




"""simple docstring"""

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      A__   (   lowercase_  ,						unittest.TestCase  ):
	A_				:					List[str]      =   KandinskyPipeline
	A_				:					List[str]      =   [
	    """prompt""",
	    """image_embeds""",
	    """negative_image_embeds""",
	]
	A_				:					List[str]      =   ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
	A_				:					str      =   [
	    """generator""",
	    """height""",
	    """width""",
	    """latents""",
	    """guidance_scale""",
	    """negative_prompt""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	A_				:					Optional[Any]      =   False




	@property
	def 				a_		( self	:						Union[str, Any]						)       ->			Tuple:
						return 32




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




	@property
	def 				a_		( self	:						List[str]						)       ->			Dict:
						return self.time_input_dim




	@property
	def 				a_		( self	:						Dict						)       ->			str:
						return self.time_input_dim * 4




	@property
	def 				a_		( self	:						Any						)       ->			str:
						return 100




	@property
	def 				a_		( self	:						Dict						)       ->			Optional[Any]:
						__snake_case        =						XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
						return tokenizer




	@property
	def 				a_		( self	:						Union[str, Any]						)       ->			Optional[int]:
						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=1_005   ,     )

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

						return text_encoder




	@property
	def 				a_		( self	:						List[str]						)       ->			Union[str, Any]:
						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(**lowercase						)
						return model




	@property
	def 				a_		( self	:						Union[str, Any]						)       ->			Optional[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 				a_		( self	:						Any						)       ->			Any:
						torch.manual_seed(0						)
						__snake_case        =						VQModel(**self.dummy_movq_kwargs						)
						return model




	def 				a_		( self	:						int						)       ->			Optional[int]:
						__snake_case        =						self.dummy_text_encoder
						__snake_case        =						self.dummy_tokenizer
						__snake_case        =						self.dummy_unet
						__snake_case        =						self.dummy_movq

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

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




	def 				a_		( self	:						List[str]   ,     lowercase	:						Tuple   ,     lowercase	:						Optional[Any]=0						)       ->			List[Any]:
						__snake_case        =						floats_tensor((1, self.cross_attention_dim)   ,     rng=random.Random(lowercase						)						).to(lowercase						)
						__snake_case        =						floats_tensor((1, self.cross_attention_dim)   ,     rng=random.Random(seed + 1						)						).to(lowercase						)
						if str(lowercase						).startswith("""mps"""						):
											__snake_case        =						torch.manual_seed(lowercase						)
						else:
											__snake_case        =						torch.Generator(device=lowercase						).manual_seed(lowercase						)
						__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 				a_		( self	:						Optional[int]						)       ->			Optional[int]:
						__snake_case        =						"""cpu"""

						__snake_case        =						self.get_dummy_components()

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

						pipe.set_progress_bar_config(disable=lowercase						)

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

						__snake_case        =						pipe(
						    **self.get_dummy_inputs(lowercase						)   ,     return_dict=lowercase   ,     )[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(
						    [0.32_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      A__   (   unittest.TestCase  ):




	def 				a_		( self	:						Dict						)       ->			List[Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()




	def 				a_		( self	:						Any						)       ->			int:
						__snake_case        =						load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""						)

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

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

						__snake_case        =						"""red cat, 4k photo"""

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

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

						__snake_case        =						output.images[0]

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

						assert_mean_pixel_difference(lowercase   ,     lowercase						)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  =			logging.get_logger(__name__)

UpperCAmelCase__  =			{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}







class      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	'''vilt'''



			def __init__(		self  :    List[Any]					,						A  :    Dict=3_05_22					,						A  :    Union[str, Any]=2					,						A  :    Tuple=2					,						A  :    List[Any]=40					,						A  :    Optional[int]=7_68					,						A  :    Any=12					,						A  :    Optional[int]=12					,						A  :    Optional[Any]=30_72					,						A  :    Optional[Any]="gelu"					,						A  :    str=0.0					,						A  :    Any=0.0					,						A  :    Union[str, Any]=0.0_2					,						A  :    Union[str, Any]=1E-12					,						A  :    Optional[Any]=3_84					,						A  :    Dict=32					,						A  :    int=3					,						A  :    int=True					,						A  :    Union[str, Any]=-1					,						A  :    int=False					,						A  :    int=-1					,						**A  :    Tuple					,						)  ->       Dict:



						"""simple docstring"""






						super().__init__(tie_word_embeddings=A					,						**A)

						_UpperCAmelCase									=  vocab_size
						_UpperCAmelCase									=  type_vocab_size
						_UpperCAmelCase									=  modality_type_vocab_size
						_UpperCAmelCase									=  max_position_embeddings

						_UpperCAmelCase									=  hidden_size
						_UpperCAmelCase									=  num_hidden_layers
						_UpperCAmelCase									=  num_attention_heads
						_UpperCAmelCase									=  intermediate_size
						_UpperCAmelCase									=  hidden_act
						_UpperCAmelCase									=  hidden_dropout_prob
						_UpperCAmelCase									=  attention_probs_dropout_prob
						_UpperCAmelCase									=  initializer_range
						_UpperCAmelCase									=  layer_norm_eps

						_UpperCAmelCase									=  image_size
						_UpperCAmelCase									=  patch_size
						_UpperCAmelCase									=  num_channels
						_UpperCAmelCase									=  qkv_bias
						_UpperCAmelCase									=  max_image_length
						_UpperCAmelCase									=  num_images
