





import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				snake_case_       ,				snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Tuple       =       StableUnCLIPPipeline
						__magic_name__:			Any       =       TEXT_TO_IMAGE_PARAMS
						__magic_name__:			Optional[int]       =       TEXT_TO_IMAGE_BATCH_PARAMS
						__magic_name__:			Optional[int]       =       TEXT_TO_IMAGE_IMAGE_PARAMS
						__magic_name__:			Tuple       =       TEXT_TO_IMAGE_IMAGE_PARAMS

						# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
						__magic_name__:			int       =       False






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	32
							snake_case_		:			Dict											=	embedder_hidden_size

							# prior components

							torch.manual_seed(0	)
							snake_case_		:			Union[str, Any]											=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

							torch.manual_seed(0	)
							snake_case_		:			Tuple											=	CLIPTextModelWithProjection(
							    CLIPTextConfig(
							        bos_token_id=0		,   eos_token_id=2		,   hidden_size=_A		,   projection_dim=_A		,   intermediate_size=37		,   layer_norm_eps=1E-05		,   num_attention_heads=4		,   num_hidden_layers=5		,   pad_token_id=1		,   vocab_size=1000		,   )	)

							torch.manual_seed(0	)
							snake_case_		:			Dict											=	PriorTransformer(
							    num_attention_heads=2		,   attention_head_dim=12		,   embedding_dim=_A		,   num_layers=1		,   )

							torch.manual_seed(0	)
							snake_case_		:			Dict											=	DDPMScheduler(
							    variance_type='fixed_small_log'		,   prediction_type='sample'		,   num_train_timesteps=1000		,   clip_sample=_A		,   clip_sample_range=5.0		,   beta_schedule='squaredcos_cap_v2'		,   )

							# regular denoising components

							torch.manual_seed(0	)
							snake_case_		:			Union[str, Any]											=	StableUnCLIPImageNormalizer(embedding_dim=_A	)
							snake_case_		:			str											=	DDPMScheduler(beta_schedule='squaredcos_cap_v2'	)

							torch.manual_seed(0	)
							snake_case_		:			Tuple											=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

							torch.manual_seed(0	)
							snake_case_		:			Union[str, Any]											=	CLIPTextModel(
							    CLIPTextConfig(
							        bos_token_id=0		,   eos_token_id=2		,   hidden_size=_A		,   projection_dim=32		,   intermediate_size=37		,   layer_norm_eps=1E-05		,   num_attention_heads=4		,   num_hidden_layers=5		,   pad_token_id=1		,   vocab_size=1000		,   )	)

							torch.manual_seed(0	)
							snake_case_		:			Tuple											=	UNetaDConditionModel(
							    sample_size=32		,   in_channels=4		,   out_channels=4		,   down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')		,   up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')		,   block_out_channels=(32, 64)		,   attention_head_dim=(2, 4)		,   class_embed_type='projection'		,   projection_class_embeddings_input_dim=embedder_projection_dim * 2		,   cross_attention_dim=_A		,   layers_per_block=1		,   upcast_attention=_A		,   use_linear_projection=_A		,   )

							torch.manual_seed(0	)
							snake_case_		:			Optional[int]											=	DDIMScheduler(
							    beta_schedule='scaled_linear'		,   beta_start=0.0_0_0_8_5		,   beta_end=0.0_1_2		,   prediction_type='v_prediction'		,   set_alpha_to_one=_A		,   steps_offset=1		,   )

							torch.manual_seed(0	)
							snake_case_		:			str											=	AutoencoderKL()

							snake_case_		:			str											=	{
							    # prior components
							    'prior_tokenizer': prior_tokenizer,
							    'prior_text_encoder': prior_text_encoder,
							    'prior': prior,
							    'prior_scheduler': prior_scheduler,
							    # image noising components
							    'image_normalizer': image_normalizer,
							    'image_noising_scheduler': image_noising_scheduler,
							    # regular denoising components
							    'tokenizer': tokenizer,
							    'text_encoder': text_encoder,
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							}

							return components






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Tuple		,   _A      :      List[str]=0	)							->    List[Any]:







