


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 		__a      (				lowerCAmelCase__    ,      unittest.TestCase			):
		_a	:  Optional[int]				=	UnCLIPImageVariationPipeline
		_a	:  Optional[Any]				=	IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
		_a	:  Union[str, Any]				=	IMAGE_VARIATION_BATCH_PARAMS

		_a	:  Dict				=	[
		    '''generator''',
		    '''return_dict''',
		    '''decoder_num_inference_steps''',
		    '''super_res_num_inference_steps''',
		]
		_a	:  Optional[Any]				=	False
		@property
		def 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





									return 32
		@property
		def 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





									return 32
		@property
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									return self.time_input_dim
		@property
		def 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									return self.time_input_dim * 4
		@property
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





									return 100
		@property
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
									return tokenizer
		@property
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase 		=			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-0_5			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1000			,    )
									return CLIPTextModelWithProjection(__lowerCAmelCase						)
		@property
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase 		=			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(__lowerCAmelCase						)
		@property
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									torch.manual_seed(0						)

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

									_UpperCAmelCase 		=			UnCLIPTextProjModel(**__lowerCAmelCase						)
									return model
		@property
		def 	UpperCAmelCase__				(			self						) ->       List[str]:




									"""simple docstring"""





									torch.manual_seed(0						)

									_UpperCAmelCase 		=			{
									    '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',
									}

									_UpperCAmelCase 		=			UNetaDConditionModel(**__lowerCAmelCase						)
									return model
		@property
		def 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""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 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									torch.manual_seed(0						)

									_UpperCAmelCase 		=			UNetaDModel(**self.dummy_super_res_kwargs						)
									return model
		@property
		def 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									torch.manual_seed(1						)

									_UpperCAmelCase 		=			UNetaDModel(**self.dummy_super_res_kwargs						)
									return model
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





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

									_UpperCAmelCase 		=			UnCLIPScheduler(
									    variance_type='learned_range'			,    prediction_type='epsilon'			,    num_train_timesteps=1000			,    )

									_UpperCAmelCase 		=			UnCLIPScheduler(
									    variance_type='fixed_small_log'			,    prediction_type='epsilon'			,    num_train_timesteps=1000			,    )

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

									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=0			,    _SCREAMING_SNAKE_CASE=True						) ->       str:




									"""simple docstring"""





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

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

									return {
									    "image": input_image,
									    "generator": generator,
									    "decoder_num_inference_steps": 2,
									    "super_res_num_inference_steps": 2,
									    "output_type": "np",
									}
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'

									_UpperCAmelCase 		=			self.get_dummy_components()

									_UpperCAmelCase 		=			self.pipeline_class(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			pipe.to(__lowerCAmelCase						)

									pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)

									_UpperCAmelCase 		=			pipe(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			output.images

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)

									_UpperCAmelCase 		=			pipe(
									    **__lowerCAmelCase			,    return_dict=__lowerCAmelCase			,    )[0]

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

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

									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Tuple:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'

									_UpperCAmelCase 		=			self.get_dummy_components()

									_UpperCAmelCase 		=			self.pipeline_class(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			pipe.to(__lowerCAmelCase						)

									pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)

									_UpperCAmelCase 		=			pipe(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			output.images

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)

									_UpperCAmelCase 		=			pipe(
									    **__lowerCAmelCase			,    return_dict=__lowerCAmelCase			,    )[0]

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

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

									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       int:




									"""simple docstring"""





									_UpperCAmelCase 		=			'cpu'

									_UpperCAmelCase 		=			self.get_dummy_components()

									_UpperCAmelCase 		=			self.pipeline_class(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			pipe.to(__lowerCAmelCase						)

									pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)
									_UpperCAmelCase 		=			[
									    pipeline_inputs['image'],
									    pipeline_inputs['image'],
									]

									_UpperCAmelCase 		=			pipe(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			output.images

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)
									_UpperCAmelCase 		=			[
									    tuple_pipeline_inputs['image'],
									    tuple_pipeline_inputs['image'],
									]

									_UpperCAmelCase 		=			pipe(
									    **__lowerCAmelCase			,    return_dict=__lowerCAmelCase			,    )[0]

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

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

									_UpperCAmelCase 		=			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 	UpperCAmelCase__				(			self						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			torch.device('cpu'						)

									class 		__a      :
											_a	:  Dict				=	1



									_UpperCAmelCase 		=			self.get_dummy_components()

									_UpperCAmelCase 		=			self.pipeline_class(**__lowerCAmelCase						)
									_UpperCAmelCase 		=			pipe.to(__lowerCAmelCase						)

									pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

									_UpperCAmelCase 		=			torch.Generator(device=__lowerCAmelCase						).manual_seed(0						)
									_UpperCAmelCase 		=			pipe.decoder.dtype
									_UpperCAmelCase 		=			1

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

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

									_UpperCAmelCase 		=			self.get_dummy_inputs(__lowerCAmelCase			,    pil_image=__lowerCAmelCase						)

									_UpperCAmelCase 		=			pipe(
									    **__lowerCAmelCase			,    decoder_latents=__lowerCAmelCase			,    super_res_latents=__lowerCAmelCase						).images

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

									_UpperCAmelCase 		=			pipe(
									    **__lowerCAmelCase			,    decoder_latents=__lowerCAmelCase			,    super_res_latents=__lowerCAmelCase			,    image_embeddings=__lowerCAmelCase			,    ).images

									# make sure passing text embeddings manually is identical
									assert np.abs(img_out_a - img_out_a						).max() < 1e-4
		@skip_mps
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			torch_device == 'cpu'

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

									self._test_attention_slicing_forward_pass(
									    test_max_difference=__lowerCAmelCase			,    expected_max_diff=__lowerCAmelCase						)
		@skip_mps
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





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

									self._test_inference_batch_single_identical(
									    test_max_difference=__lowerCAmelCase			,    relax_max_difference=__lowerCAmelCase			,    additional_params_copy_to_batched_inputs=__lowerCAmelCase			,    )
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			[
									    'decoder_num_inference_steps',
									    'super_res_num_inference_steps',
									]

									if torch_device == "mps":
																# TODO: MPS errors with larger batch sizes
																_UpperCAmelCase 		=			[2, 3]
																self._test_inference_batch_consistent(
																    batch_sizes=__lowerCAmelCase			,    additional_params_copy_to_batched_inputs=__lowerCAmelCase			,    )
									else:
																self._test_inference_batch_consistent(
																    additional_params_copy_to_batched_inputs=__lowerCAmelCase						)
		@skip_mps
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									return super().test_dict_tuple_outputs_equivalent()
		@skip_mps
		def 	UpperCAmelCase__				(			self						) ->       List[str]:




									"""simple docstring"""





									return super().test_save_load_local()






		@skip_mps
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									return super().test_save_load_optional_components()



@slow
@require_torch_gpu
class 		__a      (				unittest.TestCase			):
		def 	UpperCAmelCase__				(			self						) ->       List[str]:




									"""simple docstring"""





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






		def 	UpperCAmelCase__				(			self						) ->       List[str]:




									"""simple docstring"""





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

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

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

									_UpperCAmelCase 		=			output.images[0]

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

									assert_mean_pixel_difference(__lowerCAmelCase			,    __lowerCAmelCase			,    15						)

'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__lowercase          =  logging.get_logger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			def __init__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					warnings.warn(
					    """The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use DeformableDetrImageProcessor instead."""       ,    __lowerCAmelCase       ,    )
					super().__init__(*__lowerCAmelCase       ,    **__lowerCAmelCase)




