


'''simple docstring'''



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    UpperCAmelCase_  (__SCREAMING_SNAKE_CASE					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			UnCLIPImageVariationPipeline
    lowerCamelCase  :					Tuple							     =			IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
    lowerCamelCase  :					Any							     =			IMAGE_VARIATION_BATCH_PARAMS

    lowerCamelCase  :					Any							     =			[
        'generator',
        'return_dict',
        'decoder_num_inference_steps',
        'super_res_num_inference_steps',
    ]
    lowerCamelCase  :					Any							     =			False







    @property
    def       lowercase_				(							self	)				->     Union[str, Any]:
         return 32







    @property
    def       lowercase_				(							self	)				->     Any:
         return 32







    @property
    def       lowercase_				(							self	)				->     Tuple:
         return self.time_input_dim







    @property
    def       lowercase_				(							self	)				->     int:
         return self.time_input_dim * 4







    @property
    def       lowercase_				(							self	)				->     Tuple:
         return 1_00







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	str								=		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
         return tokenizer







    @property
    def       lowercase_				(							self	)				->     Tuple:
         torch.manual_seed(0	)
         __lowerCamelCase   :	Optional[int]								=		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=10_00			,     )
         return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE	)







    @property
    def       lowercase_				(							self	)				->     str:
         torch.manual_seed(0	)
         __lowerCamelCase   :	List[str]								=		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(_SCREAMING_SNAKE_CASE	)







    @property
    def       lowercase_				(							self	)				->     Union[str, Any]:
         torch.manual_seed(0	)

         __lowerCamelCase   :	List[Any]								=		{
             "clip_embeddings_dim": self.text_embedder_hidden_size,
             "time_embed_dim": self.time_embed_dim,
             "cross_attention_dim": self.cross_attention_dim,
         }

         __lowerCamelCase   :	Tuple								=		UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE	)
         return model







    @property
    def       lowercase_				(							self	)				->     Dict:
         torch.manual_seed(0	)

         __lowerCamelCase   :	Optional[Any]								=		{
             "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",
         }

         __lowerCamelCase   :	str								=		UNetaDConditionModel(**_SCREAMING_SNAKE_CASE	)
         return model







    @property
    def       lowercase_				(							self	)				->     Optional[int]:
         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       lowercase_				(							self	)				->     Tuple:
         torch.manual_seed(0	)

         __lowerCamelCase   :	Optional[Any]								=		UNetaDModel(**self.dummy_super_res_kwargs	)
         return model







    @property
    def       lowercase_				(							self	)				->     Optional[Any]:
         torch.manual_seed(1	)

         __lowerCamelCase   :	List[str]								=		UNetaDModel(**self.dummy_super_res_kwargs	)
         return model







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	List[str]								=		self.dummy_decoder
         __lowerCamelCase   :	str								=		self.dummy_text_proj
         __lowerCamelCase   :	List[Any]								=		self.dummy_text_encoder
         __lowerCamelCase   :	int								=		self.dummy_tokenizer
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_super_res_first
         __lowerCamelCase   :	str								=		self.dummy_super_res_last

         __lowerCamelCase   :	Union[str, Any]								=		UnCLIPScheduler(
             variance_type='learned_range'			,     prediction_type='epsilon'			,     num_train_timesteps=10_00			,     )

         __lowerCamelCase   :	str								=		UnCLIPScheduler(
             variance_type='fixed_small_log'			,     prediction_type='epsilon'			,     num_train_timesteps=10_00			,     )

         __lowerCamelCase   :	List[str]								=		CLIPImageProcessor(crop_size=32			,     size=32	)

         __lowerCamelCase   :	List[str]								=		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       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=True	)				->     Union[str, Any]:
         __lowerCamelCase   :	Tuple								=		floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_SCREAMING_SNAKE_CASE	)	).to(_SCREAMING_SNAKE_CASE	)
         if str(_SCREAMING_SNAKE_CASE	).startswith('mps'	):
              __lowerCamelCase   :	List[Any]								=		torch.manual_seed(_SCREAMING_SNAKE_CASE	)
         else:
              __lowerCamelCase   :	str								=		torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(_SCREAMING_SNAKE_CASE	)

         if pil_image:
              __lowerCamelCase   :	str								=		input_image * 0.5 + 0.5
              __lowerCamelCase   :	Optional[int]								=		input_image.clamp(0			,     1	)
              __lowerCamelCase   :	int								=		input_image.cpu().permute(0			,     2			,     3			,     1	).float().numpy()
              __lowerCamelCase   :	Optional[int]								=		DiffusionPipeline.numpy_to_pil(_SCREAMING_SNAKE_CASE	)[0]

