


'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class 			UpperCAmelCase			:

 def __init__(			self			:						Union[str, Any],       a_			:						Optional[Any],       a_			:						Optional[Any]=sys.maxsize       ):





    """simple docstring"""



    UpperCamelCase__			    =    "bilinear"
    UpperCamelCase__			    =    max_size
    UpperCamelCase__			    =    short_edge_length

 def __call__(			self			:						int,       a_			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    []
    for img in imgs:
       UpperCamelCase__    ,		UpperCamelCase__			    =    img.shape[:2]
       # later: provide list and randomly choose index for resize
       UpperCamelCase__			    =    np.random.randint(self.short_edge_length[0],       self.short_edge_length[1] + 1       )
       if size == 0:
          return img
       UpperCamelCase__			    =    size * 1.0 / min(a_,       a_       )
       if h < w:
          UpperCamelCase__    ,		UpperCamelCase__			    =    size, scale * w
       else:
          UpperCamelCase__    ,		UpperCamelCase__			    =    scale * h, size
       if max(a_,       a_       ) > self.max_size:
          UpperCamelCase__			    =    self.max_size * 1.0 / max(a_,       a_       )
          UpperCamelCase__			    =    newh * scale
          UpperCamelCase__			    =    neww * scale
       UpperCamelCase__			    =    int(neww + 0.5       )
       UpperCamelCase__			    =    int(newh + 0.5       )

       if img.dtype == np.uinta:
          UpperCamelCase__			    =    Image.fromarray(a_       )
          UpperCamelCase__			    =    pil_image.resize((neww, newh),       PILImageResampling.BILINEAR       )
          UpperCamelCase__			    =    np.asarray(a_       )
       else:
          UpperCamelCase__			    =    img.permute(2,       0,       1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
          UpperCamelCase__			    =    nn.functional.interpolate(
              a_,       (newh, neww),       mode=self.interp_method,       align_corners=a_       ).squeeze(0       )
       img_augs.append(a_       )

    return img_augs







class 			UpperCAmelCase			:

 def __init__(			self			:						int,       a_			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],       cfg.INPUT.MAX_SIZE_TEST       )
    UpperCamelCase__			    =    cfg.INPUT.FORMAT
    UpperCamelCase__			    =    cfg.SIZE_DIVISIBILITY
    UpperCamelCase__			    =    cfg.PAD_VALUE
    UpperCamelCase__			    =    cfg.INPUT.MAX_SIZE_TEST
    UpperCamelCase__			    =    cfg.MODEL.DEVICE
    UpperCamelCase__			    =    torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       ),       1,       1       )
    UpperCamelCase__			    =    torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       ),       1,       1       )
    UpperCamelCase__			    =    lambda a_       : (x - self.pixel_mean) / self.pixel_std

 def 		lowercase_    (			self			:						Dict,       a_			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    tuple(max(a_       ) for s in zip(*[img.shape for img in images]       )       )
    UpperCamelCase__			    =    [im.shape[-2:] for im in images]
    UpperCamelCase__			    =    [
        nn.functional.pad(
            a_,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],       value=self.pad_value,       )
        for size, im in zip(a_,       a_       )
    ]

    return torch.stack(a_       ), torch.tensor(a_       )

 def __call__(			self			:						List[Any],       a_			:						Optional[int],       a_			:						List[Any]=False       ):





    """simple docstring"""



    with torch.no_grad():
       if not isinstance(a_,       a_       ):
          UpperCamelCase__			    =    [images]
       if single_image:
          assert len(a_       ) == 1
       for i in range(len(a_       )       ):
          if isinstance(images[i],       torch.Tensor       ):
             images.insert(a_,       images.pop(a_       ).to(self.device       ).float()       )
          elif not isinstance(images[i],       torch.Tensor       ):
             images.insert(
                 a_,       torch.as_tensor(img_tensorize(images.pop(a_       ),       input_format=self.input_format       )       )
                 .to(self.device       )
                 .float(),       )
            # resize smallest edge
       UpperCamelCase__			    =    torch.tensor([im.shape[:2] for im in images]       )
       UpperCamelCase__			    =    self.aug(a_       )
       # transpose images and convert to torch tensors
       # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
       # now normalize before pad to avoid useless arithmetic
       UpperCamelCase__			    =    [self.normalizer(a_       ) for x in images]
       # now pad them to do the following operations
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.pad(a_       )
       # Normalize

       if self.size_divisibility > 0:
          raise NotImplementedError()
       # pad
       UpperCamelCase__			    =    torch.true_divide(a_,       a_       )
       if single_image:
          return images[0], sizes[0], scales_yx[0]
       else:
          return images, sizes, scales_yx







def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					int )		->       str:





   '''simple docstring'''







   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes






def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Tuple[int, int] )		->       int:





   '''simple docstring'''







   assert torch.isfinite(_UpperCamelCase ).all(), "Box tensor contains infinite or NaN!"
   UpperCamelCase__    ,		UpperCamelCase__			    =    box_size
   tensor[:, 0].clamp_(min=0     ,		max=_UpperCamelCase )
   tensor[:, 1].clamp_(min=0     ,		max=_UpperCamelCase )
   tensor[:, 2].clamp_(min=0     ,		max=_UpperCamelCase )
   tensor[:, 3].clamp_(min=0     ,		max=_UpperCamelCase )



