



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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 	__snake_case   ( SCREAMING_SNAKE_CASE							, unittest.TestCase ):
		SCREAMING_SNAKE_CASE__								=	KandinskyVaaControlnetPipeline
		SCREAMING_SNAKE_CASE__								=	['image_embeds', 'negative_image_embeds', 'hint']
		SCREAMING_SNAKE_CASE__								=	['image_embeds', 'negative_image_embeds', 'hint']
		SCREAMING_SNAKE_CASE__								=	[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		SCREAMING_SNAKE_CASE__								=	False
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 32
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 32
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.time_input_dim
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.time_input_dim * 4
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return 100
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						torch.manual_seed(0						)

						lowerCAmelCase__		  =  {
						    'in_channels': 8,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 8,
						    'addition_embed_type': 'image_hint',
						    '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,
						}

						lowerCAmelCase__		  =  UNetaDConditionModel(**a_						)
						return model
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return {
						    "block_out_channels": [32, 32, 64, 64],
						    "down_block_types": [
						        "DownEncoderBlock2D",
						        "DownEncoderBlock2D",
						        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "vq_embed_dim": 4,
						}
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						torch.manual_seed(0						)
						lowerCAmelCase__		  =  VQModel(**self.dummy_movq_kwargs						)
						return model
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.dummy_unet
						lowerCAmelCase__		  =  self.dummy_movq

						lowerCAmelCase__		  =  DDIMScheduler(
						    num_train_timesteps=1000    ,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_    ,)

						lowerCAmelCase__		  =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'movq': movq,
						}
						return components
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_=0						):







						"""simple docstring"""
						lowerCAmelCase__		  =  floats_tensor((1, self.text_embedder_hidden_size)    ,rng=random.Random(a_						)						).to(a_						)
						lowerCAmelCase__		  =  floats_tensor((1, self.text_embedder_hidden_size)    ,rng=random.Random(seed + 1						)						).to(
						    a_						)

						# create hint
						lowerCAmelCase__		  =  floats_tensor((1, 3, 64, 64)    ,rng=random.Random(a_						)						).to(a_						)

						if str(a_						).startswith('mps'						):
										lowerCAmelCase__		  =  torch.manual_seed(a_						)
						else:
										lowerCAmelCase__		  =  torch.Generator(device=a_						).manual_seed(a_						)
						lowerCAmelCase__		  =  {
						    'image_embeds': image_embeds,
						    'negative_image_embeds': negative_image_embeds,
						    'hint': hint,
						    'generator': generator,
						    'height': 64,
						    'width': 64,
						    'guidance_scale': 4.0,
						    'num_inference_steps': 2,
						    'output_type': 'np',
						}
						return inputs



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  'cpu'

						lowerCAmelCase__		  =  self.get_dummy_components()

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

						pipe.set_progress_bar_config(disable=a_						)

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

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

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

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

						lowerCAmelCase__		  =  np.array(
						    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595]						)

						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 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'						)

						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/kandinskyv22/hint_image_cat.png'						)
						lowerCAmelCase__		  =  torch.from_numpy(np.array(a_						)						).float() / 255.0
						lowerCAmelCase__		  =  hint.permute(2    ,0    ,1						).unsqueeze(0						)

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

						lowerCAmelCase__		  =  KandinskyVaaControlnetPipeline.from_pretrained(
						    'kandinsky-community/kandinsky-2-2-controlnet-depth'    ,torch_dtype=torch.floataa						)
						lowerCAmelCase__		  =  pipeline.to(a_						)
						pipeline.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  'A robot, 4k photo'

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

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

						lowerCAmelCase__		  =  output.images[0]

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

						assert_mean_pixel_difference(a_    ,a_						)





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor


@require_tokenizers
@require_vision
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  tempfile.mkdtemp()

						# fmt: off
						lowerCAmelCase__		  =  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
						# fmt: on
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']						)
						with open(self.vocab_file    ,'w'    ,encoding='utf-8'						) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

						lowerCAmelCase__		  =  {
						    'do_resize': True,
						    'size': {'height': 18, 'width': 18},
						    'do_normalize': True,
						    'image_mean': [0.5, 0.5, 0.5],
						    'image_std': [0.5, 0.5, 0.5],
						}
						lowerCAmelCase__		  =  os.path.join(self.tmpdirname    ,a_						)
						with open(self.image_processor_file    ,'w'    ,encoding='utf-8'						) as fp:
										json.dump(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return BertTokenizer.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,**a_						):







						"""simple docstring"""
						return ViTImageProcessor.from_pretrained(self.tmpdirname    ,**a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						shutil.rmtree(self.tmpdirname						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [np.random.randint(255    ,size=(3, 30, 400)    ,dtype=np.uinta						)]

						lowerCAmelCase__		  =  [Image.fromarray(np.moveaxis(a_    ,0    ,-1						)						) for x in image_inputs]

						return image_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_tokenizer()
						lowerCAmelCase__		  =  self.get_image_processor()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						processor.save_pretrained(self.tmpdirname						)
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()    ,image_processor=self.get_image_processor()						)
						processor.save_pretrained(self.tmpdirname						)

						lowerCAmelCase__		  =  self.get_tokenizer(bos_token='(BOS)'    ,eos_token='(EOS)'						)
						lowerCAmelCase__		  =  self.get_image_processor(do_normalize=a_    ,padding_value=1.0						)

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname    ,bos_token='(BOS)'    ,eos_token='(EOS)'    ,do_normalize=a_    ,padding_value=1.0						)

						self.assertEqual(processor.tokenizer.get_vocab()    ,tokenizer_add_kwargs.get_vocab()						)
						self.assertIsInstance(processor.tokenizer    ,(BertTokenizer, BertTokenizerFast)						)

						self.assertEqual(processor.image_processor.to_json_string()    ,image_processor_add_kwargs.to_json_string()						)
						self.assertIsInstance(processor.image_processor    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  image_processor(a_    ,return_tensors='np'						)
						lowerCAmelCase__		  =  processor(images=a_    ,return_tensors='np'						)

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







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'

						lowerCAmelCase__		  =  processor(text=a_						)

						lowerCAmelCase__		  =  tokenizer(a_						)

						for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]    ,encoded_processor[key]						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

						self.assertListEqual(list(inputs.keys()						)    ,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

						# test if it raises when no input is passed
						with self.assertRaises(a_						):
										processor()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						lowerCAmelCase__		  =  processor.batch_decode(a_						)
						lowerCAmelCase__		  =  tokenizer.batch_decode(a_						)

						self.assertListEqual(a_    ,a_						)



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.get_image_processor()
						lowerCAmelCase__		  =  self.get_tokenizer()

						lowerCAmelCase__		  =  VisionTextDualEncoderProcessor(tokenizer=a_    ,image_processor=a_						)

						lowerCAmelCase__		  =  'lower newer'
						lowerCAmelCase__		  =  self.prepare_image_inputs()

						lowerCAmelCase__		  =  processor(text=a_    ,images=a_						)

						self.assertListEqual(list(inputs.keys()						)    ,processor.model_input_names						)
