




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 		A__				(   __SCREAMING_SNAKE_CASE		,     unittest.TestCase ):
	lowerCamelCase__		: Optional[Any]				    =UnCLIPPipeline
	lowerCamelCase__		: int				    =TEXT_TO_IMAGE_PARAMS - {
	    "negative_prompt",
	    "height",
	    "width",
	    "negative_prompt_embeds",
	    "guidance_scale",
	    "prompt_embeds",
	    "cross_attention_kwargs",
	}
	lowerCamelCase__		: Union[str, Any]				    =TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCamelCase__		: Dict				    =[
	    "generator",
	    "return_dict",
	    "prior_num_inference_steps",
	    "decoder_num_inference_steps",
	    "super_res_num_inference_steps",
	]
	lowerCamelCase__		: List[str]				    =False






	@property
	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						return 32






	@property
	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						return 32






	@property
	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						return self.time_input_dim






	@property
	def  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						return self.time_input_dim * 4






	@property
	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						return 100






	@property
	def  lowercase      (	self						)      ->   Optional[Any]:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)
						return tokenizer






	@property
	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						torch.manual_seed(0						)
						__magic_name__					:							Union[str, Any]		   =							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(lowerCamelCase						)






	@property
	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						torch.manual_seed(0						)

						__magic_name__					:							Optional[Any]		   =							{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 12,
						    '''embedding_dim''': self.text_embedder_hidden_size,
						    '''num_layers''': 1,
						}

						__magic_name__					:							Any		   =							PriorTransformer(**lowerCamelCase						)
						return model






	@property
	def  lowercase      (	self						)      ->   Optional[Any]:




						"""simple docstring"""






						torch.manual_seed(0						)

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

						__magic_name__					:							Optional[int]		   =							UnCLIPTextProjModel(**lowerCamelCase						)
						return model






	@property
	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						torch.manual_seed(0						)

						__magic_name__					:							Tuple		   =							{
						    '''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''',
						}

						__magic_name__					:							List[str]		   =							UNetaDConditionModel(**lowerCamelCase						)
						return model






	@property
	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""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  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						torch.manual_seed(0						)

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






	@property
	def  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						torch.manual_seed(1						)

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






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Dict		   =							self.dummy_prior
						__magic_name__					:							Optional[int]		   =							self.dummy_decoder
						__magic_name__					:							List[Any]		   =							self.dummy_text_proj
						__magic_name__					:							Union[str, Any]		   =							self.dummy_text_encoder
						__magic_name__					:							Tuple		   =							self.dummy_tokenizer
						__magic_name__					:							Union[str, Any]		   =							self.dummy_super_res_first
						__magic_name__					:							Optional[Any]		   =							self.dummy_super_res_last

						__magic_name__					:							Any		   =							UnCLIPScheduler(
						    variance_type='''fixed_small_log'''				,     prediction_type='''sample'''				,     num_train_timesteps=1000				,     clip_sample_range=5.0				,     )

						__magic_name__					:							List[Any]		   =							UnCLIPScheduler(
						    variance_type='''learned_range'''				,     prediction_type='''epsilon'''				,     num_train_timesteps=1000				,     )

						__magic_name__					:							int		   =							UnCLIPScheduler(
						    variance_type='''fixed_small_log'''				,     prediction_type='''epsilon'''				,     num_train_timesteps=1000				,     )

						__magic_name__					:							Dict		   =							{
						    '''prior''': prior,
						    '''decoder''': decoder,
						    '''text_proj''': text_proj,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''super_res_first''': super_res_first,
						    '''super_res_last''': super_res_last,
						    '''prior_scheduler''': prior_scheduler,
						    '''decoder_scheduler''': decoder_scheduler,
						    '''super_res_scheduler''': super_res_scheduler,
						}

						return components






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=0						)      ->   List[Any]:




						"""simple docstring"""






						if str(lowerCamelCase						).startswith('''mps'''						):
											__magic_name__					:							List[Any]		   =							torch.manual_seed(lowerCamelCase						)
						else:
											__magic_name__					:							Dict		   =							torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
						__magic_name__					:							Dict		   =							{
						    '''prompt''': '''horse''',
						    '''generator''': generator,
						    '''prior_num_inference_steps''': 2,
						    '''decoder_num_inference_steps''': 2,
						    '''super_res_num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}
						return inputs






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




						"""simple docstring"""






						__magic_name__					:							Any		   =							'''cpu'''

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

						__magic_name__					:							Tuple		   =							self.pipeline_class(**lowerCamelCase						)
						__magic_name__					:							List[str]		   =							pipe.to(lowerCamelCase						)

						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__					:							Optional[Any]		   =							pipe(**self.get_dummy_inputs(lowerCamelCase						)						)
						__magic_name__					:							Optional[Any]		   =							output.images

