




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






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






   @property
   def  _a						(	self   :       int     )->			int:
     return 32






   @property
   def  _a						(	self   :       str     )->			List[Any]:
     return self.time_input_dim






   @property
   def  _a						(	self   :       Optional[Any]     )->			Optional[Any]:
     return self.time_input_dim * 4






   @property
   def  _a						(	self   :       int     )->			int:
     return 1_00






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






   @property
   def  _a						(	self   :       Optional[int]     )->			List[str]:
     torch.manual_seed(0     )
     _lowercase: List[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=10_00						,					)
     return CLIPTextModelWithProjection(UpperCamelCase_     )






   @property
   def  _a						(	self   :       Optional[Any]     )->			List[str]:
     torch.manual_seed(0     )

     _lowercase: int						     =    {
         'num_attention_heads': 2,
         'attention_head_dim': 12,
         'embedding_dim': self.text_embedder_hidden_size,
         'num_layers': 1,
     }

     _lowercase: Tuple						     =    PriorTransformer(**UpperCamelCase_     )
     return model






   @property
   def  _a						(	self   :       str     )->			int:
     torch.manual_seed(0     )

     _lowercase: Optional[Any]						     =    {
         'clip_embeddings_dim': self.text_embedder_hidden_size,
         'time_embed_dim': self.time_embed_dim,
         'cross_attention_dim': self.cross_attention_dim,
     }

     _lowercase: Tuple						     =    UnCLIPTextProjModel(**UpperCamelCase_     )
     return model






   @property
   def  _a						(	self   :       Any     )->			str:
     torch.manual_seed(0     )

     _lowercase: List[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',
     }

     _lowercase: Optional[int]						     =    UNetaDConditionModel(**UpperCamelCase_     )
     return model






   @property
   def  _a						(	self   :       Any     )->			List[str]:
     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  _a						(	self   :       int     )->			Tuple:
     torch.manual_seed(0     )

     _lowercase: Dict						     =    UNetaDModel(**self.dummy_super_res_kwargs     )
     return model






   @property
   def  _a						(	self   :       Tuple     )->			str:
     # seeded differently to get different unet than `self.dummy_super_res_first`
     torch.manual_seed(1     )

     _lowercase: List[Any]						     =    UNetaDModel(**self.dummy_super_res_kwargs     )
     return model






   def  _a						(	self   :       Dict     )->			Any:
     _lowercase: str						     =    self.dummy_prior
     _lowercase: Union[str, Any]						     =    self.dummy_decoder
     _lowercase: Any						     =    self.dummy_text_proj
     _lowercase: List[str]						     =    self.dummy_text_encoder
     _lowercase: List[str]						     =    self.dummy_tokenizer
     _lowercase: List[Any]						     =    self.dummy_super_res_first
     _lowercase: Optional[Any]						     =    self.dummy_super_res_last

     _lowercase: Tuple						     =    UnCLIPScheduler(
         variance_type='fixed_small_log'						,					prediction_type='sample'						,					num_train_timesteps=10_00						,					clip_sample_range=5.0						,					)

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

     _lowercase: int						     =    UnCLIPScheduler(
         variance_type='fixed_small_log'						,					prediction_type='epsilon'						,					num_train_timesteps=10_00						,					)

     _lowercase: List[Any]						     =    {
         '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  _a						(	self   :       int						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Union[str, Any]=0     )->			int:
     if str(UpperCamelCase_     ).startswith('mps'     ):
       _lowercase: Dict						     =    torch.manual_seed(UpperCamelCase_     )
     else:
       _lowercase: Optional[Any]						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    {
         '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  _a						(	self   :       Union[str, Any]     )->			Optional[Any]:
     _lowercase: Any						     =    'cpu'

     _lowercase: int						     =    self.get_dummy_components()

     _lowercase: Optional[Any]						     =    self.pipeline_class(**UpperCamelCase_     )
     _lowercase: str						     =    pipe.to(UpperCamelCase_     )

     pipe.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Any						     =    pipe(**self.get_dummy_inputs(UpperCamelCase_     )     )
     _lowercase: Optional[Any]						     =    output.images

     _lowercase: Union[str, Any]						     =    pipe(
         **self.get_dummy_inputs(UpperCamelCase_     )						,					return_dict=UpperCamelCase_						,					)[0]

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

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

     _lowercase: List[Any]						     =    np.array(
         [
             0.9_997,
             0.9_988,
             0.0_028,
             0.9_997,
             0.9_984,
             0.9_965,
             0.0_029,
             0.9_986,
             0.0_025,
         ]     )

     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  _a						(	self   :       List[Any]     )->			Union[str, Any]:
     _lowercase: Optional[Any]						     =    torch.device('cpu'     )

     class      UpperCAmelCase						:
        __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					1
     _lowercase: str						     =    self.get_dummy_components()

     _lowercase: str						     =    self.pipeline_class(**UpperCamelCase_     )
     _lowercase: Any						     =    pipe.to(UpperCamelCase_     )

     _lowercase: Any						     =    components['prior']
     _lowercase: List[str]						     =    components['decoder']
     _lowercase: str						     =    components['super_res_first']
     _lowercase: List[str]						     =    components['tokenizer']
     _lowercase: List[str]						     =    components['text_encoder']

     _lowercase: Tuple						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(0     )
     _lowercase: str						     =    prior.dtype
     _lowercase: Tuple						     =    1

