





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 				_UpperCamelCase       (						lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       List[str]       = KandinskyPipeline
       _UpperCamelCase					:       Any       = [
           '''prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
       ]
       _UpperCamelCase					:       Dict       = ['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
       _UpperCamelCase					:       Dict       = [
           '''generator''',
           '''height''',
           '''width''',
           '''latents''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       _UpperCamelCase					:       Dict       = False

       @property
       def lowercase			(		self:			Union[str, Any]		)							->			Tuple:




              """simple docstring"""


              return 32

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




              """simple docstring"""


              return 32

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




              """simple docstring"""


              return self.time_input_dim

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




              """simple docstring"""


              return self.time_input_dim * 4

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




              """simple docstring"""


              return 100

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




              """simple docstring"""


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

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




              """simple docstring"""


              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(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	text_encoder.eval()

              return text_encoder

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




              """simple docstring"""


              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(**_SCREAMING_SNAKE_CASE		)
              return model

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




              """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:			Any		)							->			Tuple:




              """simple docstring"""


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

       def lowercase			(		self:			List[Any]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.dummy_text_encoder
              UpperCamelCase_ 							=	self.dummy_tokenizer
              UpperCamelCase_ 							=	self.dummy_unet
              UpperCamelCase_ 							=	self.dummy_movq

              UpperCamelCase_ 							=	DDIMScheduler(
                  num_train_timesteps=1000       , beta_schedule="linear"       , beta_start=0.0_00_85       , beta_end=0.0_12       , clip_sample=_SCREAMING_SNAKE_CASE       , set_alpha_to_one=_SCREAMING_SNAKE_CASE       , steps_offset=1       , prediction_type="epsilon"       , thresholding=_SCREAMING_SNAKE_CASE       , )

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

       def lowercase			(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			Any=0		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	floats_tensor((1, self.cross_attention_dim)       , rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	floats_tensor((1, self.cross_attention_dim)       , rng=random.Random(seed + 1		)		).to(_SCREAMING_SNAKE_CASE		)
              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_ 							=	torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	{
                  "prompt": "horse",
                  "image_embeds": image_embeds,
                  "negative_image_embeds": negative_image_embeds,
                  "generator": generator,
                  "height": 64,
                  "width": 64,
                  "guidance_scale": 4.0,
                  "num_inference_steps": 2,
                  "output_type": "np",
              }
              return inputs



       def lowercase			(		self:			int		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	"cpu"

              UpperCamelCase_ 							=	self.get_dummy_components()

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

              pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

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

              UpperCamelCase_ 							=	pipe(
                  **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)       , return_dict=_SCREAMING_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(
                  [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]		)

              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 				_UpperCamelCase       (						unittest.TestCase  ):

       def lowercase			(		self:			str		)							->			int:




              """simple docstring"""


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



       def lowercase			(		self:			Optional[int]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/kandinsky/kandinsky_text2img_cat_fp16.npy"		)

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

              UpperCamelCase_ 							=	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"       , torch_dtype=torch.floataa		)
              UpperCamelCase_ 							=	pipeline.to(_SCREAMING_SNAKE_CASE		)
              pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	"red cat, 4k photo"

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

              UpperCamelCase_ 							=	torch.Generator(device="cuda"		).manual_seed(0		)
              UpperCamelCase_ 							=	pipeline(
                  _SCREAMING_SNAKE_CASE       , image_embeds=_SCREAMING_SNAKE_CASE       , negative_image_embeds=_SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , num_inference_steps=100       , output_type="np"       , )

              UpperCamelCase_ 							=	output.images[0]

              assert image.shape == (512, 512, 3)

              assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)


from __future__ import annotations







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    [None] * (len(__A						) + len(__A						))

    _lowerCamelCase  :		Optional[int]			    =    0  # pointer to current index for left Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the right Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the sorted array Half

    while pointera < len(__A						) and pointera < len(__A						):
        if left_half[pointera] < right_half[pointera]:
            _lowerCamelCase  :		Optional[Any]			    =    left_half[pointera]
            pointera += 1
            index += 1
        else:
            _lowerCamelCase  :		List[Any]			    =    right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(__A						):
        _lowerCamelCase  :		int			    =    left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(__A						):
        _lowerCamelCase  :		Optional[Any]			    =    right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array







def     A__	(       __A						):







    '''simple docstring'''
    if len(__A						) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    _lowerCamelCase  :		int			    =    0 + (len(__A						) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    _lowerCamelCase  :		List[str]			    =    array[:middle]
    _lowerCamelCase  :		Optional[int]			    =    array[middle:]

    return merge(merge_sort(__A						) ,			merge_sort(__A						)						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




