



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"""


 a__				: str		       =							KandinskyImgaImgPipeline
 a__				: Any		       =							["prompt", "image_embeds", "negative_image_embeds", "image"]
 a__				: int		       =							[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
 ]
 a__				: List[Any]		       =							[
     "generator",
     "height",
     "width",
     "strength",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 a__				: Union[str, Any]		       =							False




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
       return 32




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




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Union[str, Any]:
       return self.time_input_dim




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			int:
       return self.time_input_dim * 4




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Any:
       return 100




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




 @property
 def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, 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   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			List[Any]:
       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   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[Any]:
       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   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			int:
       torch.manual_seed(0					)
       UpperCAmelCase_=       VQModel(**self.dummy_movq_kwargs					)
       return model




 def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
       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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Union[str, Any]=0					)      ->			List[str]:
       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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
       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 		lowercase						(			unittest.TestCase):






 """simple docstring"""


 def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
       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_					)









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 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
							self.builder.download_and_prepare(
							    download_mode=__UpperCAmelCase						,  file_format=self._file_format						,  )
							return self.builder.as_dataset(split=self.split					)