'''simple docstring'''

import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       List[Any]            =       KandinskyVaaPriorEmbaEmbPipeline
 _lowerCamelCase			:       Tuple            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       ['prompt', 'image']
 _lowerCamelCase			:       List[str]            =       [
     'num_images_per_prompt',
     'strength',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 _lowerCamelCase			:       List[str]            =       False

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    return 32

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    return self.time_input_dim

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    return self.time_input_dim * 4

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return 100

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
    return tokenizer

 @property
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPTextConfig(
        bos_token_id=0,       eos_token_id=2,       hidden_size=self.text_embedder_hidden_size,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       layer_norm_eps=1e-0_5,       num_attention_heads=4,       num_hidden_layers=5,       pad_token_id=1,       vocab_size=1000,       )
    return CLIPTextModelWithProjection(a_       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    torch.manual_seed(0       )

    UpperCamelCase__			    =    {
        "num_attention_heads": 2,
        "attention_head_dim": 12,
        "embedding_dim": self.text_embedder_hidden_size,
        "num_layers": 1,
    }

    UpperCamelCase__			    =    PriorTransformer(**a_       )
    # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
    UpperCamelCase__			    =    nn.Parameter(torch.ones(model.clip_std.shape       )       )
    return model

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    torch.manual_seed(0       )
    UpperCamelCase__			    =    CLIPVisionConfig(
        hidden_size=self.text_embedder_hidden_size,       image_size=224,       projection_dim=self.text_embedder_hidden_size,       intermediate_size=37,       num_attention_heads=4,       num_channels=3,       num_hidden_layers=5,       patch_size=14,       )

    UpperCamelCase__			    =    CLIPVisionModelWithProjection(a_       )
    return model

 @property
 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    CLIPImageProcessor(
        crop_size=224,       do_center_crop=a_,       do_normalize=a_,       do_resize=a_,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

    return image_processor

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.dummy_prior
    UpperCamelCase__			    =    self.dummy_image_encoder
    UpperCamelCase__			    =    self.dummy_text_encoder
    UpperCamelCase__			    =    self.dummy_tokenizer
    UpperCamelCase__			    =    self.dummy_image_processor

    UpperCamelCase__			    =    UnCLIPScheduler(
        variance_type="fixed_small_log",       prediction_type="sample",       num_train_timesteps=1000,       clip_sample=a_,       clip_sample_range=10.0,       )

    UpperCamelCase__			    =    {
        "prior": prior,
        "image_encoder": image_encoder,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "scheduler": scheduler,
        "image_processor": image_processor,
    }

    return components

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						List[str],       a_			:						Union[str, Any]=0       ):





    """simple docstring"""



    if str(a_       ).startswith("mps"       ):
       UpperCamelCase__			    =    torch.manual_seed(a_       )
    else:
       UpperCamelCase__			    =    torch.Generator(device=a_       ).manual_seed(a_       )

    UpperCamelCase__			    =    floats_tensor((1, 3, 64, 64),       rng=random.Random(a_       )       ).to(a_       )
    UpperCamelCase__			    =    image.cpu().permute(0,       2,       3,       1       )[0]
    UpperCamelCase__			    =    Image.fromarray(np.uinta(a_       )       ).convert("RGB"       ).resize((256, 256)       )

    UpperCamelCase__			    =    {
        "prompt": "horse",
        "image": init_image,
        "strength": 0.5,
        "generator": generator,
        "guidance_scale": 4.0,
        "num_inference_steps": 2,
        "output_type": "np",
    }
    return inputs

 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    UpperCamelCase__			    =    "cpu"

    UpperCamelCase__			    =    self.get_dummy_components()

    UpperCamelCase__			    =    self.pipeline_class(**a_       )
    UpperCamelCase__			    =    pipe.to(a_       )

    pipe.set_progress_bar_config(disable=a_       )

    UpperCamelCase__			    =    pipe(**self.get_dummy_inputs(a_       )       )
    UpperCamelCase__			    =    output.image_embeds

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

    UpperCamelCase__			    =    image[0, -10:]
    UpperCamelCase__			    =    image_from_tuple[0, -10:]

    assert image.shape == (1, 32)

    UpperCamelCase__			    =    np.array(
        [
            0.1_071_284,
            1.3_330_271,
            0.61_260_223,
            -0.6_691_065,
            -0.3_846_852,
            -1.0_303_661,
            0.22_716_111,
            0.03_348_901,
            0.30_040_675,
            -0.24_805_029,
        ]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    True
    UpperCamelCase__			    =    False

    self._test_inference_batch_single_identical(
        test_max_difference=a_,       relax_max_difference=a_,       test_mean_pixel_difference=a_,       )

 @skip_mps
 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    torch_device == "cpu"
    UpperCamelCase__			    =    False

    self._test_attention_slicing_forward_pass(
        test_max_difference=a_,       test_mean_pixel_difference=a_,       )