'''simple docstring'''


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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:List[str]		       =						UnCLIPPipeline
  lowerCamelCase_						:List[Any]		       =						TEXT_TO_IMAGE_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
      '''guidance_scale''',
      '''prompt_embeds''',
      '''cross_attention_kwargs''',
  }
  lowerCamelCase_						:Any		       =						TEXT_TO_IMAGE_BATCH_PARAMS
  lowerCamelCase_						:Optional[Any]		       =						[
      '''generator''',
      '''return_dict''',
      '''prior_num_inference_steps''',
      '''decoder_num_inference_steps''',
      '''super_res_num_inference_steps''',
  ]
  lowerCamelCase_						:str		       =						False


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return 3_2


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return 3_2


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self.time_input_dim


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return self.time_input_dim * 4


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return 1_0_0


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
       return tokenizer


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)
       UpperCAmelCase_				:  str  = 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=3_7 ,						layer_norm_eps=1E-05 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1_0_0_0 ,						)
       return CLIPTextModelWithProjection(snake_case_		)


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)

       UpperCAmelCase_				:  int  = {
           'num_attention_heads': 2,
           'attention_head_dim': 1_2,
           'embedding_dim': self.text_embedder_hidden_size,
           'num_layers': 1,
       }

       UpperCAmelCase_				:  Dict  = PriorTransformer(**snake_case_		)
       return model


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)

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

       UpperCAmelCase_				:  Union[str, Any]  = UnCLIPTextProjModel(**snake_case_		)
       return model


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(0		)

       UpperCAmelCase_				:  Optional[int]  = {
           'sample_size': 3_2,
           # 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_				:  Optional[Any]  = UNetaDConditionModel(**snake_case_		)
       return model


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return {
           "sample_size": 6_4,
           "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		):




       '''simple docstring'''

       torch.manual_seed(0		)

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


  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       torch.manual_seed(1		)

       UpperCAmelCase_				:  Tuple  = UNetaDModel(**self.dummy_super_res_kwargs		)
       return model


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = self.dummy_prior
       UpperCAmelCase_				:  Tuple  = self.dummy_decoder
       UpperCAmelCase_				:  Dict  = self.dummy_text_proj
       UpperCAmelCase_				:  Dict  = self.dummy_text_encoder
       UpperCAmelCase_				:  Tuple  = self.dummy_tokenizer
       UpperCAmelCase_				:  Dict  = self.dummy_super_res_first
       UpperCAmelCase_				:  List[str]  = self.dummy_super_res_last

       UpperCAmelCase_				:  Optional[int]  = UnCLIPScheduler(
           variance_type='fixed_small_log' ,						prediction_type='sample' ,						num_train_timesteps=1_0_0_0 ,						clip_sample_range=5.0 ,						)

       UpperCAmelCase_				:  str  = UnCLIPScheduler(
           variance_type='learned_range' ,						prediction_type='epsilon' ,						num_train_timesteps=1_0_0_0 ,						)

       UpperCAmelCase_				:  Union[str, Any]  = UnCLIPScheduler(
           variance_type='fixed_small_log' ,						prediction_type='epsilon' ,						num_train_timesteps=1_0_0_0 ,						)

       UpperCAmelCase_				:  str  = {
           '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 _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_=0		):




       '''simple docstring'''

       if str(snake_case_		).startswith('mps'		):
            UpperCAmelCase_				:  str  = torch.manual_seed(snake_case_		)
       else:
            UpperCAmelCase_				:  Dict  = torch.Generator(device=snake_case_		).manual_seed(snake_case_		)
       UpperCAmelCase_				:  int  = {
           '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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = 'cpu'

       UpperCAmelCase_				:  List[Any]  = self.get_dummy_components()

       UpperCAmelCase_				:  str  = self.pipeline_class(**snake_case_		)
       UpperCAmelCase_				:  Any  = pipe.to(snake_case_		)

       pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  int  = pipe(**self.get_dummy_inputs(snake_case_		)		)
       UpperCAmelCase_				:  List[str]  = output.images

       UpperCAmelCase_				:  Dict  = pipe(
           **self.get_dummy_inputs(snake_case_		) ,						return_dict=snake_case_ ,						)[0]

       UpperCAmelCase_				:  int  = image[0, -3:, -3:, -1]
       UpperCAmelCase_				:  Optional[Any]  = image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCAmelCase_				:  Union[str, Any]  = np.array(
           [
               0.99_97,
               0.99_88,
               0.00_28,
               0.99_97,
               0.99_84,
               0.99_65,
               0.00_29,
               0.99_86,
               0.00_25,
           ]		)

       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		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = torch.device('cpu'		)






       class 	__SCREAMING_SNAKE_CASE		:







         '''simple docstring'''






         lowerCamelCase_						:List[Any]		       =						1







       UpperCAmelCase_				:  Tuple  = self.get_dummy_components()

       UpperCAmelCase_				:  int  = self.pipeline_class(**snake_case_		)
       UpperCAmelCase_				:  Optional[int]  = pipe.to(snake_case_		)