						__magic_name__					:							int		   =							pipe(
						    **self.get_dummy_inputs(lowerCamelCase						)				,     return_dict=lowerCamelCase				,     )[0]

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

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

						__magic_name__					:							Tuple		   =							np.array(
						    [
						        0.9_9_9_7,
						        0.9_9_8_8,
						        0.0_0_2_8,
						        0.9_9_9_7,
						        0.9_9_8_4,
						        0.9_9_6_5,
						        0.0_0_2_9,
						        0.9_9_8_6,
						        0.0_0_2_5,
						    ]						)

						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						)      ->   List[Any]:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							torch.device('''cpu'''						)





						class 		A__				:
							lowerCamelCase__		: Union[str, Any]				    =1

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

						__magic_name__					:							int		   =							self.pipeline_class(**lowerCamelCase						)
						__magic_name__					:							List[Any]		   =							pipe.to(lowerCamelCase						)

						__magic_name__					:							Any		   =							components['''prior''']
						__magic_name__					:							Optional[int]		   =							components['''decoder''']
						__magic_name__					:							Any		   =							components['''super_res_first''']
						__magic_name__					:							Any		   =							components['''tokenizer''']
						__magic_name__					:							Tuple		   =							components['''text_encoder''']

						__magic_name__					:							Tuple		   =							torch.Generator(device=lowerCamelCase						).manual_seed(0						)
						__magic_name__					:							Tuple		   =							prior.dtype
						__magic_name__					:							Tuple		   =							1

						__magic_name__					:							Union[str, Any]		   =							(batch_size, prior.config.embedding_dim)
						__magic_name__					:							Optional[Any]		   =							pipe.prepare_latents(
						    lowerCamelCase				,     dtype=lowerCamelCase				,     device=lowerCamelCase				,     generator=lowerCamelCase				,     latents=lowerCamelCase				,     scheduler=DummyScheduler()						)
						__magic_name__					:							List[str]		   =							(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
						__magic_name__					:							str		   =							pipe.prepare_latents(
						    lowerCamelCase				,     dtype=lowerCamelCase				,     device=lowerCamelCase				,     generator=lowerCamelCase				,     latents=lowerCamelCase				,     scheduler=DummyScheduler()						)

						__magic_name__					:							int		   =							(
						    batch_size,
						    super_res_first.config.in_channels // 2,
						    super_res_first.config.sample_size,
						    super_res_first.config.sample_size,
						)
						__magic_name__					:							Optional[int]		   =							pipe.prepare_latents(
						    lowerCamelCase				,     dtype=lowerCamelCase				,     device=lowerCamelCase				,     generator=lowerCamelCase				,     latents=lowerCamelCase				,     scheduler=DummyScheduler()						)

						pipe.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__					:							Tuple		   =							'''this is a prompt example'''

						__magic_name__					:							List[Any]		   =							torch.Generator(device=lowerCamelCase						).manual_seed(0						)
						__magic_name__					:							Union[str, Any]		   =							pipe(
						    [prompt]				,     generator=lowerCamelCase				,     prior_num_inference_steps=2				,     decoder_num_inference_steps=2				,     super_res_num_inference_steps=2				,     prior_latents=lowerCamelCase				,     decoder_latents=lowerCamelCase				,     super_res_latents=lowerCamelCase				,     output_type='''np'''				,     )
						__magic_name__					:							Optional[int]		   =							output.images

						__magic_name__					:							Optional[int]		   =							tokenizer(
						    lowerCamelCase				,     padding='''max_length'''				,     max_length=tokenizer.model_max_length				,     return_tensors='''pt'''				,     )
						__magic_name__					:							Union[str, Any]		   =							text_encoder(text_inputs.input_ids						)
						__magic_name__					:							Optional[Any]		   =							text_inputs.attention_mask

						__magic_name__					:							Optional[int]		   =							torch.Generator(device=lowerCamelCase						).manual_seed(0						)
						__magic_name__					:							List[Any]		   =							pipe(
						    generator=lowerCamelCase				,     prior_num_inference_steps=2				,     decoder_num_inference_steps=2				,     super_res_num_inference_steps=2				,     prior_latents=lowerCamelCase				,     decoder_latents=lowerCamelCase				,     super_res_latents=lowerCamelCase				,     text_model_output=lowerCamelCase				,     text_attention_mask=lowerCamelCase				,     output_type='''np'''				,     )[0]

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






	@skip_mps
	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""simple docstring"""






						__magic_name__					:							int		   =							torch_device == '''cpu'''

						self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase				,     expected_max_diff=0.0_1						)






	@skip_mps
	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Any		   =							torch_device == '''cpu'''
						__magic_name__					:							Optional[Any]		   =							True
						__magic_name__					:							List[str]		   =							[
						    '''prior_num_inference_steps''',
						    '''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  lowercase      (	self						)      ->   int:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							[
						    '''prior_num_inference_steps''',
						    '''decoder_num_inference_steps''',
						    '''super_res_num_inference_steps''',
						]

						if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											__magic_name__					:							Union[str, Any]		   =							[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  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def  lowercase      (	self						)      ->   Union[str, Any]:




						"""simple docstring"""






						return super().test_save_load_local()






	@skip_mps
	def  lowercase      (	self						)      ->   Optional[Any]:




						"""simple docstring"""






						return super().test_save_load_optional_components()





@nightly
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Optional[Any]:




						"""simple docstring"""






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






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




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/unclip/karlo_v1_alpha_horse_cpu.npy'''						)

						__magic_name__					:							Union[str, Any]		   =							UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''						)
						pipeline.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__					:							List[str]		   =							torch.manual_seed(0						)
						__magic_name__					:							str		   =							pipeline(
						    '''horse'''				,     num_images_per_prompt=1				,     generator=lowerCamelCase				,     output_type='''np'''				,     )

						__magic_name__					:							Tuple		   =							output.images[0]

						assert image.shape == (256, 256, 3)
						assert np.abs(expected_image - image						).max() < 1e-1





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






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




						"""simple docstring"""






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






	def  lowercase      (	self						)      ->   Tuple:




						"""simple docstring"""






						__magic_name__					:							Union[str, Any]		   =							load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/unclip/karlo_v1_alpha_horse_fp16.npy'''						)

						__magic_name__					:							Any		   =							UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''				,     torch_dtype=torch.floataa						)
						__magic_name__					:							List[str]		   =							pipeline.to(lowerCamelCase						)
						pipeline.set_progress_bar_config(disable=lowerCamelCase						)

						__magic_name__					:							Union[str, Any]		   =							torch.Generator(device='''cpu'''						).manual_seed(0						)
						__magic_name__					:							Any		   =							pipeline(
						    '''horse'''				,     generator=lowerCamelCase				,     output_type='''np'''				,     )

						__magic_name__					:							int		   =							output.images[0]

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

						assert_mean_pixel_difference(lowerCamelCase				,     lowerCamelCase						)






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




						"""simple docstring"""






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

						__magic_name__					:							str		   =							UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''				,     torch_dtype=torch.floataa						)
						__magic_name__					:							str		   =							pipe.to(lowerCamelCase						)
						pipe.set_progress_bar_config(disable=lowerCamelCase						)
						pipe.enable_attention_slicing()
						pipe.enable_sequential_cpu_offload()

						__magic_name__					:							int		   =							pipe(
						    '''horse'''				,     num_images_per_prompt=1				,     prior_num_inference_steps=2				,     decoder_num_inference_steps=2				,     super_res_num_inference_steps=2				,     output_type='''np'''				,     )

						__magic_name__					:							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


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Tuple				    ="yoso"






	def __init__(	self				,     lowerCamelCase=50265				,     lowerCamelCase=768				,     lowerCamelCase=12				,     lowerCamelCase=12				,     lowerCamelCase=3072				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0.1				,     lowerCamelCase=4096				,     lowerCamelCase=1				,     lowerCamelCase=0.0_2				,     lowerCamelCase=1e-12				,     lowerCamelCase="absolute"				,     lowerCamelCase=True				,     lowerCamelCase=9				,     lowerCamelCase=64				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=True				,     lowerCamelCase=1				,     lowerCamelCase=0				,     lowerCamelCase=2				,     **lowerCamelCase				,     )      ->   Tuple:




						"""simple docstring"""






						super().__init__(pad_token_id=lowerCamelCase				,     bos_token_id=lowerCamelCase				,     eos_token_id=lowerCamelCase				,     **lowerCamelCase						)

						__magic_name__					:							Union[str, Any]		   =							vocab_size
						__magic_name__					:							Union[str, Any]		   =							max_position_embeddings
						__magic_name__					:							Tuple		   =							hidden_size
						__magic_name__					:							Optional[Any]		   =							num_hidden_layers
						__magic_name__					:							Any		   =							num_attention_heads
						__magic_name__					:							List[str]		   =							intermediate_size
						__magic_name__					:							List[str]		   =							hidden_act
						__magic_name__					:							Tuple		   =							hidden_dropout_prob
						__magic_name__					:							str		   =							attention_probs_dropout_prob
						__magic_name__					:							str		   =							initializer_range
						__magic_name__					:							List[str]		   =							type_vocab_size
						__magic_name__					:							Dict		   =							layer_norm_eps
						__magic_name__					:							Any		   =							position_embedding_type
						__magic_name__					:							Optional[Any]		   =							use_expectation
						__magic_name__					:							Optional[Any]		   =							hash_code_len
						__magic_name__					:							Dict		   =							num_hash
						__magic_name__					:							str		   =							conv_window
						__magic_name__					:							Optional[Any]		   =							use_fast_hash
						__magic_name__					:							Union[str, Any]		   =							lsh_backward



