





'''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 	_lowercase    (				_UpperCAmelCase		,       unittest.TestCase					):
   _UpperCamelCase									= UnCLIPPipeline
   _UpperCamelCase									= TEXT_TO_IMAGE_PARAMS - {
       "negative_prompt",
       "height",
       "width",
       "negative_prompt_embeds",
       "guidance_scale",
       "prompt_embeds",
       "cross_attention_kwargs",
   }
   _UpperCamelCase									= TEXT_TO_IMAGE_BATCH_PARAMS
   _UpperCamelCase									= [
       "generator",
       "return_dict",
       "prior_num_inference_steps",
       "decoder_num_inference_steps",
       "super_res_num_inference_steps",
   ]
   _UpperCamelCase									= False


   @property
   def        snake_case					(			self       ):
       return 32


   @property
   def        snake_case					(			self       ):
       return 32


   @property
   def        snake_case					(			self       ):
       return self.time_input_dim


   @property
   def        snake_case					(			self       ):
       return self.time_input_dim * 4


   @property
   def        snake_case					(			self       ):
       return 100


   @property
   def        snake_case					(			self       ):
       A							:							Optional[int]				=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
       return tokenizer


   @property
   def        snake_case					(			self       ):
       torch.manual_seed(0       )
       A							:							Optional[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-0_5     , num_attention_heads=4     , num_hidden_layers=5     , pad_token_id=1     , vocab_size=1_000     , )
       return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_       )


   @property
   def        snake_case					(			self       ):
       torch.manual_seed(0       )

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

       A							:							Any				=    PriorTransformer(**SCREAMING_SNAKE_CASE_       )
       return model


   @property
   def        snake_case					(			self       ):
       torch.manual_seed(0       )

       A							:							Optional[int]				=    {
           """clip_embeddings_dim""": self.text_embedder_hidden_size,
           """time_embed_dim""": self.time_embed_dim,
           """cross_attention_dim""": self.cross_attention_dim,
       }

       A							:							Optional[int]				=    UnCLIPTextProjModel(**SCREAMING_SNAKE_CASE_       )
       return model


   @property
   def        snake_case					(			self       ):
       torch.manual_seed(0       )

       A							:							Optional[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""",
       }

       A							:							List[str]				=    UNetaDConditionModel(**SCREAMING_SNAKE_CASE_       )
       return model


   @property
   def        snake_case					(			self       ):
       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       ):
       torch.manual_seed(0       )

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


   @property
   def        snake_case					(			self       ):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1       )

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


   def        snake_case					(			self       ):
       A							:							str				=    self.dummy_prior
       A							:							List[str]				=    self.dummy_decoder
       A							:							List[str]				=    self.dummy_text_proj
       A							:							Union[str, Any]				=    self.dummy_text_encoder
       A							:							List[str]				=    self.dummy_tokenizer
       A							:							Any				=    self.dummy_super_res_first
       A							:							List[str]				=    self.dummy_super_res_last

       A							:							Optional[Any]				=    UnCLIPScheduler(
           variance_type='''fixed_small_log'''     , prediction_type='''sample'''     , num_train_timesteps=1_000     , clip_sample_range=5.0     , )

       A							:							List[str]				=    UnCLIPScheduler(
           variance_type='''learned_range'''     , prediction_type='''epsilon'''     , num_train_timesteps=1_000     , )

       A							:							Union[str, Any]				=    UnCLIPScheduler(
           variance_type='''fixed_small_log'''     , prediction_type='''epsilon'''     , num_train_timesteps=1_000     , )

       A							:							Union[str, 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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=0       ):
       if str(SCREAMING_SNAKE_CASE_       ).startswith('''mps'''       ):
           A							:							str				=    torch.manual_seed(SCREAMING_SNAKE_CASE_       )
       else:
           A							:							Optional[Any]				=    torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
       A							:							str				=    {
           """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        snake_case					(			self       ):
       A							:							str				=    """cpu"""

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

       A							:							Dict				=    self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
       A							:							int				=    pipe.to(SCREAMING_SNAKE_CASE_       )

       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

       A							:							Union[str, Any]				=    pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )       )
       A							:							int				=    output.images

       A							:							str				=    pipe(
           **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )     , return_dict=SCREAMING_SNAKE_CASE_     , )[0]

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

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

       A							:							Dict				=    np.array(
           [
               0.9997,
               0.9988,
               0.0028,
               0.9997,
               0.9984,
               0.9965,
               0.0029,
               0.9986,
               0.0025,
           ]       )

       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       ):
       A							:							Dict				=    torch.device('''cpu'''       )


       class 	_lowercase    :
          _UpperCamelCase									= 1







       A							:							Dict				=    self.get_dummy_components()

       A							:							List[Any]				=    self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
       A							:							Optional[Any]				=    pipe.to(SCREAMING_SNAKE_CASE_       )

