


"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							KandinskyInpaintPipeline
		A    =							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
		A    =							[
		    "prompt",
		    "negative_prompt",
		    "image_embeds",
		    "negative_image_embeds",
		    "image",
		    "mask_image",
		]
		A    =							[
		    "generator",
		    "height",
		    "width",
		    "latents",
		    "guidance_scale",
		    "negative_prompt",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		A    =							False
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					return 32
		@property
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					return 32
		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return self.time_input_dim
		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					return self.time_input_dim * 4
		@property
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					return 1_00
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
					return tokenizer
		@property
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:Union[str, Any]					     =	MCLIPConfig(
					    numDims=self.cross_attention_dim					,				transformerDimensions=self.text_embedder_hidden_size					,				hidden_size=self.text_embedder_hidden_size					,				intermediate_size=37					,				num_attention_heads=4					,				num_hidden_layers=5					,				vocab_size=10_05					,				)

					_UpperCamelCase					:Optional[Any]					     =	MultilingualCLIP(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	text_encoder.eval()

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

					"""simple docstring"""



					torch.manual_seed(0   )

					_UpperCamelCase					:Any					     =	{
					    '''in_channels''': 9,
					    # Out channels is double in channels because predicts mean and variance
					    '''out_channels''': 8,
					    '''addition_embed_type''': '''text_image''',
					    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
					    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
					    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
					    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
					    '''layers_per_block''': 1,
					    '''encoder_hid_dim''': self.text_embedder_hidden_size,
					    '''encoder_hid_dim_type''': '''text_image_proj''',
					    '''cross_attention_dim''': self.cross_attention_dim,
					    '''attention_head_dim''': 4,
					    '''resnet_time_scale_shift''': '''scale_shift''',
					    '''class_embed_type''': None,
					}

					_UpperCamelCase					:Dict					     =	UNetaDConditionModel(**SCREAMING_SNAKE_CASE__   )
					return model
		@property
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					return {
					    "block_out_channels": [32, 64],
					    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
					    "in_channels": 3,
					    "latent_channels": 4,
					    "layers_per_block": 1,
					    "norm_num_groups": 8,
					    "norm_type": "spatial",
					    "num_vq_embeddings": 12,
					    "out_channels": 3,
					    "up_block_types": [
					        "AttnUpDecoderBlock2D",
					        "UpDecoderBlock2D",
					    ],
					    "vq_embed_dim": 4,
					}
		@property
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	VQModel(**self.dummy_movq_kwargs   )
					return model
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.dummy_text_encoder
					_UpperCamelCase					:str					     =	self.dummy_tokenizer
					_UpperCamelCase					:Dict					     =	self.dummy_unet
					_UpperCamelCase					:Dict					     =	self.dummy_movq

					_UpperCamelCase					:Optional[Any]					     =	DDIMScheduler(
					    num_train_timesteps=10_00					,				beta_schedule='''linear'''					,				beta_start=0.0_0_0_8_5					,				beta_end=0.0_1_2					,				clip_sample=SCREAMING_SNAKE_CASE__					,				set_alpha_to_one=SCREAMING_SNAKE_CASE__					,				steps_offset=1					,				prediction_type='''epsilon'''					,				thresholding=SCREAMING_SNAKE_CASE__					,				)

					_UpperCamelCase					:int					     =	{
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''movq''': movq,
					}

					return components
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=0   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	floats_tensor((1, self.cross_attention_dim)					,				rng=random.Random(SCREAMING_SNAKE_CASE__   )   ).to(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:int					     =	floats_tensor((1, self.cross_attention_dim)					,				rng=random.Random(seed + 1   )   ).to(SCREAMING_SNAKE_CASE__   )
					# create init_image
					_UpperCamelCase					:Optional[Any]					     =	floats_tensor((1, 3, 64, 64)					,				rng=random.Random(SCREAMING_SNAKE_CASE__   )   ).to(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	image.cpu().permute(0					,				2					,				3					,				1   )[0]
					_UpperCamelCase					:List[Any]					     =	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )
					# create mask
					_UpperCamelCase					:Tuple					     =	np.ones((64, 64)					,				dtype=np.floataa   )
					_UpperCamelCase					:List[str]					     =	0

					if str(SCREAMING_SNAKE_CASE__   ).startswith('''mps'''   ):
								_UpperCamelCase					:Dict					     =	torch.manual_seed(SCREAMING_SNAKE_CASE__   )
					else:
								_UpperCamelCase					:Any					     =	torch.Generator(device=SCREAMING_SNAKE_CASE__   ).manual_seed(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Any					     =	{
					    '''prompt''': '''horse''',
					    '''image''': init_image,
					    '''mask_image''': mask,
					    '''image_embeds''': image_embeds,
					    '''negative_image_embeds''': negative_image_embeds,
					    '''generator''': generator,
					    '''height''': 64,
					    '''width''': 64,
					    '''num_inference_steps''': 2,
					    '''guidance_scale''': 4.0,
					    '''output_type''': '''np''',
					}
					return inputs
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	'''cpu'''

