
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 							lowercase						(   __lowerCamelCase			,    unittest.TestCase						):
							lowercase_			:			Dict			   =KandinskyPipeline
							lowercase_			:			int			   =[
							    '''prompt''',
							    '''image_embeds''',
							    '''negative_image_embeds''',
							]
							lowercase_			:			Union[str, Any]			   =['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
							lowercase_			:			Any			   =[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''negative_prompt''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_			:			List[str]			   =False




							@property
							def 					A__ (							self):
												return 3_2




							@property
							def 					A__ (							self):
												return 3_2




							@property
							def 					A__ (							self):
												return self.time_input_dim




							@property
							def 					A__ (							self):
												return self.time_input_dim * 4




							@property
							def 					A__ (							self):
												return 1_0_0




							@property
							def 					A__ (							self):
												lowercase		      =		XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''')
												return tokenizer




							@property
							def 					A__ (							self):
												torch.manual_seed(0)
												lowercase		      =		MCLIPConfig(
												    numDims=self.cross_attention_dim  ,transformerDimensions=self.text_embedder_hidden_size  ,hidden_size=self.text_embedder_hidden_size  ,intermediate_size=3_7  ,num_attention_heads=4  ,num_hidden_layers=5  ,vocab_size=1_0_0_5  ,)

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

												return text_encoder




							@property
							def 					A__ (							self):
												torch.manual_seed(0)

												lowercase		      =		{
												    '''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,
												}

												lowercase		      =		UNetaDConditionModel(**__lowerCAmelCase)
												return model




							@property
							def 					A__ (							self):
												return {
												    "block_out_channels": [3_2, 6_4],
												    "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": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}




							@property
							def 					A__ (							self):
												torch.manual_seed(0)
												lowercase		      =		VQModel(**self.dummy_movq_kwargs)
												return model




							def 					A__ (							self):
												lowercase		      =		self.dummy_text_encoder
												lowercase		      =		self.dummy_tokenizer
												lowercase		      =		self.dummy_unet
												lowercase		      =		self.dummy_movq

												lowercase		      =		DDIMScheduler(
												    num_train_timesteps=1_0_0_0  ,beta_schedule='''linear'''  ,beta_start=0.00085  ,beta_end=0.012  ,clip_sample=__lowerCAmelCase  ,set_alpha_to_one=__lowerCAmelCase  ,steps_offset=1  ,prediction_type='''epsilon'''  ,thresholding=__lowerCAmelCase  ,)

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




							def 					A__ (							self  ,A__  ,A__=0):
												lowercase		      =		floats_tensor((1, self.cross_attention_dim)  ,rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
												lowercase		      =		floats_tensor((1, self.cross_attention_dim)  ,rng=random.Random(seed + 1)).to(__lowerCAmelCase)
												if str(__lowerCAmelCase).startswith('''mps'''):
																	lowercase		      =		torch.manual_seed(__lowerCAmelCase)
												else:
																	lowercase		      =		torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
												lowercase		      =		{
												    '''prompt''': '''horse''',
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs





							def 					A__ (							self):
												lowercase		      =		'''cpu'''

												lowercase		      =		self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=__lowerCAmelCase)

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

												lowercase		      =		pipe(
												    **self.get_dummy_inputs(__lowerCAmelCase)  ,return_dict=__lowerCAmelCase  ,)[0]

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

												assert image.shape == (1, 6_4, 6_4, 3)

												lowercase		      =		np.array(
												    [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953])

												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						):




							def 					A__ (							self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





							def 					A__ (							self):
												lowercase		      =		load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''')

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

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

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

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

												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)
												lowercase		      =		pipeline(
												    __lowerCAmelCase  ,image_embeds=__lowerCAmelCase  ,negative_image_embeds=__lowerCAmelCase  ,generator=__lowerCAmelCase  ,num_inference_steps=1_0_0  ,output_type='''np'''  ,)

												lowercase		      =		output.images[0]

												assert image.shape == (5_1_2, 5_1_2, 3)

												assert_mean_pixel_difference(__lowerCAmelCase  ,__lowerCAmelCase)





'''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 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =KandinskyPipeline
   lowerCamelCase_            =[
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   lowerCamelCase_            =['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   lowerCamelCase_            =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase_            =False


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


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return 32


   @property
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Any:
         return self.time_input_dim


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return self.time_input_dim * 4


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return 100


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


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         torch.manual_seed(0)
         lowercase_     			=   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 ,		)

         lowercase_     			=   MultilingualCLIP(__lowerCAmelCase)
         lowercase_     			=   text_encoder.eval()

         return text_encoder


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       Any:
         torch.manual_seed(0)

         lowercase_     			=   {
             "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,
         }

         lowercase_     			=   UNetaDConditionModel(**__lowerCAmelCase)
         return model


   @property
   def __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         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					:						Optional[Any])   ->       Tuple:
         torch.manual_seed(0)
         lowercase_     			=   VQModel(**self.dummy_movq_kwargs)
         return model


   def __UpperCAmelCase							( self					:						Tuple)   ->       Union[str, Any]:
         lowercase_     			=   self.dummy_text_encoder
         lowercase_     			=   self.dummy_tokenizer
         lowercase_     			=   self.dummy_unet
         lowercase_     			=   self.dummy_movq

         lowercase_     			=   DDIMScheduler(
             num_train_timesteps=1000 ,		beta_schedule="linear" ,		beta_start=0.0_0085 ,		beta_end=0.012 ,		clip_sample=__lowerCAmelCase ,		set_alpha_to_one=__lowerCAmelCase ,		steps_offset=1 ,		prediction_type="epsilon" ,		thresholding=__lowerCAmelCase ,		)

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


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						List[str]=0)   ->       List[Any]:
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(seed + 1)).to(__lowerCAmelCase)
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     			=   torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     			=   torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
         lowercase_     			=   {
             "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					:						Any)   ->       Optional[int]:
         lowercase_     			=   "cpu"

         lowercase_     			=   self.get_dummy_components()

         lowercase_     			=   self.pipeline_class(**__lowerCAmelCase)
         lowercase_     			=   pipe.to(__lowerCAmelCase)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase))
         lowercase_     			=   output.images

         lowercase_     			=   pipe(
             **self.get_dummy_inputs(__lowerCAmelCase) ,		return_dict=__lowerCAmelCase ,		)[0]

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

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

         lowercase_     			=   np.array(
             [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953])

         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			):


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


   def __UpperCAmelCase							( self					:						List[str])   ->       Tuple:
         lowercase_     			=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy")

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

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

         lowercase_     			=   "red cat, 4k photo"

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

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

         lowercase_     			=   output.images[0]

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

         assert_mean_pixel_difference(__lowerCAmelCase ,		__lowerCAmelCase)