       A							:							Optional[int]				=    components["""prior"""]
       A							:							int				=    components["""decoder"""]
       A							:							Union[str, Any]				=    components["""super_res_first"""]
       A							:							Tuple				=    components["""tokenizer"""]
       A							:							Optional[int]				=    components["""text_encoder"""]

       A							:							Tuple				=    torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
       A							:							Optional[Any]				=    prior.dtype
       A							:							Dict				=    1

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

       A							:							Tuple				=    (
           batch_size,
           super_res_first.config.in_channels // 2,
           super_res_first.config.sample_size,
           super_res_first.config.sample_size,
       )
       A							:							Dict				=    pipe.prepare_latents(
           SCREAMING_SNAKE_CASE_     , dtype=SCREAMING_SNAKE_CASE_     , device=SCREAMING_SNAKE_CASE_     , generator=SCREAMING_SNAKE_CASE_     , latents=SCREAMING_SNAKE_CASE_     , scheduler=DummyScheduler()       )

       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

       A							:							Any				=    """this is a prompt example"""

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

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

       A							:							Optional[int]				=    torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(0       )
       A							:							Dict				=    pipe(
           generator=SCREAMING_SNAKE_CASE_     , prior_num_inference_steps=2     , decoder_num_inference_steps=2     , super_res_num_inference_steps=2     , prior_latents=SCREAMING_SNAKE_CASE_     , decoder_latents=SCREAMING_SNAKE_CASE_     , super_res_latents=SCREAMING_SNAKE_CASE_     , text_model_output=SCREAMING_SNAKE_CASE_     , text_attention_mask=SCREAMING_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        snake_case					(			self       ):
       A							:							Dict				=    torch_device == """cpu"""

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


   @skip_mps
   def        snake_case					(			self       ):
       A							:							List[str]				=    torch_device == """cpu"""
       A							:							List[Any]				=    True
       A							:							int				=    [
           """prior_num_inference_steps""",
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

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


   def        snake_case					(			self       ):
       A							:							Dict				=    [
           """prior_num_inference_steps""",
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       if torch_device == "mps":
           # TODO: MPS errors with larger batch sizes
           A							:							str				=    [2, 3]
           self._test_inference_batch_consistent(
               batch_sizes=SCREAMING_SNAKE_CASE_     , additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_     , )
       else:
           self._test_inference_batch_consistent(
               additional_params_copy_to_batched_inputs=SCREAMING_SNAKE_CASE_       )


   @skip_mps
   def        snake_case					(			self       ):
       return super().test_dict_tuple_outputs_equivalent()


   @skip_mps
   def        snake_case					(			self       ):
       return super().test_save_load_local()


   @skip_mps
   def        snake_case					(			self       ):
       return super().test_save_load_optional_components()







@nightly
class 	_lowercase    (				unittest.TestCase					):


   def        snake_case					(			self       ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def        snake_case					(			self       ):
       A							:							Union[str, Any]				=    load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_horse_cpu.npy'''       )

       A							:							Dict				=    UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''       )
       pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

       A							:							Dict				=    torch.manual_seed(0       )
       A							:							Tuple				=    pipeline(
           '''horse'''     , num_images_per_prompt=1     , generator=SCREAMING_SNAKE_CASE_     , output_type='''np'''     , )

       A							:							str				=    output.images[0]

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







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


   def        snake_case					(			self       ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


   def        snake_case					(			self       ):
       A							:							Any				=    load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/unclip/karlo_v1_alpha_horse_fp16.npy'''       )

       A							:							int				=    UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     , torch_dtype=torch.floataa       )
       A							:							str				=    pipeline.to(SCREAMING_SNAKE_CASE_       )
       pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

       A							:							int				=    torch.Generator(device='''cpu'''       ).manual_seed(0       )
       A							:							Dict				=    pipeline(
           '''horse'''     , generator=SCREAMING_SNAKE_CASE_     , output_type='''np'''     , )

       A							:							Optional[Any]				=    output.images[0]

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

       assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_       )


   def        snake_case					(			self       ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       A							:							str				=    UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''     , torch_dtype=torch.floataa       )
       A							:							Union[str, Any]				=    pipe.to(SCREAMING_SNAKE_CASE_       )
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       A							:							Optional[Any]				=    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'''     , )

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




import sys



def      __lowerCAmelCase    (				snake_case						:     int    ,   snake_case						:     list[int]			)							->				int:
   if not numbers:
      return 0
   if target == 0 and target in numbers:
      return 0
   if not isinstance(snake_case    ,   (list, tuple)			) or not all(
       isinstance(snake_case    ,   snake_case			) for number in numbers			):
      raise ValueError("""numbers must be an iterable of integers"""			)

   __lowerCamelCase:					Union[str, Any]	 =						0
   __lowerCamelCase:					str	 =						sys.maxsize

   while right < len(snake_case			):
      curr_sum += numbers[right]
      while curr_sum >= target and left <= right:
         __lowerCamelCase:					str	 =						min(snake_case    ,   right - left + 1			)
         curr_sum -= numbers[left]
         left += 1
      right += 1

   return 0 if min_len == sys.maxsize else min_len


