



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_					( a     ,      unittest.TestCase):
							lowerCamelCase__         =	UnCLIPImageVariationPipeline
							lowerCamelCase__         =	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
							lowerCamelCase__         =	IMAGE_VARIATION_BATCH_PARAMS

							lowerCamelCase__         =	[
							    'generator',
							    'return_dict',
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]
							lowerCamelCase__         =	False







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 32







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 32







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self.time_input_dim







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return self.time_input_dim * 4







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														return 100







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							str				     =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
														return tokenizer







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)
														_lowerCAmelCase  :							Tuple				     =					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=1000,    )
														return CLIPTextModelWithProjection(__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)
														_lowerCAmelCase  :							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(__a)







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase  :							Union[str, Any]				     =					{
														    "clip_embeddings_dim": self.text_embedder_hidden_size,
														    "time_embed_dim": self.time_embed_dim,
														    "cross_attention_dim": self.cross_attention_dim,
														}

														_lowerCAmelCase  :							Union[str, Any]				     =					UnCLIPTextProjModel(**__a)
														return model







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase  :							Optional[int]				     =					{
														    "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  :							Tuple				     =					UNetaDConditionModel(**__a)
														return model







							@property
							def      snake_case__   (     self):

														'''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      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(0)

														_lowerCAmelCase  :							str				     =					UNetaDModel(**self.dummy_super_res_kwargs)
														return model







							@property
							def      snake_case__   (     self):

														'''simple docstring'''

														torch.manual_seed(1)

														_lowerCAmelCase  :							Union[str, Any]				     =					UNetaDModel(**self.dummy_super_res_kwargs)
														return model







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.dummy_decoder
														_lowerCAmelCase  :							int				     =					self.dummy_text_proj
														_lowerCAmelCase  :							Tuple				     =					self.dummy_text_encoder
														_lowerCAmelCase  :							str				     =					self.dummy_tokenizer
														_lowerCAmelCase  :							Optional[Any]				     =					self.dummy_super_res_first
														_lowerCAmelCase  :							List[Any]				     =					self.dummy_super_res_last

														_lowerCAmelCase  :							Tuple				     =					UnCLIPScheduler(
														    variance_type="learned_range",    prediction_type="epsilon",    num_train_timesteps=1000,    )

														_lowerCAmelCase  :							Any				     =					UnCLIPScheduler(
														    variance_type="fixed_small_log",    prediction_type="epsilon",    num_train_timesteps=1000,    )

														_lowerCAmelCase  :							Dict				     =					CLIPImageProcessor(crop_size=32,    size=32)

														_lowerCAmelCase  :							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      snake_case__   (     self,    __a,    __a=0,    __a=True):

														'''simple docstring'''

														_lowerCAmelCase  :							List[Any]				     =					floats_tensor((1, 3, 32, 32),    rng=random.Random(__a)).to(__a)
														if str(__a).startswith("mps"):
																					_lowerCAmelCase  :							List[Any]				     =					torch.manual_seed(__a)
														else:
																					_lowerCAmelCase  :							Dict				     =					torch.Generator(device=__a).manual_seed(__a)

														if pil_image:
																					_lowerCAmelCase  :							Any				     =					input_image * 0.5 + 0.5
																					_lowerCAmelCase  :							int				     =					input_image.clamp(0,    1)
																					_lowerCAmelCase  :							Optional[Any]				     =					input_image.cpu().permute(0,    2,    3,    1).float().numpy()
																					_lowerCAmelCase  :							Dict				     =					DiffusionPipeline.numpy_to_pil(__a)[0]

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					"cpu"

														_lowerCAmelCase  :							Union[str, Any]				     =					self.get_dummy_components()

														_lowerCAmelCase  :							int				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							int				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							Optional[Any]				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							List[str]				     =					pipe(**__a)
														_lowerCAmelCase  :							Optional[int]				     =					output.images

														_lowerCAmelCase  :							str				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Optional[int]				     =					pipe(
														    **__a,    return_dict=__a,    )[0]

														_lowerCAmelCase  :							Union[str, Any]				     =					image[0, -3:, -3:, -1]
														_lowerCAmelCase  :							str				     =					image_from_tuple[0, -3:, -3:, -1]

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

														_lowerCAmelCase  :							str				     =					np.array(
														    [
														        0.9_997,
														        0.0_002,
														        0.9_997,
														        0.9_997,
														        0.9_969,
														        0.0_023,
														        0.9_997,
														        0.9_969,
														        0.9_970,
														    ])

														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Union[str, Any]				     =					"cpu"

														_lowerCAmelCase  :							Dict				     =					self.get_dummy_components()

														_lowerCAmelCase  :							int				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							Tuple				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							List[Any]				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Optional[int]				     =					pipe(**__a)
														_lowerCAmelCase  :							Any				     =					output.images

														_lowerCAmelCase  :							Optional[Any]				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Optional[Any]				     =					pipe(
														    **__a,    return_dict=__a,    )[0]

														_lowerCAmelCase  :							str				     =					image[0, -3:, -3:, -1]
														_lowerCAmelCase  :							List[Any]				     =					image_from_tuple[0, -3:, -3:, -1]

