"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class  a__     ( _a       ,       _a       ,       unittest.TestCase						):
   snake_case_				=   StableDiffusionXLPipeline
   snake_case_				=   TEXT_TO_IMAGE_PARAMS
   snake_case_				=   TEXT_TO_IMAGE_BATCH_PARAMS
   snake_case_				=   TEXT_TO_IMAGE_IMAGE_PARAMS
   snake_case_				=   TEXT_TO_IMAGE_IMAGE_PARAMS


   def 						snake_case__						(  self	):
        '''simple docstring'''



        torch.manual_seed(0	)
        lowercase__								=				UNetaDConditionModel(
            block_out_channels=(32, 64),					layers_per_block=2,					sample_size=32,					in_channels=4,					out_channels=4,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),					attention_head_dim=(2, 4),					use_linear_projection=_UpperCAmelCase,					addition_embed_type="text_time",					addition_time_embed_dim=8,					transformer_layers_per_block=(1, 2),					projection_class_embeddings_input_dim=80,					cross_attention_dim=64,					)
        lowercase__								=				EulerDiscreteScheduler(
            beta_start=0.00_085,					beta_end=0.012,					steps_offset=1,					beta_schedule="scaled_linear",					timestep_spacing="leading",					)
        torch.manual_seed(0	)
        lowercase__								=				AutoencoderKL(
            block_out_channels=[32, 64],					in_channels=3,					out_channels=3,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],					latent_channels=4,					sample_size=128,					)
        torch.manual_seed(0	)
        lowercase__								=				CLIPTextConfig(
            bos_token_id=0,					eos_token_id=2,					hidden_size=32,					intermediate_size=37,					layer_norm_eps=1E-05,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1000,					hidden_act="gelu",					projection_dim=32,					)
        lowercase__								=				CLIPTextModel(_UpperCAmelCase	)
        lowercase__								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip",					local_files_only=_UpperCAmelCase	)

        lowercase__								=				CLIPTextModelWithProjection(_UpperCAmelCase	)
        lowercase__								=				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip",					local_files_only=_UpperCAmelCase	)

        lowercase__								=				{
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_a,
            "tokenizer_2": tokenizer_a,
            # "safety_checker": None,
            # "feature_extractor": None,
        }
        return components


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase=0	):
        '''simple docstring'''



        if str(_UpperCAmelCase	).startswith("mps"	):
             lowercase__								=				torch.manual_seed(_UpperCAmelCase	)
        else:
             lowercase__								=				torch.Generator(device=_UpperCAmelCase	).manual_seed(_UpperCAmelCase	)
        lowercase__								=				{
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 5.0,
            "output_type": "numpy",
        }
        return inputs


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
        lowercase__								=				self.get_dummy_components()
        lowercase__								=				StableDiffusionXLPipeline(**_UpperCAmelCase	)
        lowercase__								=				sd_pipe.to(_UpperCAmelCase	)
        sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase	)

        lowercase__								=				self.get_dummy_inputs(_UpperCAmelCase	)
        lowercase__								=				sd_pipe(**_UpperCAmelCase	).images
        lowercase__								=				image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase__								=				np.array([0.5_873, 0.6_128, 0.4_797, 0.5_122, 0.5_674, 0.4_639, 0.5_227, 0.5_149, 0.4_747]	)

        assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.get_dummy_components()
        lowercase__								=				StableDiffusionXLPipeline(**_UpperCAmelCase	)
        lowercase__								=				sd_pipe.to(_UpperCAmelCase	)
        lowercase__								=				sd_pipe.to(_UpperCAmelCase	)
        sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase	)

        # forward without prompt embeds
        lowercase__								=				self.get_dummy_inputs(_UpperCAmelCase	)
        lowercase__								=				3 * ["this is a negative prompt"]
        lowercase__								=				negative_prompt
        lowercase__								=				3 * [inputs["prompt"]]

        lowercase__								=				sd_pipe(**_UpperCAmelCase	)
        lowercase__								=				output.images[0, -3:, -3:, -1]

        # forward with prompt embeds
        lowercase__								=				self.get_dummy_inputs(_UpperCAmelCase	)
        lowercase__								=				3 * ["this is a negative prompt"]
        lowercase__								=				3 * [inputs.pop("prompt"	)]

        (
            (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    (
            lowercase__
        )			,    
        )								=				sd_pipe.encode_prompt(_UpperCAmelCase,					negative_prompt=_UpperCAmelCase	)

        lowercase__								=				sd_pipe(
            **_UpperCAmelCase,					prompt_embeds=_UpperCAmelCase,					negative_prompt_embeds=_UpperCAmelCase,					pooled_prompt_embeds=_UpperCAmelCase,					negative_pooled_prompt_embeds=_UpperCAmelCase,					)
        lowercase__								=				output.images[0, -3:, -3:, -1]

        # make sure that it's equal
        assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4


   def 						snake_case__						(  self	):
        '''simple docstring'''



        super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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


   def 						snake_case__						(  self	):
        '''simple docstring'''



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


   def 						snake_case__						(  self,					_UpperCAmelCase,					_UpperCAmelCase="cpu",					_UpperCAmelCase=torch.floataa,					_UpperCAmelCase=0	):
        '''simple docstring'''



        lowercase__								=				torch.Generator(device=_UpperCAmelCase	).manual_seed(_UpperCAmelCase	)
        lowercase__								=				np.random.RandomState(_UpperCAmelCase	).standard_normal((1, 4, 64, 64)	)
        lowercase__								=				torch.from_numpy(_UpperCAmelCase	).to(device=_UpperCAmelCase,					dtype=_UpperCAmelCase	)
        lowercase__								=				{
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"	)
        pipe.to(_UpperCAmelCase	)
        pipe.set_progress_bar_config(disable=_UpperCAmelCase	)

        lowercase__								=				self.get_inputs(_UpperCAmelCase	)
        lowercase__								=				pipe(**_UpperCAmelCase	).images
        lowercase__								=				image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        lowercase__								=				np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]	)
        assert np.abs(image_slice - expected_slice	).max() < 7E-3





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 lowerCAmelCase_:       List[Any]			     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




