


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
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 		__magic_name__						(snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       KandinskyVaaPipeline
     __lowercase				:							List[str]	   =       [
         'image_embeds',
         'negative_image_embeds',
     ]
     __lowercase				:							Any	   =       ['image_embeds', 'negative_image_embeds']
     __lowercase				:							Any	   =       [
         'generator',
         'height',
         'width',
         'latents',
         'guidance_scale',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     __lowercase				:							Optional[int]	   =       False

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       return 32

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return 32

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       return self.time_input_dim

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

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       return 1_00

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       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''': '''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''': '''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(**_a   )
       return model

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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:Optional[int]   ):
       torch.manual_seed(0   )
       snake_case__						= VQModel(**self.dummy_movq_kwargs   )
       return model

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.dummy_unet
       snake_case__						= self.dummy_movq

       snake_case__						= DDIMScheduler(
           num_train_timesteps=10_00     ,		beta_schedule='''linear'''     ,		beta_start=0.00085     ,		beta_end=0.012     ,		clip_sample=_a     ,		set_alpha_to_one=_a     ,		steps_offset=1     ,		prediction_type='''epsilon'''     ,		thresholding=_a     ,		)

       snake_case__						= {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }
       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:int     ,		_a:Union[str, Any]=0   ):
       snake_case__						= floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(_a   )   ).to(_a   )
       snake_case__						= floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(seed + 1   )   ).to(
           _a   )
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= '''cpu'''

       snake_case__						= self.get_dummy_components()

       snake_case__						= self.pipeline_class(**_a   )
       snake_case__						= pipe.to(_a   )

       pipe.set_progress_bar_config(disable=_a   )

       snake_case__						= pipe(**self.get_dummy_inputs(_a   )   )
       snake_case__						= output.images

       snake_case__						= pipe(
           **self.get_dummy_inputs(_a   )     ,		return_dict=_a     ,		)[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(
           [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]   )

       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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



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







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy'''   )

       snake_case__						= KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''     ,		torch_dtype=torch.floataa   )
       pipe_prior.to(_a   )

       snake_case__						= KandinskyVaaPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''     ,		torch_dtype=torch.floataa   )
       snake_case__						= pipeline.to(_a   )
       pipeline.set_progress_bar_config(disable=_a   )

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

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

       snake_case__						= torch.Generator(device='''cuda'''   ).manual_seed(0   )
       snake_case__						= pipeline(
           image_embeds=_a     ,		negative_image_embeds=_a     ,		generator=_a     ,		num_inference_steps=1_00     ,		output_type='''np'''     ,		)

       snake_case__						= output.images[0]

       assert image.shape == (5_12, 5_12, 3)

       assert_mean_pixel_difference(_a     ,		_a   )





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
   from transformers import TvltImageProcessor

if is_speech_available():
   from transformers import TvltFeatureExtractor

from transformers import TvltProcessor
@require_torch
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''ZinengTang/tvlt-base'''
       snake_case__						= tempfile.mkdtemp()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:List[Any]   ):
       return TvltImageProcessor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		**_a:Tuple   ):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )
       processor.save_pretrained(self.tmpdirname   )
       snake_case__						= TvltProcessor.from_pretrained(self.tmpdirname   )

       self.assertIsInstance(processor.feature_extractor     ,		_a   )
       self.assertIsInstance(processor.image_processor     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )

       snake_case__						= feature_extractor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(audio=_a     ,		return_tensors='''np'''   )

       for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= image_processor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(images=_a     ,		return_tensors='''np'''   )

       for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )
       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= processor(audio=_a     ,		images=_a   )

       self.assertListEqual(list(inputs.keys()   )     ,		['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']   )

       # test if it raises when no input is passed
       with pytest.raises(_a   ):
         processor()







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       self.assertListEqual(
           processor.model_input_names     ,		image_processor.model_input_names + feature_extractor.model_input_names     ,		msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,		)

