






"""simple docstring"""
import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import 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_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



@skip_mps
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Tuple    =				StableDiffusionPanoramaPipeline
	_A       :				Dict    =				TEXT_TO_IMAGE_PARAMS
	_A       :				Optional[int]    =				TEXT_TO_IMAGE_BATCH_PARAMS
	_A       :				Any    =				TEXT_TO_IMAGE_IMAGE_PARAMS
	_A       :				Dict    =				TEXT_TO_IMAGE_IMAGE_PARAMS





	def    lowerCamelCase(self							):
		torch.manual_seed(0							)
		A_		:						Tuple	    =   UNetaDConditionModel(
		    block_out_channels=(32, 64)     ,     layers_per_block=1     ,     sample_size=32     ,     in_channels=4     ,     out_channels=4     ,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,     cross_attention_dim=32     ,     )
		A_		:						str	    =   DDIMScheduler()
		torch.manual_seed(0							)
		A_		:						Dict	    =   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     ,     )
		torch.manual_seed(0							)
		A_		:						List[Any]	    =   CLIPTextConfig(
		    bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=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     ,     )
		A_		:						Optional[int]	    =   CLIPTextModel(lowerCAmelCase_							)
		A_		:						str	    =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

		A_		:						Optional[Any]	    =   {
		    """unet""": unet,
		    """scheduler""": scheduler,
		    """vae""": vae,
		    """text_encoder""": text_encoder,
		    """tokenizer""": tokenizer,
		    """safety_checker""": None,
		    """feature_extractor""": None,
		}
		return components





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=0							):
		A_		:						List[str]	    =   torch.manual_seed(lowerCAmelCase_							)
		A_		:						Any	    =   {
		    """prompt""": """a photo of the dolomites""",
		    """generator""": generator,
		    # Setting height and width to None to prevent OOMs on CPU.
		    """height""": None,
		    """width""": None,
		    """num_inference_steps""": 1,
		    """guidance_scale""": 6.0,
		    """output_type""": """numpy""",
		}
		return inputs





	def    lowerCamelCase(self							):
		A_		:						int	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						Tuple	    =   self.get_dummy_components()
		A_		:						List[Any]	    =   StableDiffusionPanoramaPipeline(**lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						str	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						str	    =   sd_pipe(**lowerCAmelCase_							).images
		A_		:						List[str]	    =   image[0, -3:, -3:, -1]
		assert image.shape == (1, 64, 64, 3)

		A_		:						List[Any]	    =   np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]							)

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





	def    lowerCamelCase(self							):
		super().test_inference_batch_consistent(batch_sizes=[1, 2]							)





	def    lowerCamelCase(self							):
		super().test_inference_batch_single_identical(batch_size=2     ,     expected_max_diff=3.2_5e-3							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						Union[str, Any]	    =   self.get_dummy_components()
		A_		:						List[str]	    =   StableDiffusionPanoramaPipeline(**lowerCAmelCase_							)
		A_		:						str	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						List[Any]	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						Dict	    =   """french fries"""
		A_		:						Union[str, Any]	    =   sd_pipe(**lowerCAmelCase_     ,     negative_prompt=lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   output.images
		A_		:						int	    =   image[0, -3:, -3:, -1]

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

		A_		:						int	    =   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]							)

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





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						List[Any]	    =   self.get_dummy_components()
		A_		:						Tuple	    =   StableDiffusionPanoramaPipeline(**lowerCAmelCase_							)
		A_		:						Optional[Any]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						Any	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						List[str]	    =   sd_pipe(**lowerCAmelCase_     ,     view_batch_size=2							)
		A_		:						Dict	    =   output.images
		A_		:						int	    =   image[0, -3:, -3:, -1]

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

		A_		:						Tuple	    =   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]							)

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





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						Tuple	    =   self.get_dummy_components()
		A_		:						Optional[Any]	    =   EulerAncestralDiscreteScheduler(
		    beta_start=0.00085     ,     beta_end=0.012     ,     beta_schedule="""scaled_linear"""							)
		A_		:						Optional[Any]	    =   StableDiffusionPanoramaPipeline(**lowerCAmelCase_							)
		A_		:						Optional[int]	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						str	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						str	    =   sd_pipe(**lowerCAmelCase_							).images
		A_		:						List[Any]	    =   image[0, -3:, -3:, -1]

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

		A_		:						Any	    =   np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]							)

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





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
		A_		:						int	    =   self.get_dummy_components()
		A_		:						List[Any]	    =   PNDMScheduler(
		    beta_start=0.00085     ,     beta_end=0.012     ,     beta_schedule="""scaled_linear"""     ,     skip_prk_steps=lowerCAmelCase_							)
		A_		:						Dict	    =   StableDiffusionPanoramaPipeline(**lowerCAmelCase_							)
		A_		:						str	    =   sd_pipe.to(lowerCAmelCase_							)
		sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

