






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					KandinskyInpaintPipeline
		_UpperCamelCase				  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
		_UpperCamelCase				  =					[
		    'prompt',
		    'negative_prompt',
		    'image_embeds',
		    'negative_image_embeds',
		    'image',
		    'mask_image',
		]
		_UpperCamelCase				  =					[
		    'generator',
		    'height',
		    'width',
		    'latents',
		    'guidance_scale',
		    'negative_prompt',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		_UpperCamelCase				  =					False






		@property
		def 			UpperCamelCase_					(      self       ):
								return 32






		@property
		def 			UpperCamelCase_					(      self       ):
								return 32






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






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






		@property
		def 			UpperCamelCase_					(      self       ):
								return 1_00






		@property
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""       )
								return tokenizer






		@property
		def 			UpperCamelCase_					(      self       ):
								torch.manual_seed(0       )
								lowerCamelCase__         =  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=10_05   ,)

								lowerCamelCase__         =  MultilingualCLIP(_lowerCAmelCase       )
								lowerCamelCase__         =  text_encoder.eval()

								return text_encoder






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

								lowerCamelCase__         =  {
								    """in_channels""": 9,
								    # 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,
								}

								lowerCamelCase__         =  UNetaDConditionModel(**_lowerCAmelCase       )
								return model






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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.dummy_text_encoder
								lowerCamelCase__         =  self.dummy_tokenizer
								lowerCamelCase__         =  self.dummy_unet
								lowerCamelCase__         =  self.dummy_movq

								lowerCamelCase__         =  DDIMScheduler(
								    num_train_timesteps=10_00   ,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   ,)

								lowerCamelCase__         =  {
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=0       ):
								lowerCamelCase__         =  floats_tensor((1, self.cross_attention_dim)   ,rng=random.Random(_lowerCAmelCase       )       ).to(_lowerCAmelCase       )
								lowerCamelCase__         =  floats_tensor((1, self.cross_attention_dim)   ,rng=random.Random(seed + 1       )       ).to(_lowerCAmelCase       )
								# create init_image
								lowerCamelCase__         =  floats_tensor((1, 3, 64, 64)   ,rng=random.Random(_lowerCAmelCase       )       ).to(_lowerCAmelCase       )
								lowerCamelCase__         =  image.cpu().permute(0   ,2   ,3   ,1       )[0]
								lowerCamelCase__         =  Image.fromarray(np.uinta(_lowerCAmelCase       )       ).convert("""RGB"""       ).resize((2_56, 2_56)       )
								# create mask
								lowerCamelCase__         =  np.ones((64, 64)   ,dtype=np.floataa       )
								lowerCamelCase__         =  0

								if str(_lowerCAmelCase       ).startswith("""mps"""       ):
														lowerCamelCase__         =  torch.manual_seed(_lowerCAmelCase       )
								else:
														lowerCamelCase__         =  torch.Generator(device=_lowerCAmelCase       ).manual_seed(_lowerCAmelCase       )
								lowerCamelCase__         =  {
								    """prompt""": """horse""",
								    """image""": init_image,
								    """mask_image""": mask,
								    """image_embeds""": image_embeds,
								    """negative_image_embeds""": negative_image_embeds,
								    """generator""": generator,
								    """height""": 64,
								    """width""": 64,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 4.0,
								    """output_type""": """np""",
								}
								return inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """cpu"""

								lowerCamelCase__         =  self.get_dummy_components()

								lowerCamelCase__         =  self.pipeline_class(**_lowerCAmelCase       )
								lowerCamelCase__         =  pipe.to(_lowerCAmelCase       )

								pipe.set_progress_bar_config(disable=_lowerCAmelCase       )

								lowerCamelCase__         =  pipe(**self.get_dummy_inputs(_lowerCAmelCase       )       )
								lowerCamelCase__         =  output.images

								lowerCamelCase__         =  pipe(
								    **self.get_dummy_inputs(_lowerCAmelCase       )   ,return_dict=_lowerCAmelCase   ,)[0]

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

								print(F'''image.shape {image.shape}'''       )

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

								lowerCamelCase__         =  np.array(
								    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]       )

								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()}'''






		def 			UpperCamelCase_					(      self       ):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3       )