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

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

     pipe.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Optional[int]						     =    'this is a prompt example'

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

     _lowercase: int						     =    tokenizer(
         UpperCamelCase_						,					padding='max_length'						,					max_length=tokenizer.model_max_length						,					return_tensors='pt'						,					)
     _lowercase: Any						     =    text_encoder(text_inputs.input_ids     )
     _lowercase: List[Any]						     =    text_inputs.attention_mask

     _lowercase: int						     =    torch.Generator(device=UpperCamelCase_     ).manual_seed(0     )
     _lowercase: List[str]						     =    pipe(
         generator=UpperCamelCase_						,					prior_num_inference_steps=2						,					decoder_num_inference_steps=2						,					super_res_num_inference_steps=2						,					prior_latents=UpperCamelCase_						,					decoder_latents=UpperCamelCase_						,					super_res_latents=UpperCamelCase_						,					text_model_output=UpperCamelCase_						,					text_attention_mask=UpperCamelCase_						,					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  _a						(	self   :       List[Any]     )->			int:
     _lowercase: Any						     =    torch_device == 'cpu'

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_						,					expected_max_diff=0.01     )






   @skip_mps
   def  _a						(	self   :       Any     )->			Optional[int]:
     _lowercase: str						     =    torch_device == 'cpu'
     _lowercase: str						     =    True
     _lowercase: str						     =    [
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

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






   def  _a						(	self   :       Union[str, Any]     )->			List[Any]:
     _lowercase: List[str]						     =    [
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]

     if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       _lowercase: List[Any]						     =    [2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=UpperCamelCase_						,					additional_params_copy_to_batched_inputs=UpperCamelCase_						,					)
     else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=UpperCamelCase_     )






   @skip_mps
   def  _a						(	self   :       List[Any]     )->			Optional[int]:
     return super().test_dict_tuple_outputs_equivalent()






   @skip_mps
   def  _a						(	self   :       Dict     )->			Dict:
     return super().test_save_load_local()






   @skip_mps
   def  _a						(	self   :       Tuple     )->			str:
     return super().test_save_load_optional_components()

@nightly
class      UpperCAmelCase						(			unittest.TestCase   ):






   def  _a						(	self   :       Tuple     )->			List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def  _a						(	self   :       Optional[Any]     )->			Tuple:
     _lowercase: int						     =    load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/unclip/karlo_v1_alpha_horse_cpu.npy'     )

     _lowercase: int						     =    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'     )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Tuple						     =    torch.manual_seed(0     )
     _lowercase: List[Any]						     =    pipeline(
         'horse'						,					num_images_per_prompt=1						,					generator=UpperCamelCase_						,					output_type='np'						,					)

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

     assert image.shape == (2_56, 2_56, 3)
     assert np.abs(expected_image - image     ).max() < 1E-1

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






   def  _a						(	self   :       Optional[int]     )->			List[str]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def  _a						(	self   :       Tuple     )->			List[Any]:
     _lowercase: Dict						     =    load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/unclip/karlo_v1_alpha_horse_fp16.npy'     )

     _lowercase: Any						     =    UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'						,					torch_dtype=torch.floataa     )
     _lowercase: Optional[int]						     =    pipeline.to(UpperCamelCase_     )
     pipeline.set_progress_bar_config(disable=UpperCamelCase_     )

     _lowercase: Any						     =    torch.Generator(device='cpu'     ).manual_seed(0     )
     _lowercase: Tuple						     =    pipeline(
         'horse'						,					generator=UpperCamelCase_						,					output_type='np'						,					)

     _lowercase: Tuple						     =    output.images[0]

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

     assert_mean_pixel_difference(UpperCamelCase_						,					UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			int:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

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

     _lowercase: Tuple						     =    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'						,					)

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






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      UpperCAmelCase						(			A   ):






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple     )->			Any:
     super().__init__()
     self.register_modules(unet=UpperCamelCase_						,					scheduler=UpperCamelCase_     )






   @torch.no_grad()
   def __call__(	self   :       Optional[int]						,					UpperCamelCase_   :       int = 1						,					UpperCamelCase_   :       Optional[torch.Generator] = None						,					UpperCamelCase_   :       int = 50						,					UpperCamelCase_   :       Optional[str] = "pil"						,					UpperCamelCase_   :       bool = True						,					**UpperCamelCase_   :       Dict						,					)->			Union[ImagePipelineOutput, Tuple]:
     _lowercase: List[str]						     =    torch.randn(
         (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=UpperCamelCase_						,					)
     _lowercase: str						     =    image.to(self.device     )

     # set step values
     self.scheduler.set_timesteps(UpperCamelCase_     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
       # 1. predict noise model_output
       _lowercase: int						     =    self.unet(UpperCamelCase_						,					UpperCamelCase_     ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       _lowercase: List[Any]						     =    self.scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ).prev_sample

     _lowercase: str						     =    (image / 2 + 0.5).clamp(0						,					1     )
     _lowercase: List[str]						     =    image.cpu().permute(0						,					2						,					3						,					1     ).numpy()
     if output_type == "pil":
       _lowercase: Any						     =    self.numpy_to_pil(UpperCamelCase_     )

     if not return_dict:
       return (image,), "This is a local test"

     return ImagePipelineOutput(images=UpperCamelCase_     ), "This is a local test"
