






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						(SCREAMING_SNAKE_CASE__			,unittest.TestCase       ):
			__snake_case							: int							=				KandinskyPipeline
			__snake_case							: int							=				[
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			__snake_case							: Tuple							=				["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			__snake_case							: Tuple							=				[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			__snake_case							: str							=				False







			@property
			def 				UpperCamelCase (      self:    List[Any]	):







									'''simple docstring'''




									return 32







			@property
			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									return 32







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







									'''simple docstring'''




									return self.time_input_dim







			@property
			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									return self.time_input_dim * 4







			@property
			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									return 100







			@property
			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




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







			@property
			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									torch.manual_seed(0	)
									_SCREAMING_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=1_005 ,      )

									_SCREAMING_SNAKE_CASE       =    MultilingualCLIP(lowerCAmelCase__	)
									_SCREAMING_SNAKE_CASE       =    text_encoder.eval()

									return text_encoder







			@property
			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									torch.manual_seed(0	)

									_SCREAMING_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,
									}

									_SCREAMING_SNAKE_CASE       =    UNetaDConditionModel(**lowerCAmelCase__	)
									return model







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







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.dummy_text_encoder
									_SCREAMING_SNAKE_CASE       =    self.dummy_tokenizer
									_SCREAMING_SNAKE_CASE       =    self.dummy_unet
									_SCREAMING_SNAKE_CASE       =    self.dummy_movq

									_SCREAMING_SNAKE_CASE       =    DDIMScheduler(
									    num_train_timesteps=1_000 ,      beta_schedule="""linear""" ,      beta_start=0.0_00_85 ,      beta_end=0.0_12 ,      clip_sample=lowerCAmelCase__ ,      set_alpha_to_one=lowerCAmelCase__ ,      steps_offset=1 ,      prediction_type="""epsilon""" ,      thresholding=lowerCAmelCase__ ,      )

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







			def 				UpperCamelCase (      self:    Optional[int] ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    List[Any]=0	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    floats_tensor((1, self.cross_attention_dim) ,      rng=random.Random(lowerCAmelCase__	)	).to(lowerCAmelCase__	)
									_SCREAMING_SNAKE_CASE       =    floats_tensor((1, self.cross_attention_dim) ,      rng=random.Random(seed + 1	)	).to(lowerCAmelCase__	)
									if str(lowerCAmelCase__	).startswith("""mps"""	):
															_SCREAMING_SNAKE_CASE       =    torch.manual_seed(lowerCAmelCase__	)
									else:
															_SCREAMING_SNAKE_CASE       =    torch.Generator(device=lowerCAmelCase__	).manual_seed(lowerCAmelCase__	)
									_SCREAMING_SNAKE_CASE       =    {
									    "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 				UpperCamelCase (      self:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    "cpu"

									_SCREAMING_SNAKE_CASE       =    self.get_dummy_components()

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

									pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

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

									_SCREAMING_SNAKE_CASE       =    pipe(
									    **self.get_dummy_inputs(lowerCAmelCase__	) ,      return_dict=lowerCAmelCase__ ,      )[0]

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

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

									_SCREAMING_SNAKE_CASE       =    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 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




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

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

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

									_SCREAMING_SNAKE_CASE       =    "red cat, 4k photo"

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

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

									_SCREAMING_SNAKE_CASE       =    output.images[0]

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

									assert_mean_pixel_difference(lowerCAmelCase__ ,      lowerCAmelCase__	)







"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_SCREAMING_SNAKE_CASE						 =     logging.get_logger(__name__)







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 def __init__(self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  warnings.warn(
      "The class LayoutLMv3FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
      " Please use LayoutLMv3ImageProcessor instead."					,     lowerCAmelCase__					,     )
  super().__init__(*lowerCAmelCase__					,     **lowerCAmelCase__   )



