





"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
       import torch

       from transformers.generation import DisjunctiveConstraint







@require_torch
class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= [[1, 2, 4], [1, 2, 3, 4]]
  lowerCAmelCase    				= DisjunctiveConstraint(_snake_case     )
  self.assertTrue(isinstance(dc.token_ids				,       _snake_case     )     )

  with self.assertRaises(_snake_case     ):
   DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]     )     )

  with self.assertRaises(_snake_case     ):
   DisjunctiveConstraint([torch.LongTensor([1, 2, 4]     ), torch.LongTensor([1, 2, 3, 4, 5]     )]     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= [[1, 2], [1, 2, 3, 4]]

  with self.assertRaises(_snake_case     ):
   DisjunctiveConstraint(_snake_case     )  # fails here



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= [[1, 2, 3], [1, 2, 4]]

  lowerCAmelCase    				= DisjunctiveConstraint(_snake_case     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(1     )
  lowerCAmelCase    				= stepped is True and completed is False and reset is False
  self.assertTrue(_snake_case     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.current_seq == [1]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(2     )
  lowerCAmelCase    				= stepped is True and completed is False and reset is False
  self.assertTrue(_snake_case     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.current_seq == [1, 2]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(3     )
  lowerCAmelCase    				= stepped is True and completed is True and reset is False
  self.assertTrue(_snake_case     )
  self.assertTrue(dc.completed     )  # Completed!
  self.assertTrue(dc.current_seq == [1, 2, 3]     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

  lowerCAmelCase    				= DisjunctiveConstraint(_snake_case     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(1     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.current_seq == [1]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(2     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.current_seq == [1, 2]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(4     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.current_seq == [1, 2, 4]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(5     )
  self.assertTrue(dc.completed     )  # Completed!
  self.assertTrue(dc.current_seq == [1, 2, 4, 5]     )

  dc.reset()

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(1     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.remaining() == 3     )
  self.assertTrue(dc.current_seq == [1]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(2     )
  self.assertTrue(not dc.completed     )
  self.assertTrue(dc.remaining() == 2     )
  self.assertTrue(dc.current_seq == [1, 2]     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= dc.update(5     )
  self.assertTrue(dc.completed     )  # Completed!
  self.assertTrue(dc.remaining() == 0     )
  self.assertTrue(dc.current_seq == [1, 2, 5]     )






'''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, KandinskyImgaImgPipeline, 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 							a__(   lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			KandinskyImgaImgPipeline
			UpperCAmelCase_     :	Union[str, Any]						=			['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
			UpperCAmelCase_     :	List[str]						=			[
			    '''prompt''',
			    '''negative_prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			UpperCAmelCase_     :	Dict						=			[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCAmelCase_     :	str						=			False



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 32



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return 100



			@property
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
					return tokenizer



			@property
			def 				a_			(  self):




					"""simple docstring"""


					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=1005			,       )

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

					return text_encoder



			@property
			def 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase     =    {
					    """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,
					}

					lowerCAmelCase     =    UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 				a_			(  self):




					"""simple docstring"""


					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 				a_			(  self):




					"""simple docstring"""


					torch.manual_seed(0)
					lowerCAmelCase     =    VQModel(**self.dummy_movq_kwargs)
					return model



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.dummy_text_encoder
					lowerCAmelCase     =    self.dummy_tokenizer
					lowerCAmelCase     =    self.dummy_unet
					lowerCAmelCase     =    self.dummy_movq

					lowerCAmelCase     =    {
					    """num_train_timesteps""": 1000,
					    """beta_schedule""": """linear""",
					    """beta_start""": 0.00085,
					    """beta_end""": 0.012,
					    """clip_sample""": False,
					    """set_alpha_to_one""": False,
					    """steps_offset""": 0,
					    """prediction_type""": """epsilon""",
					    """thresholding""": False,
					}

					lowerCAmelCase     =    DDIMScheduler(**__lowerCAmelCase)

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

					return components



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=0):




					"""simple docstring"""


					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((256, 256))

					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,
					    """image_embeds""": image_embeds,
					    """negative_image_embeds""": negative_image_embeds,
					    """generator""": generator,
					    """height""": 64,
					    """width""": 64,
					    """num_inference_steps""": 10,
					    """guidance_scale""": 7.0,
					    """strength""": 0.2,
					    """output_type""": """np""",
					}
					return inputs







			def 				a_			(  self):




					"""simple docstring"""


					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]

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

					lowerCAmelCase     =    np.array(
					    [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233])
					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 				a_			(  self):




					"""simple docstring"""


					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinsky/kandinsky_img2img_frog.npy""")

					lowerCAmelCase     =    load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
					lowerCAmelCase     =    """A red cartoon frog, 4k"""

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

					lowerCAmelCase     =    KandinskyImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-1"""			,       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			,       image_embeds=__lowerCAmelCase			,       negative_image_embeds=__lowerCAmelCase			,       generator=__lowerCAmelCase			,       num_inference_steps=100			,       height=768			,       width=768			,       strength=0.2			,       output_type="""np"""			,       )

					lowerCAmelCase     =    output.images[0]

					assert image.shape == (768, 768, 3)

					assert_mean_pixel_difference(__lowerCAmelCase			,       __lowerCAmelCase)




