




'''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      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   List[Any]										=      KandinskyImgaImgPipeline
       __lowercase	:   int										=      ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
       __lowercase	:   List[Any]										=      [
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       __lowercase	:   Optional[int]										=      [
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       __lowercase	:   List[Any]										=      False


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



            '''simple docstring'''




            return 32


       @property
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            return 32


       @property
       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            return self.time_input_dim


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



            '''simple docstring'''




            return self.time_input_dim * 4


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



            '''simple docstring'''




            return 100


       @property
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




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


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



            '''simple docstring'''




            torch.manual_seed(0	)
            __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=1005     ,	)

            __snake_case            =	MultilingualCLIP(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	text_encoder.eval()

            return text_encoder


       @property
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            torch.manual_seed(0	)

            __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,
            }

            __snake_case            =	UNetaDConditionModel(**__SCREAMING_SNAKE_CASE	)
            return model


       @property
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''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    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	self.dummy_text_encoder
            __snake_case            =	self.dummy_tokenizer
            __snake_case            =	self.dummy_unet
            __snake_case            =	self.dummy_movq

            __snake_case            =	{
                '''num_train_timesteps''': 1000,
                '''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,
            }

            __snake_case            =	DDIMScheduler(**__SCREAMING_SNAKE_CASE	)

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

            return components


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=0	)   ->	List[str]:



            '''simple docstring'''




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

            if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
                 __snake_case            =	torch.manual_seed(__SCREAMING_SNAKE_CASE	)
            else:
                 __snake_case            =	torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
            __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    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	'''cpu'''

            __snake_case            =	self.get_dummy_components()

            __snake_case            =	self.pipeline_class(**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	pipe.to(__SCREAMING_SNAKE_CASE	)

            pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)	)
            __snake_case            =	output.images

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

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

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

            __snake_case            =	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      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




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




       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




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

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

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

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

            pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

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

            __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=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='''np'''     ,	)

            __snake_case            =	output.images[0]

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

            assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)












'''simple docstring'''



from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = False     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = True     ,	__SCREAMING_SNAKE_CASE = "arrow"     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	Union[str, Any]:



            '''simple docstring'''




            super().__init__(
                split=__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	keep_in_memory=__SCREAMING_SNAKE_CASE     ,	streaming=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)
            __snake_case            =	load_from_cache_file
            __snake_case            =	file_format
            __snake_case            =	Spark(
                df=__SCREAMING_SNAKE_CASE     ,	features=__SCREAMING_SNAKE_CASE     ,	cache_dir=__SCREAMING_SNAKE_CASE     ,	working_dir=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)




       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            if self.streaming:
                 return self.builder.as_streaming_dataset(split=self.split	)
            __snake_case            =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
            self.builder.download_and_prepare(
                download_mode=__SCREAMING_SNAKE_CASE     ,	file_format=self._file_format     ,	)
            return self.builder.as_dataset(split=self.split	)






