
'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
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_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__lowerCAmelCase    (  __a       ,					unittest.TestCase				):
     snake_case   :    List[str]							      =      PaintByExamplePipeline
     snake_case   :    str							      =      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     snake_case   :    str							      =      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     snake_case   :    int							      =      frozenset([]				)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



     def     snake_case_ (self	):
          torch.manual_seed(0	)
          _UpperCAmelCase					:  Tuple         =    UNetaDConditionModel(
              block_out_channels=(3_2, 6_4)						,	layers_per_block=2						,	sample_size=3_2						,	in_channels=9						,	out_channels=4						,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,	cross_attention_dim=3_2						,	)
          _UpperCAmelCase					:  Union[str, Any]         =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__	)
          torch.manual_seed(0	)
          _UpperCAmelCase					:  int         =    AutoencoderKL(
              block_out_channels=[3_2, 6_4]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=4						,	)
          torch.manual_seed(0	)
          _UpperCAmelCase					:  Tuple         =    CLIPVisionConfig(
              hidden_size=3_2						,	projection_dim=3_2						,	intermediate_size=3_7						,	layer_norm_eps=1e-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	image_size=3_2						,	patch_size=4						,	)
          _UpperCAmelCase					:  Optional[Any]         =    PaintByExampleImageEncoder(lowerCAmelCase__						,	proj_size=3_2	)
          _UpperCAmelCase					:  int         =    CLIPImageProcessor(crop_size=3_2						,	size=3_2	)

          _UpperCAmelCase					:  Tuple         =    {
              """unet""": unet,
              """scheduler""": scheduler,
              """vae""": vae,
              """image_encoder""": image_encoder,
              """safety_checker""": None,
              """feature_extractor""": feature_extractor,
          }
          return components



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  int         =    np.array(image.convert("""RGB"""	)	)
          _UpperCAmelCase					:  Any         =    image[None].transpose(0						,	3						,	1						,	2	)
          _UpperCAmelCase					:  str         =    torch.from_numpy(lowerCAmelCase__	).to(dtype=torch.floataa	) / 1_2_7.5 - 1.0
          return image



     def     snake_case_ (self						,	lowerCAmelCase__="cpu"						,	lowerCAmelCase__=0	):
          # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
          _UpperCAmelCase					:  Optional[Any]         =    floats_tensor((1, 3, 3_2, 3_2)						,	rng=random.Random(lowerCAmelCase__	)	).to(lowerCAmelCase__	)
          _UpperCAmelCase					:  Tuple         =    image.cpu().permute(0						,	2						,	3						,	1	)[0]
          _UpperCAmelCase					:  Union[str, Any]         =    Image.fromarray(np.uinta(lowerCAmelCase__	)	).convert("""RGB"""	).resize((6_4, 6_4)	)
          _UpperCAmelCase					:  List[str]         =    Image.fromarray(np.uinta(image + 4	)	).convert("""RGB"""	).resize((6_4, 6_4)	)
          _UpperCAmelCase					:  Optional[int]         =    Image.fromarray(np.uinta(lowerCAmelCase__	)	).convert("""RGB"""	).resize((3_2, 3_2)	)

          if str(lowerCAmelCase__	).startswith("""mps"""	):
               _UpperCAmelCase					:  List[str]         =    torch.manual_seed(lowerCAmelCase__	)
          else:
               _UpperCAmelCase					:  Optional[int]         =    torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
          _UpperCAmelCase					:  Optional[Any]         =    {
              """example_image""": example_image,
              """image""": init_image,
              """mask_image""": mask_image,
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
          }
          return inputs



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    self.get_dummy_components()

          # make sure here that pndm scheduler skips prk
          _UpperCAmelCase					:  int         =    PaintByExamplePipeline(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Union[str, Any]         =    pipe.to("""cpu"""	)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[Any]         =    self.get_dummy_inputs()
          _UpperCAmelCase					:  int         =    pipe(**lowerCAmelCase__	)
          _UpperCAmelCase					:  Any         =    output.images

          _UpperCAmelCase					:  Optional[Any]         =    image[0, -3:, -3:, -1]

          assert image.shape == (1, 6_4, 6_4, 3)
          _UpperCAmelCase					:  Optional[Any]         =    np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]	)

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



     def     snake_case_ (self	):
          _UpperCAmelCase					:  int         =    """cpu"""
          _UpperCAmelCase					:  str         =    self.get_dummy_inputs()
          inputs.pop("""mask_image"""	)
          _UpperCAmelCase					:  Dict         =    self.convert_to_pt(inputs.pop("""image"""	)	)
          _UpperCAmelCase					:  Optional[int]         =    image.clamp(0						,	1	) / 2

          # make sure here that pndm scheduler skips prk
          _UpperCAmelCase					:  str         =    PaintByExamplePipeline(**self.get_dummy_components()	)
          _UpperCAmelCase					:  Optional[int]         =    pipe.to(lowerCAmelCase__	)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

          _UpperCAmelCase					:  str         =    pipe(image=lowerCAmelCase__						,	mask_image=mask_image[:, 0]						,	**lowerCAmelCase__	)
          _UpperCAmelCase					:  List[Any]         =    output.images

