






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     _a							(	UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = KandinskyVaaControlnetPipeline
  A_						       = ["""image_embeds""", """negative_image_embeds""", """hint"""]
  A_						       = ["""image_embeds""", """negative_image_embeds""", """hint"""]
  A_						       = [
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  A_						       = False




  @property
  def  _UpperCAmelCase  (  self			)      ->			Tuple:
     return 32




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




  @property
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     return self.time_input_dim




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




  @property
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     return 100




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

     UpperCamelCase_						=  {
         '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,
     }

     UpperCamelCase_						=  UNetaDConditionModel(**a_			)
     return model




  @property
  def  _UpperCAmelCase  (  self			)      ->			str:
     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  _UpperCAmelCase  (  self			)      ->			int:
     torch.manual_seed(0			)
     UpperCamelCase_						=  VQModel(**self.dummy_movq_kwargs			)
     return model




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     UpperCamelCase_						=  self.dummy_unet
     UpperCamelCase_						=  self.dummy_movq

     UpperCamelCase_						=  DDIMScheduler(
         num_train_timesteps=1000		,		beta_schedule='linear'		,		beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		clip_sample=a_		,		set_alpha_to_one=a_		,		steps_offset=1		,		prediction_type='epsilon'		,		thresholding=a_		,		)

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




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=0			)      ->			str:
     UpperCamelCase_						=  floats_tensor((1, self.text_embedder_hidden_size)		,		rng=random.Random(a_			)			).to(a_			)
     UpperCamelCase_						=  floats_tensor((1, self.text_embedder_hidden_size)		,		rng=random.Random(seed + 1			)			).to(
         a_			)

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

     if str(a_			).startswith('mps'			):
        UpperCamelCase_						=  torch.manual_seed(a_			)
     else:
        UpperCamelCase_						=  torch.Generator(device=a_			).manual_seed(a_			)
     UpperCamelCase_						=  {
         '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  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  'cpu'

     UpperCamelCase_						=  self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=a_			)

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

     UpperCamelCase_						=  pipe(
         **self.get_dummy_inputs(a_			)		,		return_dict=a_		,		)[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(
         [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]			)

     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     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Dict:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'			)

     UpperCamelCase_						=  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/hint_image_cat.png'			)
     UpperCamelCase_						=  torch.from_numpy(np.array(a_			)			).float() / 2_5_5.0
     UpperCamelCase_						=  hint.permute(2		,		0		,		1			).unsqueeze(0			)

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

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

     UpperCamelCase_						=  'A robot, 4k photo'

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

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

     UpperCamelCase_						=  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						)
