






'''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       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

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

    @property
    def    a__				(					self     :     Any     )							->							Tuple:

     '''simple docstring'''




     return 32

    @property
    def    a__				(					self     :     List[str]     )							->							Optional[Any]:

     '''simple docstring'''




     return 32

    @property
    def    a__				(					self     :     Tuple     )							->							Optional[int]:

     '''simple docstring'''




     return self.time_input_dim

    @property
    def    a__				(					self     :     Union[str, Any]     )							->							Optional[Any]:

     '''simple docstring'''




     return self.time_input_dim * 4

    @property
    def    a__				(					self     :     Tuple     )							->							int:

     '''simple docstring'''




     return 100

    @property
    def    a__				(					self     :     List[str]     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
     return tokenizer

    @property
    def    a__				(					self     :     Optional[int]     )							->							str:

     '''simple docstring'''




     torch.manual_seed(0     )
     lowerCamelCase__  =     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				,     )

     lowerCamelCase__  =     MultilingualCLIP(__lowerCamelCase     )
     lowerCamelCase__  =     text_encoder.eval()

     return text_encoder

    @property
    def    a__				(					self     :     Any     )							->							Optional[Any]:

     '''simple docstring'''




     torch.manual_seed(0     )

     lowerCamelCase__  =     {
         "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,
     }

     lowerCamelCase__  =     UNetaDConditionModel(**__lowerCamelCase     )
     return model

    @property
    def    a__				(					self     :     Dict     )							->							Any:

     '''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    a__				(					self     :     int     )							->							Optional[Any]:

     '''simple docstring'''




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

    def    a__				(					self     :     List[Any]     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     self.dummy_text_encoder
     lowerCamelCase__  =     self.dummy_tokenizer
     lowerCamelCase__  =     self.dummy_unet
     lowerCamelCase__  =     self.dummy_movq

     lowerCamelCase__  =     {
         "num_train_timesteps": 1000,
         "beta_schedule": "linear",
         "beta_start": 0.0_0_0_8_5,
         "beta_end": 0.0_1_2,
         "clip_sample": False,
         "set_alpha_to_one": False,
         "steps_offset": 0,
         "prediction_type": "epsilon",
         "thresholding": False,
     }

     lowerCamelCase__  =     DDIMScheduler(**__lowerCamelCase     )

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

     return components

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[int]=0     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1     )     ).to(__lowerCamelCase     )
     # create init_image
     lowerCamelCase__  =     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     image.cpu().permute(0				,     2				,     3				,     1     )[0]
     lowerCamelCase__  =     Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     ).resize((256, 256)     )

     if str(__lowerCamelCase     ).startswith("mps"     ):
      lowerCamelCase__  =     torch.manual_seed(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "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    a__				(					self     :     List[Any]     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()

     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     lowerCamelCase__  =     pipe.to(__lowerCamelCase     )

     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     pipe(**self.get_dummy_inputs(__lowerCamelCase     )     )
     lowerCamelCase__  =     output.images

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

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

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

     lowerCamelCase__  =     np.array(
         [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]     )
     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    a__				(					self     :     str     )							->							Tuple:

     '''simple docstring'''




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




    def    a__				(					self     :     Union[str, Any]     )							->							Dict:

     '''simple docstring'''




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

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

     lowerCamelCase__  =     KandinskyPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1-prior"				,     torch_dtype=torch.floataa     )
     pipe_prior.to(__lowerCamelCase     )

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

     pipeline.set_progress_bar_config(disable=__lowerCamelCase     )

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

     lowerCamelCase__  =     pipeline(
         __lowerCamelCase				,     image=__lowerCamelCase				,     image_embeds=__lowerCamelCase				,     negative_image_embeds=__lowerCamelCase				,     generator=__lowerCamelCase				,     num_inference_steps=100				,     height=768				,     width=768				,     strength=0.2				,     output_type="np"				,     )

     lowerCamelCase__  =     output.images[0]

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

     assert_mean_pixel_difference(__lowerCamelCase				,     __lowerCamelCase     )










'''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


__A							:							Optional[Any]  							=		logging.get_logger(__name__)

__A							:							int  							=		{
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"vit"

    def __init__(					self     :     Optional[int]				,     __lowerCamelCase     :     Tuple=768				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     Tuple=12				,     __lowerCamelCase     :     int=3072				,     __lowerCamelCase     :     Optional[Any]="gelu"				,     __lowerCamelCase     :     Optional[int]=0.0				,     __lowerCamelCase     :     Tuple=0.0				,     __lowerCamelCase     :     Optional[Any]=0.0_2				,     __lowerCamelCase     :     int=1E-12				,     __lowerCamelCase     :     Tuple=224				,     __lowerCamelCase     :     int=16				,     __lowerCamelCase     :     Optional[int]=3				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     str=16				,     **__lowerCamelCase     :     Optional[int]				,     )							->							str:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     patch_size
     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     qkv_bias
     lowerCamelCase__  =     encoder_stride
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     Optional[int]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




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




    @property
    def    a__				(					self     :     Optional[int]     )							->							float:

     '''simple docstring'''




     return 1E-4