          _UpperCAmelCase					:  Optional[int]         =    image.cpu().permute(0						,	2						,	3						,	1	)[0]
          _UpperCAmelCase					:  int         =    mask_image.cpu().permute(0						,	2						,	3						,	1	)[0]

          _UpperCAmelCase					:  Optional[Any]         =    Image.fromarray(np.uinta(lowerCAmelCase__	)	).convert("""RGB"""	)
          _UpperCAmelCase					:  Tuple         =    Image.fromarray(np.uinta(lowerCAmelCase__	)	).convert("""RGB"""	)

          _UpperCAmelCase					:  Any         =    pipe(**self.get_dummy_inputs()	)
          _UpperCAmelCase					:  List[Any]         =    output.images

          assert out_a.shape == (1, 6_4, 6_4, 3)
          assert np.abs(out_a.flatten() - out_a.flatten()	).max() < 5e-2



     def     snake_case_ (self	):
          super().test_inference_batch_single_identical(expected_max_diff=3e-3	)







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



     def     snake_case_ (self	):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



     def     snake_case_ (self	):
          # make sure here that pndm scheduler skips prk
          _UpperCAmelCase					:  Optional[int]         =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/paint_by_example/dog_in_bucket.png"""	)
          _UpperCAmelCase					:  Dict         =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/paint_by_example/mask.png"""	)
          _UpperCAmelCase					:  List[str]         =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/paint_by_example/panda.jpg"""	)

          _UpperCAmelCase					:  List[Any]         =    PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""	)
          _UpperCAmelCase					:  Optional[Any]         =    pipe.to(lowerCAmelCase__	)
          pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

          _UpperCAmelCase					:  str         =    torch.manual_seed(3_2_1	)
          _UpperCAmelCase					:  Any         =    pipe(
              image=lowerCAmelCase__						,	mask_image=lowerCAmelCase__						,	example_image=lowerCAmelCase__						,	generator=lowerCAmelCase__						,	guidance_scale=5.0						,	num_inference_steps=5_0						,	output_type="""np"""						,	)

          _UpperCAmelCase					:  str         =    output.images

          _UpperCAmelCase					:  Any         =    image[0, -3:, -3:, -1]

          assert image.shape == (1, 5_1_2, 5_1_2, 3)
          _UpperCAmelCase					:  Optional[int]         =    np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]	)

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






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


snake_case_  :	Any							  =							logging.get_logger(__name__)

snake_case_  :	Any							  =							{
    '''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   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  "mobilevit"




						def __init__(				self    :					Tuple ,			_a    :					Any=3 ,			_a    :					Any=256 ,			_a    :					Any=2 ,			_a    :					List[str]=[144, 192, 240] ,			_a    :					Optional[int]=[16, 32, 64, 96, 128, 160, 640] ,			_a    :					Union[str, Any]=4 ,			_a    :					Any=2.0 ,			_a    :					List[str]=4.0 ,			_a    :					Tuple="silu" ,			_a    :					Optional[Any]=3 ,			_a    :					List[str]=32 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Tuple=0.0 ,			_a    :					Union[str, Any]=0.1 ,			_a    :					Union[str, Any]=0.02 ,			_a    :					Tuple=1E-5 ,			_a    :					Any=True ,			_a    :					str=256 ,			_a    :					Tuple=[6, 12, 18] ,			_a    :					int=0.1 ,			_a    :					str=255 ,			**_a    :					Any ,			)							->   Optional[int]:


										"""simple docstring"""





										super().__init__(**_a     )

										_SCREAMING_SNAKE_CASE	     =num_channels
										_SCREAMING_SNAKE_CASE	     =image_size
										_SCREAMING_SNAKE_CASE	     =patch_size
										_SCREAMING_SNAKE_CASE	     =hidden_sizes
										_SCREAMING_SNAKE_CASE	     =neck_hidden_sizes
										_SCREAMING_SNAKE_CASE	     =num_attention_heads
										_SCREAMING_SNAKE_CASE	     =mlp_ratio
										_SCREAMING_SNAKE_CASE	     =expand_ratio
										_SCREAMING_SNAKE_CASE	     =hidden_act
										_SCREAMING_SNAKE_CASE	     =conv_kernel_size
										_SCREAMING_SNAKE_CASE	     =output_stride
										_SCREAMING_SNAKE_CASE	     =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE	     =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE	     =classifier_dropout_prob
										_SCREAMING_SNAKE_CASE	     =initializer_range
										_SCREAMING_SNAKE_CASE	     =layer_norm_eps
										_SCREAMING_SNAKE_CASE	     =qkv_bias

										# decode head attributes for semantic segmentation
										_SCREAMING_SNAKE_CASE	     =aspp_out_channels
										_SCREAMING_SNAKE_CASE	     =atrous_rates
										_SCREAMING_SNAKE_CASE	     =aspp_dropout_prob
										_SCREAMING_SNAKE_CASE	     =semantic_loss_ignore_index

class   A__							(						UpperCamelCase__     ):
						UpperCAmelCase									=  version.parse("1.11"     )




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


										"""simple docstring"""





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




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


										"""simple docstring"""





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




						@property
						def 							__UpperCamelCase (				self    :					List[Any]     )							->   float:


										"""simple docstring"""





										return 1E-4