       UpperCAmelCase_				:  Optional[Any]  = components['prior']
       UpperCAmelCase_				:  Dict  = components['decoder']
       UpperCAmelCase_				:  Tuple  = components['super_res_first']
       UpperCAmelCase_				:  Optional[Any]  = components['tokenizer']
       UpperCAmelCase_				:  Union[str, Any]  = components['text_encoder']

       UpperCAmelCase_				:  Optional[int]  = torch.Generator(device=snake_case_		).manual_seed(0		)
       UpperCAmelCase_				:  Tuple  = prior.dtype
       UpperCAmelCase_				:  Tuple  = 1

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

       UpperCAmelCase_				:  Any  = (
           batch_size,
           super_res_first.config.in_channels // 2,
           super_res_first.config.sample_size,
           super_res_first.config.sample_size,
       )
       UpperCAmelCase_				:  str  = pipe.prepare_latents(
           snake_case_ ,						dtype=snake_case_ ,						device=snake_case_ ,						generator=snake_case_ ,						latents=snake_case_ ,						scheduler=DummyScheduler()		)

       pipe.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  List[Any]  = 'this is a prompt example'

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

       UpperCAmelCase_				:  str  = tokenizer(
           snake_case_ ,						padding='max_length' ,						max_length=tokenizer.model_max_length ,						return_tensors='pt' ,						)
       UpperCAmelCase_				:  Dict  = text_encoder(text_inputs.input_ids		)
       UpperCAmelCase_				:  List[Any]  = text_inputs.attention_mask

       UpperCAmelCase_				:  Tuple  = torch.Generator(device=snake_case_		).manual_seed(0		)
       UpperCAmelCase_				:  Dict  = pipe(
           generator=snake_case_ ,						prior_num_inference_steps=2 ,						decoder_num_inference_steps=2 ,						super_res_num_inference_steps=2 ,						prior_latents=snake_case_ ,						decoder_latents=snake_case_ ,						super_res_latents=snake_case_ ,						text_model_output=snake_case_ ,						text_attention_mask=snake_case_ ,						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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = torch_device == 'cpu'

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


  @skip_mps
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = torch_device == 'cpu'
       UpperCAmelCase_				:  str  = True
       UpperCAmelCase_				:  Union[str, Any]  = [
           'prior_num_inference_steps',
           'decoder_num_inference_steps',
           'super_res_num_inference_steps',
       ]

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[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
            UpperCAmelCase_				:  int  = [2, 3]
            self._test_inference_batch_consistent(
                batch_sizes=snake_case_ ,						additional_params_copy_to_batched_inputs=snake_case_ ,						)
       else:
            self._test_inference_batch_consistent(
                additional_params_copy_to_batched_inputs=snake_case_		)


  @skip_mps
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return super().test_dict_tuple_outputs_equivalent()


  @skip_mps
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return super().test_save_load_local()






  @skip_mps
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return super().test_save_load_optional_components()







@nightly
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[int]  = UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'		)
       pipeline.set_progress_bar_config(disable=snake_case_		)

       UpperCAmelCase_				:  Union[str, Any]  = torch.manual_seed(0		)
       UpperCAmelCase_				:  Tuple  = pipeline(
           'horse' ,						num_images_per_prompt=1 ,						generator=snake_case_ ,						output_type='np' ,						)

       UpperCAmelCase_				:  Dict  = output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)
       assert np.abs(expected_image - image		).max() < 1E-1







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







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/unclip/karlo_v1_alpha_horse_fp16.npy'		)

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

       UpperCAmelCase_				:  Optional[Any]  = torch.Generator(device='cpu'		).manual_seed(0		)
       UpperCAmelCase_				:  Optional[Any]  = pipeline(
           'horse' ,						generator=snake_case_ ,						output_type='np' ,						)

       UpperCAmelCase_				:  Any  = output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       assert_mean_pixel_difference(snake_case_ ,						snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[Any]  = UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha' ,						torch_dtype=torch.floataa		)
       UpperCAmelCase_				:  int  = pipe.to(snake_case_		)
       pipe.set_progress_bar_config(disable=snake_case_		)
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       UpperCAmelCase_				:  List[str]  = 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' ,						)

       UpperCAmelCase_				:  List[str]  = torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 1_0**9










class     __a     :
 def __init__(  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  str         =  []
        lowercase__:  Any         =  0
        lowercase__:  int         =  0
 def __str__(  self					)   ->							Any:




        '''simple docstring'''
        lowercase__:  str         =  '<' + str(self.entries					)[1:-1] + '>'
        return printed
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        self.entries.append(lowerCAmelCase__					)
        lowercase__:  List[str]         =  self.length + 1
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__:  Any         =  self.length - 1
        lowercase__:  Optional[int]         =  self.entries[self.front]
        # self.front-=1
        # self.entries = self.entries[self.front:]
        lowercase__:  int         =  self.entries[1:]
        return dequeued
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__					)   ->							List[str]:




        '''simple docstring'''
        for _ in range(lowerCAmelCase__					):
               self.put(self.get()					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Union[str, Any]:




        '''simple docstring'''
        return self.entries[0]
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[int]:




        '''simple docstring'''
        return self.length