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







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[str]								=		"cpu"

         __lowerCamelCase   :	int								=		self.get_dummy_components()

         __lowerCamelCase   :	int								=		self.pipeline_class(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	int								=		pipe.to(_SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	Any								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	int								=		pipe(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	Tuple								=		output.images

         __lowerCamelCase   :	Tuple								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	Optional[int]								=		pipe(
             **_SCREAMING_SNAKE_CASE			,     return_dict=_SCREAMING_SNAKE_CASE			,     )[0]

         __lowerCamelCase   :	Optional[Any]								=		image[0, -3:, -3:, -1]
         __lowerCamelCase   :	Optional[Any]								=		image_from_tuple[0, -3:, -3:, -1]

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

         __lowerCamelCase   :	Optional[int]								=		np.array(
             [
                 0.9_9_9_7,
                 0.0_0_0_2,
                 0.9_9_9_7,
                 0.9_9_9_7,
                 0.9_9_6_9,
                 0.0_0_2_3,
                 0.9_9_9_7,
                 0.9_9_6_9,
                 0.9_9_7_0,
             ]	)

         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       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Optional[Any]								=		"cpu"

         __lowerCamelCase   :	List[str]								=		self.get_dummy_components()

         __lowerCamelCase   :	Dict								=		self.pipeline_class(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe.to(_SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	List[Any]								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	Any								=		pipe(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	Optional[int]								=		output.images

         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	Tuple								=		pipe(
             **_SCREAMING_SNAKE_CASE			,     return_dict=_SCREAMING_SNAKE_CASE			,     )[0]

         __lowerCamelCase   :	Optional[Any]								=		image[0, -3:, -3:, -1]
         __lowerCamelCase   :	Optional[Any]								=		image_from_tuple[0, -3:, -3:, -1]

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

         __lowerCamelCase   :	Dict								=		np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]	)

         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       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Tuple								=		"cpu"

         __lowerCamelCase   :	List[Any]								=		self.get_dummy_components()

         __lowerCamelCase   :	List[Any]								=		self.pipeline_class(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	Optional[int]								=		pipe.to(_SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	List[Any]								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	str								=		[
             pipeline_inputs["image"],
             pipeline_inputs["image"],
         ]

         __lowerCamelCase   :	Optional[int]								=		pipe(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	List[Any]								=		output.images

         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	Optional[Any]								=		[
             tuple_pipeline_inputs["image"],
             tuple_pipeline_inputs["image"],
         ]

         __lowerCamelCase   :	Tuple								=		pipe(
             **_SCREAMING_SNAKE_CASE			,     return_dict=_SCREAMING_SNAKE_CASE			,     )[0]

         __lowerCamelCase   :	Dict								=		image[0, -3:, -3:, -1]
         __lowerCamelCase   :	int								=		image_from_tuple[0, -3:, -3:, -1]

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

         __lowerCamelCase   :	List[str]								=		np.array(
             [
                 0.9_9_9_7,
                 0.9_9_8_9,
                 0.0_0_0_8,
                 0.0_0_2_1,
                 0.9_9_6_0,
                 0.0_0_1_8,
                 0.0_0_1_4,
                 0.0_0_0_2,
                 0.9_9_3_3,
             ]	)

         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       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Tuple								=		torch.device('cpu'	)





         class    UpperCAmelCase_  :





             """simple docstring"""






             lowerCamelCase  :					Union[str, Any]							     =			1







         __lowerCamelCase   :	Optional[Any]								=		self.get_dummy_components()

         __lowerCamelCase   :	List[str]								=		self.pipeline_class(**_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase   :	Tuple								=		pipe.to(_SCREAMING_SNAKE_CASE	)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	Tuple								=		torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
         __lowerCamelCase   :	Any								=		pipe.decoder.dtype
         __lowerCamelCase   :	List[Any]								=		1

