
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = UnCLIPImageVariationPipeline
	_UpperCAmelCase	:		Tuple          = IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
	_UpperCAmelCase	:		List[str]          = IMAGE_VARIATION_BATCH_PARAMS

	_UpperCAmelCase	:		Union[str, Any]          = [
	    "generator",
	    "return_dict",
	    "decoder_num_inference_steps",
	    "super_res_num_inference_steps",
	]
	_UpperCAmelCase	:		Tuple          = False



	@property
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return 32



	@property
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return self.time_input_dim



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				return self.time_input_dim * 4



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


				'''simple docstring'''


				return 100



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


				'''simple docstring'''


				_snake_case          =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )
				return tokenizer



	@property
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      CLIPTextConfig(
				    bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=self.text_embedder_hidden_size      ,  projection_dim=self.text_embedder_hidden_size      ,  intermediate_size=37      ,  layer_norm_eps=1E-05      ,  num_attention_heads=4      ,  num_hidden_layers=5      ,  pad_token_id=1      ,  vocab_size=1_000      ,  )
				return CLIPTextModelWithProjection(lowercase     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size      ,  projection_dim=self.text_embedder_hidden_size      ,  num_hidden_layers=5      ,  num_attention_heads=4      ,  image_size=32      ,  intermediate_size=37      ,  patch_size=1      ,  )
				return CLIPVisionModelWithProjection(lowercase     )



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


				'''simple docstring'''


				torch.manual_seed(0     )

				_snake_case          =      {
				    'clip_embeddings_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'cross_attention_dim': self.cross_attention_dim,
				}

				_snake_case          =      UnCLIPTextProjModel(**lowercase     )
				return model



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				torch.manual_seed(0     )

				_snake_case          =      {
				    'sample_size': 32,
				    # RGB in channels
				    'in_channels': 3,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 6,
				    '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,
				    'cross_attention_dim': self.cross_attention_dim,
				    'attention_head_dim': 4,
				    'resnet_time_scale_shift': 'scale_shift',
				    'class_embed_type': 'identity',
				}

				_snake_case          =      UNetaDConditionModel(**lowercase     )
				return model



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


				'''simple docstring'''


				return {
				    "sample_size": 64,
				    "layers_per_block": 1,
				    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
				    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "in_channels": 6,
				    "out_channels": 3,
				}



	@property
	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				torch.manual_seed(0     )

				_snake_case          =      UNetaDModel(**self.dummy_super_res_kwargs     )
				return model



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				torch.manual_seed(1     )

				_snake_case          =      UNetaDModel(**self.dummy_super_res_kwargs     )
				return model



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      self.dummy_decoder
				_snake_case          =      self.dummy_text_proj
				_snake_case          =      self.dummy_text_encoder
				_snake_case          =      self.dummy_tokenizer
				_snake_case          =      self.dummy_super_res_first
				_snake_case          =      self.dummy_super_res_last

				_snake_case          =      UnCLIPScheduler(
				    variance_type='learned_range'      ,  prediction_type='epsilon'      ,  num_train_timesteps=1_000      ,  )

				_snake_case          =      UnCLIPScheduler(
				    variance_type='fixed_small_log'      ,  prediction_type='epsilon'      ,  num_train_timesteps=1_000      ,  )

				_snake_case          =      CLIPImageProcessor(crop_size=32      ,  size=32     )

				_snake_case          =      self.dummy_image_encoder

				return {
				    "decoder": decoder,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "text_proj": text_proj,
				    "feature_extractor": feature_extractor,
				    "image_encoder": image_encoder,
				    "super_res_first": super_res_first,
				    "super_res_last": super_res_last,
				    "decoder_scheduler": decoder_scheduler,
				    "super_res_scheduler": super_res_scheduler,
				}



	def 			A							(  self     :   List[Any]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Optional[Any]=0      ,  lowercase     :   List[str]=True     ):


				'''simple docstring'''


				_snake_case          =      floats_tensor((1, 3, 32, 32)      ,  rng=random.Random(lowercase     )     ).to(lowercase     )
				if str(lowercase     ).startswith('mps'     ):
							_snake_case          =      torch.manual_seed(lowercase     )
				else:
							_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(lowercase     )

				if pil_image:
							_snake_case          =      input_image * 0.5 + 0.5
							_snake_case          =      input_image.clamp(0      ,  1     )
							_snake_case          =      input_image.cpu().permute(0      ,  2      ,  3      ,  1     ).float().numpy()
							_snake_case          =      DiffusionPipeline.numpy_to_pil(lowercase     )[0]

				return {
				    "image": input_image,
				    "generator": generator,
				    "decoder_num_inference_steps": 2,
				    "super_res_num_inference_steps": 2,
				    "output_type": "np",
				}



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.get_dummy_components()

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

				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )

				_snake_case          =      pipe(**lowercase     )
				_snake_case          =      output.images

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )

				_snake_case          =      pipe(
				    **lowercase      ,  return_dict=lowercase      ,  )[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(
				    [
				        0.9997,
				        0.0002,
				        0.9997,
				        0.9997,
				        0.9969,
				        0.0023,
				        0.9997,
				        0.9969,
				        0.9970,
				    ]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.get_dummy_components()

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

				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )

				_snake_case          =      pipe(**lowercase     )
				_snake_case          =      output.images

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )

				_snake_case          =      pipe(
				    **lowercase      ,  return_dict=lowercase      ,  )[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([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      'cpu'

				_snake_case          =      self.get_dummy_components()

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

				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )
				_snake_case          =      [
				    pipeline_inputs['image'],
				    pipeline_inputs['image'],
				]