							"""simple docstring"""


							if str(_A	).startswith('mps'	):
								snake_case_		:			List[Any]											=	torch.manual_seed(_A	)
							else:
								snake_case_		:			Optional[Any]											=	torch.Generator(device=_A	).manual_seed(_A	)
							snake_case_		:			Union[str, Any]											=	{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'prior_num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs






						def UpperCAmelCase_				(      self      :      int	)							->    str:







							"""simple docstring"""


							snake_case_		:			int											=	torch_device == 'cpu'

							self._test_attention_slicing_forward_pass(test_max_difference=_A	)






						def UpperCAmelCase_				(      self      :      Any	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			Any											=	torch_device in ['cpu', 'mps']

							self._test_inference_batch_single_identical(test_max_difference=_A	)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    str:







							"""simple docstring"""


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






						def UpperCAmelCase_				(      self      :      str	)							->    str:







							"""simple docstring"""


							snake_case_		:			Tuple											=	load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'	)

							snake_case_		:			Optional[int]											=	StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'		,   torch_dtype=torch.floataa	)
							pipe.to(_A	)
							pipe.set_progress_bar_config(disable=_A	)
							# stable unclip will oom when integration tests are run on a V100,
							# so turn on memory savings
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							snake_case_		:			List[Any]											=	torch.Generator(device='cpu'	).manual_seed(0	)
							snake_case_		:			List[Any]											=	pipe('anime turle'		,   generator=_A		,   output_type='np'	)

							snake_case_		:			Union[str, Any]											=	output.images[0]

							assert image.shape == (768, 768, 3)

							assert_mean_pixel_difference(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Optional[int]:







							"""simple docstring"""


							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							snake_case_		:			Any											=	StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'		,   torch_dtype=torch.floataa	)
							snake_case_		:			Tuple											=	pipe.to(_A	)
							pipe.set_progress_bar_config(disable=_A	)
							pipe.enable_attention_slicing()
							pipe.enable_sequential_cpu_offload()

							snake_case_		:			Dict											=	pipe(
							    'anime turtle'		,   prior_num_inference_steps=2		,   num_inference_steps=2		,   output_type='np'		,   )

							snake_case_		:			Optional[Any]											=	torch.cuda.max_memory_allocated()
							# make sure that less than 7 GB is allocated
							assert mem_bytes < 7 * 10**9








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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			str       =       "lilt"






						def __init__(      self      :      Any		,   _A      :      Dict=30522		,   _A      :      List[Any]=768		,   _A      :      str=12		,   _A      :      int=12		,   _A      :      Optional[int]=3072		,   _A      :      Optional[Any]="gelu"		,   _A      :      Dict=0.1		,   _A      :      Tuple=0.1		,   _A      :      str=512		,   _A      :      Union[str, Any]=2		,   _A      :      Union[str, Any]=0.0_2		,   _A      :      Optional[Any]=1E-12		,   _A      :      Optional[int]=0		,   _A      :      Dict="absolute"		,   _A      :      Any=None		,   _A      :      Optional[Any]=4		,   _A      :      Any=1024		,   **_A      :      Union[str, Any]		,   )							->    List[str]:







							"""simple docstring"""


							super().__init__(pad_token_id=_A		,   **_A	)

							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			Dict											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			List[str]											=	num_attention_heads
							snake_case_		:			Tuple											=	hidden_act
							snake_case_		:			Tuple											=	intermediate_size
							snake_case_		:			int											=	hidden_dropout_prob
							snake_case_		:			Tuple											=	attention_probs_dropout_prob
							snake_case_		:			Dict											=	max_position_embeddings
							snake_case_		:			Any											=	type_vocab_size
							snake_case_		:			Union[str, Any]											=	initializer_range
							snake_case_		:			Optional[int]											=	layer_norm_eps
							snake_case_		:			List[str]											=	position_embedding_type
							snake_case_		:			Optional[int]											=	classifier_dropout
							snake_case_		:			Dict											=	channel_shrink_ratio
							snake_case_		:			Tuple											=	max_ad_position_embeddings

