





"""simple docstring"""


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    (						_UpperCamelCase		,  _UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					StableUnCLIPPipeline
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					TEXT_TO_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE__							:List[str]	     =					TEXT_TO_IMAGE_BATCH_PARAMS
  SCREAMING_SNAKE_CASE__							:Any	     =					TEXT_TO_IMAGE_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE__							:str	     =					TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					False

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Tuple:
        _UpperCamelCase      :	Tuple						=       32
        _UpperCamelCase      :	List[str]						=       embedder_hidden_size

        # prior components

        torch.manual_seed(0	)
        _UpperCamelCase      :	Optional[int]						=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	int						=       CLIPTextModelWithProjection(
            CLIPTextConfig(
                bos_token_id=0					,   eos_token_id=2					,   hidden_size=__a					,   projection_dim=__a					,   intermediate_size=37					,   layer_norm_eps=1e-0_5					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	str						=       PriorTransformer(
            num_attention_heads=2					,   attention_head_dim=12					,   embedding_dim=__a					,   num_layers=1					,   )

        torch.manual_seed(0	)
        _UpperCamelCase      :	List[Any]						=       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	)
        _UpperCamelCase      :	str						=       StableUnCLIPImageNormalizer(embedding_dim=__a	)
        _UpperCamelCase      :	Optional[Any]						=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	int						=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	int						=       CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0					,   eos_token_id=2					,   hidden_size=__a					,   projection_dim=32					,   intermediate_size=37					,   layer_norm_eps=1e-0_5					,   num_attention_heads=4					,   num_hidden_layers=5					,   pad_token_id=1					,   vocab_size=1000					,   )	)

        torch.manual_seed(0	)
        _UpperCamelCase      :	Union[str, Any]						=       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	)
        _UpperCamelCase      :	Optional[Any]						=       DDIMScheduler(
            beta_schedule="scaled_linear"					,   beta_start=0.0_00_85					,   beta_end=0.0_12					,   prediction_type="v_prediction"					,   set_alpha_to_one=__a					,   steps_offset=1					,   )

        torch.manual_seed(0	)
        _UpperCamelCase      :	Union[str, Any]						=       AutoencoderKL()

        _UpperCamelCase      :	List[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    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       str					,   __a   :       List[str]=0	)		->				Tuple:
        if str(__a	).startswith("mps"	):
              _UpperCamelCase      :	Optional[Any]						=       torch.manual_seed(__a	)
        else:
              _UpperCamelCase      :	List[str]						=       torch.Generator(device=__a	).manual_seed(__a	)
        _UpperCamelCase      :	List[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    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Any:
        _UpperCamelCase      :	Optional[int]						=       torch_device == "cpu"

        self._test_attention_slicing_forward_pass(test_max_difference=__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Dict:
        _UpperCamelCase      :	Optional[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   ):







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Any:
        _UpperCamelCase      :	Any						=       load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"	)

        _UpperCamelCase      :	Tuple						=       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()

        _UpperCamelCase      :	List[str]						=       torch.Generator(device="cpu"	).manual_seed(0	)
        _UpperCamelCase      :	Optional[Any]						=       pipe("anime turle"					,   generator=__a					,   output_type="np"	)

        _UpperCamelCase      :	Dict						=       output.images[0]

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

        assert_mean_pixel_difference(__a					,   __a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Optional[int]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCamelCase      :	Optional[Any]						=       StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"					,   torch_dtype=torch.floataa	)
        _UpperCamelCase      :	Optional[int]						=       pipe.to(__a	)
        pipe.set_progress_bar_config(disable=__a	)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _UpperCamelCase      :	str						=       pipe(
            "anime turtle"					,   prior_num_inference_steps=2					,   num_inference_steps=2					,   output_type="np"					,   )

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







"""simple docstring"""


lowerCamelCase__				  =   "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase__				  =   [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase__				  =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