				_snake_case          =      pipe(**lowercase     )
				_snake_case          =      output.images

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )
				_snake_case          =      [
				    tuple_pipeline_inputs['image'],
				    tuple_pipeline_inputs['image'],
				]

				_snake_case          =      pipe(
				    **lowercase      ,  return_dict=lowercase      ,  )[0]

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

				assert image.shape == (2, 64, 64, 3)

				_snake_case          =      np.array(
				    [
				        0.9997,
				        0.9989,
				        0.0008,
				        0.0021,
				        0.9960,
				        0.0018,
				        0.0014,
				        0.0002,
				        0.9933,
				    ]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      torch.device('cpu'     )




				class        SCREAMING_SNAKE_CASE__					:





					'''simple docstring'''



					_UpperCAmelCase	:		Optional[int]          = 1




				_snake_case          =      self.get_dummy_components()

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

				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(0     )
				_snake_case          =      pipe.decoder.dtype
				_snake_case          =      1

				_snake_case          =      (
				    batch_size,
				    pipe.decoder.config.in_channels,
				    pipe.decoder.config.sample_size,
				    pipe.decoder.config.sample_size,
				)
				_snake_case          =      pipe.prepare_latents(
				    lowercase      ,  dtype=lowercase      ,  device=lowercase      ,  generator=lowercase      ,  latents=lowercase      ,  scheduler=DummyScheduler()     )

				_snake_case          =      (
				    batch_size,
				    pipe.super_res_first.config.in_channels // 2,
				    pipe.super_res_first.config.sample_size,
				    pipe.super_res_first.config.sample_size,
				)
				_snake_case          =      pipe.prepare_latents(
				    lowercase      ,  dtype=lowercase      ,  device=lowercase      ,  generator=lowercase      ,  latents=lowercase      ,  scheduler=DummyScheduler()     )

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )

				_snake_case          =      pipe(
				    **lowercase      ,  decoder_latents=lowercase      ,  super_res_latents=lowercase     ).images

				_snake_case          =      self.get_dummy_inputs(lowercase      ,  pil_image=lowercase     )
				# Don't pass image, instead pass embedding
				_snake_case          =      pipeline_inputs.pop('image'     )
				_snake_case          =      pipe.image_encoder(lowercase     ).image_embeds

				_snake_case          =      pipe(
				    **lowercase      ,  decoder_latents=lowercase      ,  super_res_latents=lowercase      ,  image_embeddings=lowercase      ,  ).images

				# make sure passing text embeddings manually is identical
				assert np.abs(img_out_a - img_out_a     ).max() < 1E-4



	@skip_mps
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      torch_device == 'cpu'

				# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
				_snake_case          =      1E-2

				self._test_attention_slicing_forward_pass(
				    test_max_difference=lowercase      ,  expected_max_diff=lowercase     )



	@skip_mps
	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      torch_device == 'cpu'
				_snake_case          =      True
				_snake_case          =      [
				    'decoder_num_inference_steps',
				    'super_res_num_inference_steps',
				]

				self._test_inference_batch_single_identical(
				    test_max_difference=lowercase      ,  relax_max_difference=lowercase      ,  additional_params_copy_to_batched_inputs=lowercase      ,  )



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      [
				    'decoder_num_inference_steps',
				    'super_res_num_inference_steps',
				]

				if torch_device == "mps":
							# TODO: MPS errors with larger batch sizes
							_snake_case          =      [2, 3]
							self._test_inference_batch_consistent(
							    batch_sizes=lowercase      ,  additional_params_copy_to_batched_inputs=lowercase      ,  )
				else:
							self._test_inference_batch_consistent(
							    additional_params_copy_to_batched_inputs=lowercase     )



	@skip_mps
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return super().test_dict_tuple_outputs_equivalent()



	@skip_mps
	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return super().test_save_load_local()





	@skip_mps
	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				return super().test_save_load_optional_components()




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





	'''simple docstring'''



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


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





	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'     )
				_snake_case          =      load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'     )

				_snake_case          =      UnCLIPImageVariationPipeline.from_pretrained(
				    'kakaobrain/karlo-v1-alpha-image-variations'      ,  torch_dtype=torch.floataa     )
				_snake_case          =      pipeline.to(lowercase     )
				pipeline.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      torch.Generator(device='cpu'     ).manual_seed(0     )
				_snake_case          =      pipeline(
				    lowercase      ,  generator=lowercase      ,  output_type='np'      ,  )

				_snake_case          =      output.images[0]

				assert image.shape == (256, 256, 3)

				assert_mean_pixel_difference(lowercase      ,  lowercase      ,  15     )

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def 	a_		(	__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     int	, __lowercase       :     int			)			->  np.ndarray:
			_snake_case          =      cva.getAffineTransform(__lowercase	, __lowercase			)
			return cva.warpAffine(__lowercase	, __lowercase	, (rows, cols)			)


if __name__ == "__main__":
			# read original image
			_lowerCamelCase	:    Optional[Any]          =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
			)
			# turn image in gray scale value
			_lowerCamelCase	:    List[str]          =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			_lowerCamelCase   ,					_lowerCamelCase	:    List[Any]          =      gray_img.shape

			# set different points to rotate image
			_lowerCamelCase	:    str          =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			_lowerCamelCase	:    Optional[Any]          =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			_lowerCamelCase	:    List[str]          =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			_lowerCamelCase	:    Dict          =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			_lowerCamelCase	:    int          =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			_lowerCamelCase	:    Any          =      plt.figure(1)
			_lowerCamelCase	:    List[Any]          =      ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
						plt.title(titles[i])
						plt.axis('''off''')
						plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
			plt.show()