@slow
@require_torch_gpu
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""       )

								lowerCamelCase__         =  load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""       )
								lowerCamelCase__         =  np.ones((7_68, 7_68)   ,dtype=np.floataa       )
								lowerCamelCase__         =  0

								lowerCamelCase__         =  """a hat"""

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

								lowerCamelCase__         =  KandinskyInpaintPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1-inpaint"""   ,torch_dtype=torch.floataa       )
								lowerCamelCase__         =  pipeline.to(_lowerCAmelCase       )
								pipeline.set_progress_bar_config(disable=_lowerCAmelCase       )

								lowerCamelCase__         =  torch.Generator(device="""cpu"""       ).manual_seed(0       )
								lowerCamelCase__       ,			lowerCamelCase__         =  pipe_prior(
								    _lowerCAmelCase   ,generator=_lowerCAmelCase   ,num_inference_steps=5   ,negative_prompt=""""""   ,).to_tuple()

								lowerCamelCase__         =  pipeline(
								    _lowerCAmelCase   ,image=_lowerCAmelCase   ,mask_image=_lowerCAmelCase   ,image_embeds=_lowerCAmelCase   ,negative_image_embeds=_lowerCAmelCase   ,generator=_lowerCAmelCase   ,num_inference_steps=1_00   ,height=7_68   ,width=7_68   ,output_type="""np"""   ,)

								lowerCamelCase__         =  output.images[0]

								assert image.shape == (7_68, 7_68, 3)

								assert_mean_pixel_difference(_lowerCAmelCase   ,_lowerCAmelCase       )









'''simple docstring'''







import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  tempfile.mkdtemp()

								lowerCamelCase__         =  BlipImageProcessor()
								lowerCamelCase__         =  BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""       )

								lowerCamelCase__         =  BlipProcessor(_lowerCAmelCase   ,_lowerCAmelCase       )

								processor.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).tokenizer






		def 			UpperCamelCase_					(      self   ,**_lowerCAmelCase       ):
								return AutoProcessor.from_pretrained(self.tmpdirname   ,**_lowerCAmelCase       ).image_processor






		def 			UpperCamelCase_					(      self       ):
								shutil.rmtree(self.tmpdirname       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [np.random.randint(2_55   ,size=(3, 30, 4_00)   ,dtype=np.uinta       )]

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

								return image_inputs






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BlipProcessor(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=_lowerCAmelCase   ,padding_value=1.0       )

								lowerCamelCase__         =  BlipProcessor.from_pretrained(
								    self.tmpdirname   ,bos_token="""(BOS)"""   ,eos_token="""(EOS)"""   ,do_normalize=_lowerCAmelCase   ,padding_value=1.0       )

								self.assertEqual(processor.tokenizer.get_vocab()   ,tokenizer_add_kwargs.get_vocab()       )
								self.assertIsInstance(processor.tokenizer   ,_lowerCAmelCase       )

								self.assertEqual(processor.image_processor.to_json_string()   ,image_processor_add_kwargs.to_json_string()       )
								self.assertIsInstance(processor.image_processor   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  image_processor(_lowerCAmelCase   ,return_tensors="""np"""       )
								lowerCamelCase__         =  processor(images=_lowerCAmelCase   ,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 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""

								lowerCamelCase__         =  processor(text=_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer(_lowerCAmelCase   ,return_token_type_ids=_lowerCAmelCase       )

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

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

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






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

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

								lowerCamelCase__         =  processor.batch_decode(_lowerCAmelCase       )
								lowerCamelCase__         =  tokenizer.batch_decode(_lowerCAmelCase       )

								self.assertListEqual(_lowerCAmelCase   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.get_image_processor()
								lowerCamelCase__         =  self.get_tokenizer()

								lowerCamelCase__         =  BlipProcessor(tokenizer=_lowerCAmelCase   ,image_processor=_lowerCAmelCase       )

								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  self.prepare_image_inputs()

								lowerCamelCase__         =  processor(text=_lowerCAmelCase   ,images=_lowerCAmelCase       )

								# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
								self.assertListEqual(list(inputs.keys()       )   ,["""pixel_values""", """input_ids""", """attention_mask"""]       )

