
"""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  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							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 _lowercase					(self    ):

									"""simple docstring"""







									return 32

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 32

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.time_input_dim

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 1_00

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''    )
									return tokenizer

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = 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       ,       )

									SCREAMING_SNAKE_CASE_						 = MultilingualCLIP(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = text_encoder.eval()

									return text_encoder

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = {
									    '''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,
									}

									SCREAMING_SNAKE_CASE_						 = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_    )
									return model

							@property
							def _lowercase					(self    ):

									"""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 _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = VQModel(**self.dummy_movq_kwargs    )
									return model

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.dummy_text_encoder
									SCREAMING_SNAKE_CASE_						 = self.dummy_tokenizer
									SCREAMING_SNAKE_CASE_						 = self.dummy_unet
									SCREAMING_SNAKE_CASE_						 = self.dummy_movq

									SCREAMING_SNAKE_CASE_						 = {
									    '''num_train_timesteps''': 10_00,
									    '''beta_schedule''': '''linear''',
									    '''beta_start''': 0.0_00_85,
									    '''beta_end''': 0.0_12,
									    '''clip_sample''': False,
									    '''set_alpha_to_one''': False,
									    '''steps_offset''': 0,
									    '''prediction_type''': '''epsilon''',
									    '''thresholding''': False,
									}

									SCREAMING_SNAKE_CASE_						 = DDIMScheduler(**SCREAMING_SNAKE_CASE_    )

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

									return components

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=0    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, self.cross_attention_dim)       ,       rng=random.Random(seed + 1    )    ).to(SCREAMING_SNAKE_CASE_    )
									# create init_image
									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, 3, 64, 64)       ,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = image.cpu().permute(0       ,       2       ,       3       ,       1    )[0]
									SCREAMING_SNAKE_CASE_						 = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_    )    ).convert('''RGB'''    ).resize((2_56, 2_56)    )

									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											SCREAMING_SNAKE_CASE_						 = torch.manual_seed(SCREAMING_SNAKE_CASE_    )
									else:
											SCREAMING_SNAKE_CASE_						 = torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = {
									    '''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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''cpu'''

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_components()

									SCREAMING_SNAKE_CASE_						 = self.pipeline_class(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = pipe.to(SCREAMING_SNAKE_CASE_    )

									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = output.images

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

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

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

									SCREAMING_SNAKE_CASE_						 = np.array(
									    [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]    )
									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  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







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







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinsky/kandinsky_img2img_frog.npy'''    )

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

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

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

									pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

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

									SCREAMING_SNAKE_CASE_						 = pipeline(
									    SCREAMING_SNAKE_CASE_       ,       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       ,       strength=0.2       ,       output_type='''np'''       ,       )

									SCREAMING_SNAKE_CASE_						 = output.images[0]

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

									assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index