



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_  (		_snake_case					, unittest.TestCase				):
						UpperCamelCase__      :						Optional[int]				       =KandinskyPipeline
						UpperCamelCase__      :						Optional[Any]				       =[
						    "prompt",
						    "image_embeds",
						    "negative_image_embeds",
						]
						UpperCamelCase__      :						Dict				       =["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
						UpperCamelCase__      :						Union[str, Any]				       =[
						    "generator",
						    "height",
						    "width",
						    "latents",
						    "guidance_scale",
						    "negative_prompt",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						UpperCamelCase__      :						List[Any]				       =False

						@property
						def 	__a       (    self	:List[Any])		->					str:
												return 32

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

						@property
						def 	__a       (    self	:Optional[int])		->					Tuple:
												return self.time_input_dim

						@property
						def 	__a       (    self	:int)		->					Any:
												return self.time_input_dim * 4

						@property
						def 	__a       (    self	:Dict)		->					int:
												return 100

						@property
						def 	__a       (    self	:Tuple)		->					Optional[int]:
												UpperCAmelCase_     							=     XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''')
												return tokenizer

						@property
						def 	__a       (    self	:str)		->					List[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=1005   ,						)

												UpperCAmelCase_     							=     MultilingualCLIP(_lowercase)
												UpperCAmelCase_     							=     text_encoder.eval()

												return text_encoder

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

						def 	__a       (    self	:List[str])		->					str:
												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=0.00_085   ,						beta_end=0.012   ,						clip_sample=_lowercase   ,						set_alpha_to_one=_lowercase   ,						steps_offset=1   ,						prediction_type='''epsilon'''   ,						thresholding=_lowercase   ,						)

												UpperCAmelCase_     							=     {
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}
												return components

						def 	__a       (    self	:Optional[int]   ,						_lowercase	:Optional[int]   ,						_lowercase	:Optional[int]=0)		->					Optional[Any]:
												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)
												if str(_lowercase).startswith('''mps'''):
																		UpperCAmelCase_     							=     torch.manual_seed(_lowercase)
												else:
																		UpperCAmelCase_     							=     torch.Generator(device=_lowercase).manual_seed(_lowercase)
												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 	__a       (    self	:Tuple)		->					Optional[Any]:
												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(
												    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953])

												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	:Tuple)		->					Dict:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def 	__a       (    self	:Optional[Any])		->					Tuple:
												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(_lowercase)

												UpperCAmelCase_     							=     KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''   ,						torch_dtype=torch.floataa)
												UpperCAmelCase_     							=     pipeline.to(_lowercase)
												pipeline.set_progress_bar_config(disable=_lowercase)

												UpperCAmelCase_     							=     '''red cat, 4k photo'''

												UpperCAmelCase_     							=     torch.Generator(device='''cuda''').manual_seed(0)
												UpperCAmelCase_							,      UpperCAmelCase_     							=     pipe_prior(
												    _lowercase   ,						generator=_lowercase   ,						num_inference_steps=5   ,						negative_prompt=''''''   ,						).to_tuple()

												UpperCAmelCase_     							=     torch.Generator(device='''cuda''').manual_seed(0)
												UpperCAmelCase_     							=     pipeline(
												    _lowercase   ,						image_embeds=_lowercase   ,						negative_image_embeds=_lowercase   ,						generator=_lowercase   ,						num_inference_steps=100   ,						output_type='''np'''   ,						)

												UpperCAmelCase_     							=     output.images[0]

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

												assert_mean_pixel_difference(_lowercase   ,						_lowercase)












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["image_processor", "tokenizer"]
							lowerCamelCase_							=    "AutoImageProcessor"
							lowerCamelCase_							=    "AutoTokenizer"


							def __init__(							self			:Any							, __A			:List[str]=None							, __A			:Dict=None							, **__A			:List[str]						)      ->      List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""							, __A							, )
											SCREAMING_SNAKE_CASE__								=					kwargs.pop("""feature_extractor"""						)

									SCREAMING_SNAKE_CASE__								=					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""						)
									if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""						)

									super().__init__(__A							, __A						)
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


							def __call__(							self			:List[str]							, *__A			:int							, **__A			:Optional[int]						)      ->      Optional[Any]:



									"""simple docstring"""



									if self._in_target_context_manager:
											return self.current_processor(*__A							, **__A						)

									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""images"""							, __A						)
									SCREAMING_SNAKE_CASE__								=					kwargs.pop("""text"""							, __A						)
									if len(__A						) > 0:
											SCREAMING_SNAKE_CASE__								=					args[0]
											SCREAMING_SNAKE_CASE__								=					args[1:]

									if images is None and text is None:
											raise ValueError("""You need to specify either an `images` or `text` input to process."""						)

									if images is not None:
											SCREAMING_SNAKE_CASE__								=					self.image_processor(__A							, *__A							, **__A						)
									if text is not None:
											SCREAMING_SNAKE_CASE__								=					self.tokenizer(__A							, **__A						)

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											SCREAMING_SNAKE_CASE__								=					encodings["""input_ids"""]
											return inputs


							def    _snake_case     (							self			:Any							, *__A			:Optional[Any]							, **__A			:int						)      ->      List[Any]:



									"""simple docstring"""



									return self.tokenizer.batch_decode(*__A							, **__A						)


							def    _snake_case     (							self			:str							, *__A			:Optional[int]							, **__A			:List[Any]						)      ->      List[str]:



									"""simple docstring"""



									return self.tokenizer.decode(*__A							, **__A						)


							@contextmanager
							def    _snake_case     (							self			:Dict						)      ->      List[str]:



									"""simple docstring"""



									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your images inputs, or in a separate call."""						)
									SCREAMING_SNAKE_CASE__								=					True
									SCREAMING_SNAKE_CASE__								=					self.tokenizer
									yield
									SCREAMING_SNAKE_CASE__								=					self.image_processor
									SCREAMING_SNAKE_CASE__								=					False


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



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							, __A							, )
									return self.image_processor_class


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



									"""simple docstring"""



									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							, __A							, )
									return self.image_processor