         __lowerCamelCase   :	int								=		(
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         __lowerCamelCase   :	Union[str, Any]								=		pipe.prepare_latents(
             _SCREAMING_SNAKE_CASE			,     dtype=_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE			,     generator=_SCREAMING_SNAKE_CASE			,     latents=_SCREAMING_SNAKE_CASE			,     scheduler=DummyScheduler()	)

         __lowerCamelCase   :	List[str]								=		(
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         __lowerCamelCase   :	Union[str, Any]								=		pipe.prepare_latents(
             _SCREAMING_SNAKE_CASE			,     dtype=_SCREAMING_SNAKE_CASE			,     device=_SCREAMING_SNAKE_CASE			,     generator=_SCREAMING_SNAKE_CASE			,     latents=_SCREAMING_SNAKE_CASE			,     scheduler=DummyScheduler()	)

         __lowerCamelCase   :	Union[str, Any]								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	Any								=		pipe(
             **_SCREAMING_SNAKE_CASE			,     decoder_latents=_SCREAMING_SNAKE_CASE			,     super_res_latents=_SCREAMING_SNAKE_CASE	).images

         __lowerCamelCase   :	Dict								=		self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			,     pil_image=_SCREAMING_SNAKE_CASE	)
         # Don't pass image, instead pass embedding
         __lowerCamelCase   :	Any								=		pipeline_inputs.pop('image'	)
         __lowerCamelCase   :	Optional[Any]								=		pipe.image_encoder(_SCREAMING_SNAKE_CASE	).image_embeds

         __lowerCamelCase   :	int								=		pipe(
             **_SCREAMING_SNAKE_CASE			,     decoder_latents=_SCREAMING_SNAKE_CASE			,     super_res_latents=_SCREAMING_SNAKE_CASE			,     image_embeddings=_SCREAMING_SNAKE_CASE			,     ).images

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







    @skip_mps
    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	Union[str, Any]								=		torch_device == "cpu"

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         __lowerCamelCase   :	Union[str, Any]								=		1E-2

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







    @skip_mps
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Dict								=		torch_device == "cpu"
         __lowerCamelCase   :	int								=		True
         __lowerCamelCase   :	int								=		[
             "decoder_num_inference_steps",
             "super_res_num_inference_steps",
         ]

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







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	int								=		[
             "decoder_num_inference_steps",
             "super_res_num_inference_steps",
         ]

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







    @skip_mps
    def       lowercase_				(							self	)				->     str:
         return super().test_dict_tuple_outputs_equivalent()







    @skip_mps
    def       lowercase_				(							self	)				->     Any:
         return super().test_save_load_local()







    @skip_mps
    def       lowercase_				(							self	)				->     Any:
         return super().test_save_load_optional_components()







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





    """simple docstring"""






    def       lowercase_				(							self	)				->     str:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Optional[Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'	)
         __lowerCamelCase   :	int								=		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'	)

         __lowerCamelCase   :	Union[str, Any]								=		UnCLIPImageVariationPipeline.from_pretrained(
             'kakaobrain/karlo-v1-alpha-image-variations'			,     torch_dtype=torch.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		pipeline.to(_SCREAMING_SNAKE_CASE	)
         pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

         __lowerCamelCase   :	List[str]								=		torch.Generator(device='cpu'	).manual_seed(0	)
         __lowerCamelCase   :	Any								=		pipeline(
             _SCREAMING_SNAKE_CASE			,     generator=_SCREAMING_SNAKE_CASE			,     output_type='np'			,     )

         __lowerCamelCase   :	Optional[int]								=		output.images[0]

         assert image.shape == (2_56, 2_56, 3)

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     15	)











"""simple docstring"""






import re


def 			_lowercase				(  __snake_case	)						->   str:

			if len(re.findall("[ATCG]"					,__snake_case	)	) != len(__snake_case	):
						raise ValueError("Invalid Strand"	)

			return dna.translate(dna.maketrans("ATCG"					,"TAGC"	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()