					_UpperCamelCase					:Union[str, Any]					     =	self.get_dummy_components()

					_UpperCamelCase					:Tuple					     =	self.pipeline_class(**SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	pipe.to(SCREAMING_SNAKE_CASE__   )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:int					     =	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )   )
					_UpperCamelCase					:Optional[int]					     =	output.images

					_UpperCamelCase					:Optional[int]					     =	pipe(
					    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__   )					,				return_dict=SCREAMING_SNAKE_CASE__					,				)[0]

					_UpperCamelCase					:Optional[int]					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:Optional[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					print(f"image.shape {image.shape}"   )

					assert image.shape == (1, 64, 64, 3)

					_UpperCamelCase					:Any					     =	np.array(
					    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

					assert (
					    np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
					assert (
					    np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
					), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"





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

					"""simple docstring"""



					super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



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





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

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''   )

					_UpperCamelCase					:str					     =	load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
					_UpperCamelCase					:str					     =	np.ones((7_68, 7_68)					,				dtype=np.floataa   )
					_UpperCamelCase					:List[Any]					     =	0

					_UpperCamelCase					:Optional[Any]					     =	'''a hat'''

					_UpperCamelCase					:Tuple					     =	KandinskyPriorPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-prior'''					,				torch_dtype=torch.floataa   )
					pipe_prior.to(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	KandinskyInpaintPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-inpaint'''					,				torch_dtype=torch.floataa   )
					_UpperCamelCase					:List[Any]					     =	pipeline.to(SCREAMING_SNAKE_CASE__   )
					pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.Generator(device='''cpu'''   ).manual_seed(0   )
					_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	pipe_prior(
					    SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				num_inference_steps=5					,				negative_prompt=''''''					,				).to_tuple()

					_UpperCamelCase					:Union[str, Any]					     =	pipeline(
					    SCREAMING_SNAKE_CASE__					,				image=SCREAMING_SNAKE_CASE__					,				mask_image=SCREAMING_SNAKE_CASE__					,				image_embeds=SCREAMING_SNAKE_CASE__					,				negative_image_embeds=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				num_inference_steps=1_00					,				height=7_68					,				width=7_68					,				output_type='''np'''					,				)

					_UpperCamelCase					:Optional[Any]					     =	output.images[0]

					assert image.shape == (7_68, 7_68, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__					,				SCREAMING_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


UpperCamelCase__				:Union[str, Any]			   =	logging.get_logger(__name__)

UpperCamelCase__				:Union[str, Any]			   =	{
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"mobilenet_v2"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=2_24					,				SCREAMING_SNAKE_CASE__=1.0					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=8					,				SCREAMING_SNAKE_CASE__=6					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="relu6"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.8					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0_0_1					,				SCREAMING_SNAKE_CASE__=2_55					,				**SCREAMING_SNAKE_CASE__					,				)      ->	int:

					"""simple docstring"""



					super().__init__(**SCREAMING_SNAKE_CASE__   )

					if depth_multiplier <= 0:
								raise ValueError('''depth_multiplier must be greater than zero.'''   )

					_UpperCamelCase					:Optional[int]					     =	num_channels
					_UpperCamelCase					:Tuple					     =	image_size
					_UpperCamelCase					:Union[str, Any]					     =	depth_multiplier
					_UpperCamelCase					:Any					     =	depth_divisible_by
					_UpperCamelCase					:Optional[int]					     =	min_depth
					_UpperCamelCase					:Union[str, Any]					     =	expand_ratio
					_UpperCamelCase					:str					     =	output_stride
					_UpperCamelCase					:Tuple					     =	first_layer_is_expansion
					_UpperCamelCase					:Any					     =	finegrained_output
					_UpperCamelCase					:Union[str, Any]					     =	hidden_act
					_UpperCamelCase					:Any					     =	tf_padding
					_UpperCamelCase					:str					     =	classifier_dropout_prob
					_UpperCamelCase					:Optional[Any]					     =	initializer_range
					_UpperCamelCase					:int					     =	layer_norm_eps
					_UpperCamelCase					:Union[str, Any]					     =	semantic_loss_ignore_index






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							version.parse("1.11"		)
		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



					return OrderedDict([('''pixel_values''', {0: '''batch'''})]   )
		@property
		def   _UpperCamelCase( self   )      ->	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   )      ->	float:

					"""simple docstring"""



					return 1E-4

