import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   KandinskyPipeline
		A_    			=   [
		    '''prompt''',
		    '''image_embeds''',
		    '''negative_image_embeds''',
		]
		A_    			=   ['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
		A_    			=   [
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''latents''',
		    '''guidance_scale''',
		    '''negative_prompt''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		A_    			=   False



		@property
		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return 32



		@property
		def 					_a					(      self     :					str ):






					"""simple docstring"""
					return 32



		@property
		def 					_a					(      self     :					Any ):






					"""simple docstring"""
					return self.time_input_dim



		@property
		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return self.time_input_dim * 4



		@property
		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return 100



		@property
		def 					_a					(      self     :					Any ):






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



		@property
		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					torch.manual_seed(0 )
					_snake_case														=   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=1_005	,       )

					_snake_case														=   MultilingualCLIP(__SCREAMING_SNAKE_CASE )
					_snake_case														=   text_encoder.eval()

					return text_encoder



		@property
		def 					_a					(      self     :					str ):






					"""simple docstring"""
					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": "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,
					}

					_snake_case														=   UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
					return model



		@property
		def 					_a					(      self     :					List[Any] ):






					"""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     :					Union[str, Any] ):






					"""simple docstring"""
					torch.manual_seed(0 )
					_snake_case														=   VQModel(**self.dummy_movq_kwargs )
					return model



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   self.dummy_text_encoder
					_snake_case														=   self.dummy_tokenizer
					_snake_case														=   self.dummy_unet
					_snake_case														=   self.dummy_movq

					_snake_case														=   DDIMScheduler(
					    num_train_timesteps=1_000	,       beta_schedule="linear"	,       beta_start=0.0_0_0_8_5	,       beta_end=0.0_1_2	,       clip_sample=__SCREAMING_SNAKE_CASE	,       set_alpha_to_one=__SCREAMING_SNAKE_CASE	,       steps_offset=1	,       prediction_type="epsilon"	,       thresholding=__SCREAMING_SNAKE_CASE	,       )

					_snake_case														=   {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "movq": movq,
					}
					return components



		def 					_a					(      self     :					Optional[Any]	,       __SCREAMING_SNAKE_CASE     :					Any	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=0 ):






					"""simple docstring"""
					_snake_case														=   floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
					_snake_case														=   floats_tensor((1, self.cross_attention_dim)	,       rng=random.Random(seed + 1 ) ).to(__SCREAMING_SNAKE_CASE )
					if str(__SCREAMING_SNAKE_CASE ).startswith("mps" ):
								_snake_case														=   torch.manual_seed(__SCREAMING_SNAKE_CASE )
					else:
								_snake_case														=   torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
					_snake_case														=   {
					    "prompt": "horse",
					    "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 					_a					(      self     :					List[str] ):






					"""simple docstring"""
					_snake_case														=   "cpu"

					_snake_case														=   self.get_dummy_components()

					_snake_case														=   self.pipeline_class(**__SCREAMING_SNAKE_CASE )
					_snake_case														=   pipe.to(__SCREAMING_SNAKE_CASE )

					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

					_snake_case														=   pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
					_snake_case														=   output.images

					_snake_case														=   pipe(
					    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )	,       return_dict=__SCREAMING_SNAKE_CASE	,       )[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(
					    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3] )

					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 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def 					_a					(      self     :					Dict ):






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







		def 					_a					(      self     :					Union[str, Any] ):






					"""simple docstring"""
					_snake_case														=   load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinsky/kandinsky_text2img_cat_fp16.npy" )

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

					_snake_case														=   KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"	,       torch_dtype=torch.floataa )
					_snake_case														=   pipeline.to(__SCREAMING_SNAKE_CASE )
					pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )

					_snake_case														=   "red cat, 4k photo"

					_snake_case														=   torch.Generator(device="cuda" ).manual_seed(0 )
					_snake_case							,			_snake_case														=   pipe_prior(
					    __SCREAMING_SNAKE_CASE	,       generator=__SCREAMING_SNAKE_CASE	,       num_inference_steps=5	,       negative_prompt=""	,       ).to_tuple()

					_snake_case														=   torch.Generator(device="cuda" ).manual_seed(0 )
					_snake_case														=   pipeline(
					    __SCREAMING_SNAKE_CASE	,       image_embeds=__SCREAMING_SNAKE_CASE	,       negative_image_embeds=__SCREAMING_SNAKE_CASE	,       generator=__SCREAMING_SNAKE_CASE	,       num_inference_steps=100	,       output_type="np"	,       )

					_snake_case														=   output.images[0]