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

														_lowerCAmelCase  :							Any				     =					np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971])

														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					"cpu"

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

														_lowerCAmelCase  :							Optional[int]				     =					self.pipeline_class(**__a)
														_lowerCAmelCase  :							List[str]				     =					pipe.to(__a)

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							int				     =					self.get_dummy_inputs(__a,    pil_image=__a)
														_lowerCAmelCase  :							List[str]				     =					[
														    pipeline_inputs["image"],
														    pipeline_inputs["image"],
														]

														_lowerCAmelCase  :							Union[str, Any]				     =					pipe(**__a)
														_lowerCAmelCase  :							Optional[Any]				     =					output.images

														_lowerCAmelCase  :							Any				     =					self.get_dummy_inputs(__a,    pil_image=__a)
														_lowerCAmelCase  :							Tuple				     =					[
														    tuple_pipeline_inputs["image"],
														    tuple_pipeline_inputs["image"],
														]

														_lowerCAmelCase  :							Dict				     =					pipe(
														    **__a,    return_dict=__a,    )[0]

														_lowerCAmelCase  :							Tuple				     =					image[0, -3:, -3:, -1]
														_lowerCAmelCase  :							str				     =					image_from_tuple[0, -3:, -3:, -1]

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

														_lowerCAmelCase  :							Dict				     =					np.array(
														    [
														        0.9_997,
														        0.9_989,
														        0.0_008,
														        0.0_021,
														        0.9_960,
														        0.0_018,
														        0.0_014,
														        0.0_002,
														        0.9_933,
														    ])

														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      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Dict				     =					torch.device("cpu")



														class 						UpperCAmelCase_					:
																					lowerCamelCase__         =	1



														_lowerCAmelCase  :							Optional[int]				     =					self.get_dummy_components()

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

														pipe.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							Any				     =					torch.Generator(device=__a).manual_seed(0)
														_lowerCAmelCase  :							List[str]				     =					pipe.decoder.dtype
														_lowerCAmelCase  :							Optional[int]				     =					1

														_lowerCAmelCase  :							List[Any]				     =					(
														    batch_size,
														    pipe.decoder.config.in_channels,
														    pipe.decoder.config.sample_size,
														    pipe.decoder.config.sample_size,
														)
														_lowerCAmelCase  :							List[Any]				     =					pipe.prepare_latents(
														    __a,    dtype=__a,    device=__a,    generator=__a,    latents=__a,    scheduler=DummyScheduler())

														_lowerCAmelCase  :							Union[str, Any]				     =					(
														    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  :							int				     =					pipe.prepare_latents(
														    __a,    dtype=__a,    device=__a,    generator=__a,    latents=__a,    scheduler=DummyScheduler())

														_lowerCAmelCase  :							str				     =					self.get_dummy_inputs(__a,    pil_image=__a)

														_lowerCAmelCase  :							Dict				     =					pipe(
														    **__a,    decoder_latents=__a,    super_res_latents=__a).images

														_lowerCAmelCase  :							int				     =					self.get_dummy_inputs(__a,    pil_image=__a)
														# Don't pass image, instead pass embedding
														_lowerCAmelCase  :							Union[str, Any]				     =					pipeline_inputs.pop("image")
														_lowerCAmelCase  :							Dict				     =					pipe.image_encoder(__a).image_embeds

														_lowerCAmelCase  :							str				     =					pipe(
														    **__a,    decoder_latents=__a,    super_res_latents=__a,    image_embeddings=__a,    ).images

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







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					torch_device == "cpu"

														# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
														_lowerCAmelCase  :							Optional[int]				     =					1E-2

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







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					torch_device == "cpu"
														_lowerCAmelCase  :							Optional[Any]				     =					True
														_lowerCAmelCase  :							List[str]				     =					[
														    "decoder_num_inference_steps",
														    "super_res_num_inference_steps",
														]

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					[
														    "decoder_num_inference_steps",
														    "super_res_num_inference_steps",
														]

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







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														return super().test_dict_tuple_outputs_equivalent()







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														return super().test_save_load_local()







							@skip_mps
							def      snake_case__   (     self):

														'''simple docstring'''

														return super().test_save_load_optional_components()



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







							def      snake_case__   (     self):

														'''simple docstring'''

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







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					load_image(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png")
														_lowerCAmelCase  :							List[str]				     =					load_numpy(
														    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
														    "/unclip/karlo_v1_alpha_cat_variation_fp16.npy")

														_lowerCAmelCase  :							Any				     =					UnCLIPImageVariationPipeline.from_pretrained(
														    "kakaobrain/karlo-v1-alpha-image-variations",    torch_dtype=torch.floataa)
														_lowerCAmelCase  :							Dict				     =					pipeline.to(__a)
														pipeline.set_progress_bar_config(disable=__a)

														_lowerCAmelCase  :							Dict				     =					torch.Generator(device="cpu").manual_seed(0)
														_lowerCAmelCase  :							Tuple				     =					pipeline(
														    __a,    generator=__a,    output_type="np",    )

														_lowerCAmelCase  :							Any				     =					output.images[0]

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

														assert_mean_pixel_difference(__a,    __a,    15)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
