join([str(lowercase_ ) for x in slice] ) )

         assert image.shape == (1, 32, 32, 3)
         SCREAMING_SNAKE_CASE					:			Dict    	=       np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_dummy_components()
         SCREAMING_SNAKE_CASE					:			str    	=       StableDiffusionInstructPixaPixPipeline(**lowercase_ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       VaeImageProcessor(do_resize=lowercase_    ,							do_normalize=lowercase_ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )

         SCREAMING_SNAKE_CASE					:			Any    	=       pipe(**self.get_dummy_inputs_by_type(lowercase_    ,							input_image_type='pt' ) )[0]

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       components['vae']
         SCREAMING_SNAKE_CASE					:			int    	=       self.get_dummy_inputs_by_type(lowercase_    ,							input_image_type='pt' )

         for image_param in self.image_latents_params:
              if image_param in inputs.keys():
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       vae.encode(inputs[image_param] ).latent_dist.mode()

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       pipe(**lowercase_ )[0]

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.abs(out - out_latents_inputs ).max()
         self.assertLess(lowercase_    ,							1E-4    ,							'passing latents as image input generate different result from passing image' )






@slow
@require_torch_gpu
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> List[Any]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def       _UpperCamelCase    (       self    ,							lowercase__=0 )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.manual_seed(lowercase_ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_image(
             'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
         SCREAMING_SNAKE_CASE					:			str    	=       {
             'prompt': 'turn him into a cyborg',
             'image': image,
             'generator': generator,
             'num_inference_steps': 3,
             'guidance_scale': 7.5,
             'image_guidance_scale': 1.0,
             'output_type': 'numpy',
         }
         return inputs






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,							safety_checker=lowercase_ )
         pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_inputs()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       pipe(**lowercase_ ).images
         SCREAMING_SNAKE_CASE					:			List[str]    	=       image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )

         assert np.abs(expected_slice - image_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,							safety_checker=lowercase_ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       LMSDiscreteScheduler.from_config(pipe.scheduler.config )
         pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE					:			Dict    	=       self.get_inputs()
         SCREAMING_SNAKE_CASE					:			int    	=       pipe(**lowercase_ ).images
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			int    	=       np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )

         assert np.abs(expected_slice - image_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,							safety_checker=lowercase_ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       DDIMScheduler.from_config(pipe.scheduler.config )
         pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_inputs()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pipe(**lowercase_ ).images
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )

         assert np.abs(expected_slice - image_slice ).max() < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			str    	=       0

         def callback_fn(lowercase__    ,							lowercase__    ,							lowercase__ ) -> None:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                   SCREAMING_SNAKE_CASE					:			str    	=       latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       latents[0, -3:, -3:, -1]
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )

                   assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
              elif step == 2:
                   SCREAMING_SNAKE_CASE					:			Any    	=       latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 64)
                   SCREAMING_SNAKE_CASE					:			Any    	=       latents[0, -3:, -3:, -1]
                   SCREAMING_SNAKE_CASE					:			int    	=       np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )

                   assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2

         SCREAMING_SNAKE_CASE					:			str    	=       False

         SCREAMING_SNAKE_CASE					:			Any    	=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,							safety_checker=lowercase_    ,							torch_dtype=torch.floataa )
         SCREAMING_SNAKE_CASE					:			Dict    	=       pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE					:			int    	=       self.get_inputs()
         pipe(**lowercase_    ,							callback=lowercase_    ,							callback_steps=1 )
         assert callback_fn.has_been_called
         assert number_of_steps == 3






    def       _UpperCamelCase    (       self )							-> int:
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
             'timbrooks/instruct-pix2pix'    ,							safety_checker=lowercase_    ,							torch_dtype=torch.floataa )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )
         pipe.enable_attention_slicing(1 )
         pipe.enable_sequential_cpu_offload()

         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_inputs()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       pipe(**lowercase_ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       inputs['image'].resize((504, 504) )

         SCREAMING_SNAKE_CASE					:			Any    	=       'timbrooks/instruct-pix2pix'
         SCREAMING_SNAKE_CASE					:			Dict    	=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
             lowercase_    ,							safety_checker=lowercase_    ,							)
         pipe.to(lowercase_ )
         pipe.set_progress_bar_config(disable=lowercase_ )
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE					:			Any    	=       pipe(**lowercase_ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       output.images[0]

         SCREAMING_SNAKE_CASE					:			int    	=       image[255:258, 383:386, -1]

         assert image.shape == (504, 504, 3)
         SCREAMING_SNAKE_CASE					:			Dict    	=       np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3




'''simple docstring'''





import warnings
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


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """nvidia/segformer-b0-finetuned-ade-512-512""": (
        """https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"""
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
                  ' removed, as the behaviour will default to that of reshape_last_stage = True.'    ,							lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			int    	=       num_channels
         SCREAMING_SNAKE_CASE					:			int    	=       num_encoder_blocks
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       depths
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sr_ratios
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       patch_sizes
         SCREAMING_SNAKE_CASE					:			int    	=       strides
         SCREAMING_SNAKE_CASE					:			Tuple    	=       mlp_ratios
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Tuple    	=       classifier_dropout_prob
         SCREAMING_SNAKE_CASE					:			Any    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			int    	=       layer_norm_eps
         SCREAMING_SNAKE_CASE					:			Any    	=       decoder_hidden_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       kwargs.get('reshape_last_stage'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       semantic_loss_ignore_index






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






    @property
    def       _UpperCamelCase    (       self )							-> Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ] )






    @property
    def       _UpperCamelCase    (       self )							-> float:
         return 1E-4






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