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

					assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ViltImageProcessor

class 						_SCREAMING_SNAKE_CASE				(  unittest.TestCase				):






		"""simple docstring"""







		def __init__(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					Union[str, Any]=7	,       __SCREAMING_SNAKE_CASE     :					Dict=3	,       __SCREAMING_SNAKE_CASE     :					Any=18	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=30	,       __SCREAMING_SNAKE_CASE     :					int=400	,       __SCREAMING_SNAKE_CASE     :					int=True	,       __SCREAMING_SNAKE_CASE     :					Optional[int]=None	,       __SCREAMING_SNAKE_CASE     :					Any=2	,       __SCREAMING_SNAKE_CASE     :					Any=True	,       __SCREAMING_SNAKE_CASE     :					int=[0.5, 0.5, 0.5]	,       __SCREAMING_SNAKE_CASE     :					List[str]=[0.5, 0.5, 0.5]	,       ):






					"""simple docstring"""
					_snake_case														=   size if size is not None else {"shortest_edge": 30}
					_snake_case														=   parent
					_snake_case														=   batch_size
					_snake_case														=   num_channels
					_snake_case														=   image_size
					_snake_case														=   min_resolution
					_snake_case														=   max_resolution
					_snake_case														=   do_resize
					_snake_case														=   size
					_snake_case														=   size_divisor
					_snake_case														=   do_normalize
					_snake_case														=   image_mean
					_snake_case														=   image_std



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "size_divisor": self.size_divisor,
					}







		def 					_a					(      self     :					Any	,       __SCREAMING_SNAKE_CASE     :					List[str]	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






					"""simple docstring"""
					if not batched:
								_snake_case														=   self.size["shortest_edge"]
								_snake_case														=   image_inputs[0]
								if isinstance(__SCREAMING_SNAKE_CASE	,       Image.Image ):
											_snake_case							,			_snake_case														=   image.size
								else:
											_snake_case							,			_snake_case														=   image.shape[1], image.shape[2]
								_snake_case														=   size / min(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
								if h < w:
											_snake_case							,			_snake_case														=   size, scale * w
								else:
											_snake_case							,			_snake_case														=   scale * h, size

								_snake_case														=   int((1_333 / 800) * size )
								if max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE ) > max_size:
											_snake_case														=   max_size / max(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
											_snake_case														=   newh * scale
											_snake_case														=   neww * scale

								_snake_case							,			_snake_case														=   int(newh + 0.5 ), int(neww + 0.5 )
								_snake_case							,			_snake_case														=   (
								    newh // self.size_divisor * self.size_divisor,
								    neww // self.size_divisor * self.size_divisor,
								)

					else:
								_snake_case														=   []
								for image in image_inputs:
											_snake_case							,			_snake_case														=   self.get_expected_values([image] )
											expected_values.append((expected_height, expected_width) )
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[0] )[0]
								_snake_case														=   max(__SCREAMING_SNAKE_CASE	,       key=lambda __SCREAMING_SNAKE_CASE : item[1] )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class 						_SCREAMING_SNAKE_CASE				(  _lowercase   ,  unittest.TestCase				):






		"""simple docstring"""







		A_    			=   ViltImageProcessor if is_vision_available() else None



		def 					_a					(      self     :					Tuple ):






					"""simple docstring"""
					_snake_case														=   ViltImageProcessingTester(self )



		@property
		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 					_a					(      self     :					Optional[int] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_mean" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "image_std" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_normalize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "do_resize" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size" ) )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE	,       "size_divisor" ) )



		def 					_a					(      self     :					Optional[Any] ):






					"""simple docstring"""
					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 30} )

					_snake_case														=   self.image_processing_class.from_dict(self.image_processor_dict	,       size=42 )
					self.assertEqual(image_processor.size	,       {"shortest_edge": 42} )



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					pass



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PIL images
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       Image.Image )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )



		def 					_a					(      self     :					List[Any] ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       numpify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       np.ndarray )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )







		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					# Initialize image_processing
					_snake_case														=   self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					_snake_case														=   prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__SCREAMING_SNAKE_CASE	,       torchify=__SCREAMING_SNAKE_CASE )
					for image in image_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE	,       torch.Tensor )

					# Test not batched input
					_snake_case														=   image_processing(image_inputs[0]	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					_snake_case														=   image_processing(__SCREAMING_SNAKE_CASE	,       return_tensors="pt" ).pixel_values

					_snake_case							,			_snake_case														=   self.image_processor_tester.get_expected_values(__SCREAMING_SNAKE_CASE	,       batched=__SCREAMING_SNAKE_CASE )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )

