
"""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, KandinskyImgaImgPipeline, 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 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					KandinskyImgaImgPipeline
							_UpperCAmelCase      				=					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
							_UpperCAmelCase      				=					[
							    '''prompt''',
							    '''negative_prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							    '''image''',
							]
							_UpperCAmelCase      				=					[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''strength''',
							    '''guidance_scale''',
							    '''negative_prompt''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							_UpperCAmelCase      				=					False






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






							@property
							def        lowerCamelCase_   (  self						)			->							Dict:
									return 32






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									return self.time_input_dim






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return self.time_input_dim * 4






							@property
							def        lowerCamelCase_   (  self						)			->							Any:
									return 100






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'						)
									return tokenizer






							@property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		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=1005	,       )

									_UpperCAmelCase							 =		MultilingualCLIP(snake_case						)
									_UpperCAmelCase							 =		text_encoder.eval()

									return text_encoder






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									torch.manual_seed(0						)

									_UpperCAmelCase							 =		{
									    'in_channels': 4,
									    # 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							 =		UNetaDConditionModel(**snake_case						)
									return model






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									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        lowerCamelCase_   (  self						)			->							Optional[Any]:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		VQModel(**self.dummy_movq_kwargs						)
									return model






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		self.dummy_text_encoder
									_UpperCAmelCase							 =		self.dummy_tokenizer
									_UpperCAmelCase							 =		self.dummy_unet
									_UpperCAmelCase							 =		self.dummy_movq

									_UpperCAmelCase							 =		{
									    'num_train_timesteps': 1000,
									    'beta_schedule': 'linear',
									    'beta_start': 0.00085,
									    'beta_end': 0.012,
									    'clip_sample': False,
									    'set_alpha_to_one': False,
									    'steps_offset': 0,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									}

									_UpperCAmelCase							 =		DDIMScheduler(**snake_case						)

									_UpperCAmelCase							 =		{
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}

									return components






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							List[str]:
									_UpperCAmelCase							 =		floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(snake_case						)						).to(snake_case						)
									_UpperCAmelCase							 =		floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(seed + 1						)						).to(snake_case						)
									# create init_image
									_UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(snake_case						)						).to(snake_case						)
									_UpperCAmelCase							 =		image.cpu().permute(0	,       2	,       3	,       1						)[0]
									_UpperCAmelCase							 =		Image.fromarray(np.uinta(snake_case						)						).convert('RGB'						).resize((256, 256)						)

									if str(snake_case						).startswith('mps'						):
											_UpperCAmelCase							 =		torch.manual_seed(snake_case						)
									else:
											_UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)
									_UpperCAmelCase							 =		{
									    'prompt': 'horse',
									    'image': init_image,
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'num_inference_steps': 10,
									    'guidance_scale': 7.0,
									    'strength': 0.2,
									    'output_type': 'np',
									}
									return inputs






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()

									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									_UpperCAmelCase							 =		pipe.to(snake_case						)

									pipe.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		pipe(**self.get_dummy_inputs(snake_case						)						)
									_UpperCAmelCase							 =		output.images

									_UpperCAmelCase							 =		pipe(
									    **self.get_dummy_inputs(snake_case						)	,       return_dict=snake_case	,       )[0]

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

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

									_UpperCAmelCase							 =		np.array(
									    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]						)
									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()}'







@slow
@require_torch_gpu
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







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






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/kandinsky/kandinsky_img2img_frog.npy'						)

									_UpperCAmelCase							 =		load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
									_UpperCAmelCase							 =		'A red cartoon frog, 4k'

									_UpperCAmelCase							 =		KandinskyPriorPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-1-prior'	,       torch_dtype=torch.floataa						)
									pipe_prior.to(snake_case						)

									_UpperCAmelCase							 =		KandinskyImgaImgPipeline.from_pretrained(
									    'kandinsky-community/kandinsky-2-1'	,       torch_dtype=torch.floataa						)
									_UpperCAmelCase							 =		pipeline.to(snake_case						)

									pipeline.set_progress_bar_config(disable=snake_case						)

									_UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		pipe_prior(
									    snake_case	,       generator=snake_case	,       num_inference_steps=5	,       negative_prompt=''	,       ).to_tuple()

									_UpperCAmelCase							 =		pipeline(
									    snake_case	,       image=snake_case	,       image_embeds=snake_case	,       negative_image_embeds=snake_case	,       generator=snake_case	,       num_inference_steps=100	,       height=768	,       width=768	,       strength=0.2	,       output_type='np'	,       )

									_UpperCAmelCase							 =		output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(snake_case	,       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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 			lowercase__			(      A,					A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''swin'''

							_UpperCAmelCase      				=					{
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}






							def __init__(  self	,       snake_case=224	,       snake_case=4	,       snake_case=3	,       snake_case=96	,       snake_case=[2, 2, 6, 2]	,       snake_case=[3, 6, 12, 24]	,       snake_case=7	,       snake_case=4.0	,       snake_case=True	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case="gelu"	,       snake_case=False	,       snake_case=0.02	,       snake_case=1E-5	,       snake_case=32	,       snake_case=None	,       snake_case=None	,       **snake_case	,       )			->							List[Any]:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		depths
									_UpperCAmelCase							 =		len(snake_case						)
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		window_size
									_UpperCAmelCase							 =		mlp_ratio
									_UpperCAmelCase							 =		qkv_bias
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		use_absolute_embeddings
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		encoder_stride
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									_UpperCAmelCase							 =		int(embed_dim * 2 ** (len(snake_case						) - 1)						)
									_UpperCAmelCase							 =		['stem'] + [f'stage{idx}' for idx in range(1	,       len(snake_case						) + 1						)]
									_UpperCAmelCase ,					_UpperCAmelCase							 =		get_aligned_output_features_output_indices(
									    out_features=snake_case	,       out_indices=snake_case	,       stage_names=self.stage_names						)







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






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






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


