

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 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:str										=      KandinskyImgaImgPipeline
     UpperCamelCase_	:Tuple										=      ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
     UpperCamelCase_	:List[Any]										=      [
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
     ]
     UpperCamelCase_	:Any										=      [
         """generator""",
         """height""",
         """width""",
         """strength""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_	:Union[str, Any]										=      False






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






     @property
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         return 32






     @property
     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         return self.time_input_dim






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






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






     @property
     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"	)
         return tokenizer






     @property
     def 	UpperCAmelCase_				(				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=1_005		,						)

         UpperCamelCase_			  =						MultilingualCLIP(_lowercase	)
         UpperCamelCase_			  =						text_encoder.eval()

         return text_encoder






     @property
     def 	UpperCAmelCase_				(				self	)->     Tuple:
         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(**_lowercase	)
         return model






     @property
     def 	UpperCAmelCase_				(				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 	UpperCAmelCase_				(				self	)->     Any:
         torch.manual_seed(0	)
         UpperCamelCase_			  =						VQModel(**self.dummy_movq_kwargs	)
         return model






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

         UpperCamelCase_			  =						{
             "num_train_timesteps": 1_000,
             "beta_schedule": "linear",
             "beta_start": 0.00_085,
             "beta_end": 0.012,
             "clip_sample": False,
             "set_alpha_to_one": False,
             "steps_offset": 0,
             "prediction_type": "epsilon",
             "thresholding": False,
         }

         UpperCamelCase_			  =						DDIMScheduler(**_lowercase	)

         UpperCamelCase_			  =						{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }

         return components






     def 	UpperCAmelCase_				(				self		,						_lowercase		,						_lowercase=0	)->     Optional[int]:
         UpperCamelCase_			  =						floats_tensor((1, self.cross_attention_dim)		,						rng=random.Random(_lowercase	)	).to(_lowercase	)
         UpperCamelCase_			  =						floats_tensor((1, self.cross_attention_dim)		,						rng=random.Random(seed + 1	)	).to(_lowercase	)
         # create init_image
         UpperCamelCase_			  =						floats_tensor((1, 3, 64, 64)		,						rng=random.Random(_lowercase	)	).to(_lowercase	)
         UpperCamelCase_			  =						image.cpu().permute(0		,						2		,						3		,						1	)[0]
         UpperCamelCase_			  =						Image.fromarray(np.uinta(_lowercase	)	).convert("RGB"	).resize((256, 256)	)

         if str(_lowercase	).startswith("mps"	):
             UpperCamelCase_			  =						torch.manual_seed(_lowercase	)
         else:
             UpperCamelCase_			  =						torch.Generator(device=_lowercase	).manual_seed(_lowercase	)
         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 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						"cpu"

         UpperCamelCase_			  =						self.get_dummy_components()

         UpperCamelCase_			  =						self.pipeline_class(**_lowercase	)
         UpperCamelCase_			  =						pipe.to(_lowercase	)

         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						pipe(**self.get_dummy_inputs(_lowercase	)	)
         UpperCamelCase_			  =						output.images

         UpperCamelCase_			  =						pipe(
             **self.get_dummy_inputs(_lowercase	)		,						return_dict=_lowercase		,						)[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(
             [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]	)
         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 						__magic_name__					(							unittest.TestCase				):






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






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         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(_lowercase	)

         UpperCamelCase_			  =						KandinskyImgaImgPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1"		,						torch_dtype=torch.floataa	)
         UpperCamelCase_			  =						pipeline.to(_lowercase	)

         pipeline.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						torch.Generator(device="cpu"	).manual_seed(0	)
         UpperCamelCase_  ,       UpperCamelCase_			  =						pipe_prior(
             _lowercase		,						generator=_lowercase		,						num_inference_steps=5		,						negative_prompt=""		,						).to_tuple()

         UpperCamelCase_			  =						pipeline(
             _lowercase		,						image=_lowercase		,						image_embeds=_lowercase		,						negative_image_embeds=_lowercase		,						generator=_lowercase		,						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(_lowercase		,						_lowercase	)



SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    800,
    799,
    600,
    599,
    500,
    400,
    399,
    377,
    355,
    333,
    311,
    288,
    266,
    244,
    222,
    200,
    199,
    177,
    155,
    133,
    111,
    88,
    66,
    44,
    22,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    976,
    952,
    928,
    905,
    882,
    858,
    857,
    810,
    762,
    715,
    714,
    572,
    429,
    428,
    286,
    285,
    238,
    190,
    143,
    142,
    118,
    95,
    71,
    47,
    24,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    988,
    977,
    966,
    955,
    944,
    933,
    922,
    911,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    350,
    300,
    299,
    266,
    233,
    200,
    199,
    179,
    159,
    140,
    120,
    100,
    99,
    88,
    77,
    66,
    55,
    44,
    33,
    22,
    11,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[Any]						=     [
    999,
    995,
    992,
    989,
    985,
    981,
    978,
    975,
    971,
    967,
    964,
    961,
    957,
    956,
    951,
    947,
    942,
    937,
    933,
    928,
    923,
    919,
    914,
    913,
    908,
    903,
    897,
    892,
    887,
    881,
    876,
    871,
    870,
    864,
    858,
    852,
    846,
    840,
    834,
    828,
    827,
    820,
    813,
    806,
    799,
    792,
    785,
    784,
    777,
    770,
    763,
    756,
    749,
    742,
    741,
    733,
    724,
    716,
    707,
    699,
    698,
    688,
    677,
    666,
    656,
    655,
    645,
    634,
    623,
    613,
    612,
    598,
    584,
    570,
    569,
    555,
    541,
    527,
    526,
    505,
    484,
    483,
    462,
    440,
    439,
    396,
    395,
    352,
    351,
    308,
    307,
    264,
    263,
    220,
    219,
    176,
    132,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    997,
    995,
    992,
    990,
    988,
    986,
    984,
    981,
    979,
    977,
    975,
    972,
    970,
    968,
    966,
    964,
    961,
    959,
    957,
    956,
    954,
    951,
    949,
    946,
    944,
    941,
    939,
    936,
    934,
    931,
    929,
    926,
    924,
    921,
    919,
    916,
    914,
    913,
    910,
    907,
    905,
    902,
    899,
    896,
    893,
    891,
    888,
    885,
    882,
    879,
    877,
    874,
    871,
    870,
    867,
    864,
    861,
    858,
    855,
    852,
    849,
    846,
    843,
    840,
    837,
    834,
    831,
    828,
    827,
    824,
    821,
    817,
    814,
    811,
    808,
    804,
    801,
    798,
    795,
    791,
    788,
    785,
    784,
    780,
    777,
    774,
    770,
    766,
    763,
    760,
    756,
    752,
    749,
    746,
    742,
    741,
    737,
    733,
    730,
    726,
    722,
    718,
    714,
    710,
    707,
    703,
    699,
    698,
    694,
    690,
    685,
    681,
    677,
    673,
    669,
    664,
    660,
    656,
    655,
    650,
    646,
    641,
    636,
    632,
    627,
    622,
    618,
    613,
    612,
    607,
    602,
    596,
    591,
    586,
    580,
    575,
    570,
    569,
    563,
    557,
    551,
    545,
    539,
    533,
    527,
    526,
    519,
    512,
    505,
    498,
    491,
    484,
    483,
    474,
    466,
    457,
    449,
    440,
    439,
    428,
    418,
    407,
    396,
    395,
    381,
    366,
    352,
    351,
    330,
    308,
    307,
    286,
    264,
    263,
    242,
    220,
    219,
    176,
    175,
    132,
    131,
    88,
    44,
    0,
]

SCREAMING_SNAKE_CASE		:Optional[int]						=     [
    999,
    991,
    982,
    974,
    966,
    958,
    950,
    941,
    933,
    925,
    916,
    908,
    900,
    899,
    874,
    850,
    825,
    800,
    799,
    700,
    600,
    500,
    400,
    300,
    200,
    100,
    0,
]

SCREAMING_SNAKE_CASE		:Dict						=     [
    999,
    992,
    985,
    978,
    971,
    964,
    957,
    949,
    942,
    935,
    928,
    921,
    914,
    907,
    900,
    899,
    879,
    859,
    840,
    820,
    800,
    799,
    766,
    733,
    700,
    699,
    650,
    600,
    599,
    500,
    499,
    400,
    399,
    300,
    299,
    200,
    199,
    100,
    99,
    0,
]

SCREAMING_SNAKE_CASE		:int						=     [
    999,
    996,
    992,
    989,
    985,
    982,
    979,
    975,
    972,
    968,
    965,
    961,
    958,
    955,
    951,
    948,
    944,
    941,
    938,
    934,
    931,
    927,
    924,
    920,
    917,
    914,
    910,
    907,
    903,
    900,
    899,
    891,
    884,
    876,
    869,
    861,
    853,
    846,
    838,
    830,
    823,
    815,
    808,
    800,
    799,
    788,
    777,
    766,
    755,
    744,
    733,
    722,
    711,
    700,
    699,
    688,
    677,
    666,
    655,
    644,
    633,
    622,
    611,
    600,
    599,
    585,
    571,
    557,
    542,
    528,
    514,
    500,
    499,
    485,
    471,
    457,
    442,
    428,
    414,
    400,
    399,
    379,
    359,
    340,
    320,
    300,
    299,
    279,
    259,
    240,
    220,
    200,
    199,
    166,
    133,
    100,
    99,
    66,
    33,
    0,
]