		A_		:						Any	    =   self.get_dummy_inputs(lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   sd_pipe(**lowerCAmelCase_							).images
		A_		:						Tuple	    =   image[0, -3:, -3:, -1]

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

		A_		:						List[Any]	    =   np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]							)

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






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





	"""simple docstring"""




	def    lowerCamelCase(self							):
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0							):
		A_		:						Optional[int]	    =   torch.manual_seed(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   {
		    """prompt""": """a photo of the dolomites""",
		    """generator""": generator,
		    """num_inference_steps""": 3,
		    """guidance_scale""": 7.5,
		    """output_type""": """numpy""",
		}
		return inputs





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   """stabilityai/stable-diffusion-2-base"""
		A_		:						Any	    =   DDIMScheduler.from_pretrained(lowerCAmelCase_     ,     subfolder="""scheduler"""							)
		A_		:						Optional[int]	    =   StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase_     ,     scheduler=lowerCAmelCase_     ,     safety_checker=lowerCAmelCase_							)
		pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						List[str]	    =   self.get_inputs()
		A_		:						Optional[Any]	    =   pipe(**lowerCAmelCase_							).images
		A_		:						Optional[Any]	    =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 2048, 3)

		A_		:						Optional[int]	    =   np.array(
		    [
		        0.36968392,
		        0.27025372,
		        0.32446766,
		        0.28379387,
		        0.36363274,
		        0.30733347,
		        0.27100027,
		        0.27054125,
		        0.25536096,
		    ]							)

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





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   StableDiffusionPanoramaPipeline.from_pretrained(
		    """stabilityai/stable-diffusion-2-base"""     ,     safety_checker=lowerCAmelCase_							)
		A_		:						Dict	    =   LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
		pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						List[str]	    =   self.get_inputs()
		A_		:						Dict	    =   pipe(**lowerCAmelCase_							).images
		A_		:						Tuple	    =   image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 2048, 3)

		A_		:						List[str]	    =   np.array(
		    [
		        [
		            0.0,
		            0.0,
		            0.0,
		            0.0,
		            0.0,
		            0.0,
		            0.0,
		            0.0,
		            0.0,
		        ]
		    ]							)

		assert np.abs(expected_slice - image_slice							).max() < 1e-3





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   0

		def callback_fn(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							) -> None:
			A_		:						Dict	    =   True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 1:
				A_		:						Union[str, Any]	    =   latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 256)
				A_		:						int	    =   latents[0, -3:, -3:, -1]

				A_		:						Dict	    =   np.array(
				    [
				        0.18681869,
				        0.33907816,
				        0.5361276,
				        0.14432865,
				        -0.02856611,
				        -0.73941123,
				        0.23397987,
				        0.47322682,
				        -0.37823164,
				    ]							)
				assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2
			elif step == 2:
				A_		:						List[str]	    =   latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 256)
				A_		:						List[str]	    =   latents[0, -3:, -3:, -1]

				A_		:						int	    =   np.array(
				    [
				        0.18539645,
				        0.33987248,
				        0.5378559,
				        0.14437142,
				        -0.02455261,
				        -0.7338317,
				        0.23990755,
				        0.47356272,
				        -0.3786505,
				    ]							)

				assert np.abs(latents_slice.flatten() - expected_slice							).max() < 5e-2

		A_		:						List[Any]	    =   False

		A_		:						Optional[Any]	    =   """stabilityai/stable-diffusion-2-base"""
		A_		:						Union[str, Any]	    =   DDIMScheduler.from_pretrained(lowerCAmelCase_     ,     subfolder="""scheduler"""							)
		A_		:						List[str]	    =   StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase_     ,     scheduler=lowerCAmelCase_     ,     safety_checker=lowerCAmelCase_							)
		A_		:						List[Any]	    =   pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing()

		A_		:						int	    =   self.get_inputs()
		pipe(**lowerCAmelCase_     ,     callback=lowerCAmelCase_     ,     callback_steps=1							)
		assert callback_fn.has_been_called
		assert number_of_steps == 3





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

		A_		:						Dict	    =   """stabilityai/stable-diffusion-2-base"""
		A_		:						Dict	    =   DDIMScheduler.from_pretrained(lowerCAmelCase_     ,     subfolder="""scheduler"""							)
		A_		:						Optional[Any]	    =   StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase_     ,     scheduler=lowerCAmelCase_     ,     safety_checker=lowerCAmelCase_							)
		A_		:						List[str]	    =   pipe.to(lowerCAmelCase_							)
		pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
		pipe.enable_attention_slicing(1							)
		pipe.enable_sequential_cpu_offload()

		A_		:						Optional[Any]	    =   self.get_inputs()
		A_		:						int	    =   pipe(**lowerCAmelCase_							)

		A_		:						Any	    =   torch.cuda.max_memory_allocated()
		# make sure that less than 5.2 GB is allocated
		assert mem_bytes < 5.5 * 10**9









"""simple docstring"""
import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:



							class      SCREAMING_SNAKE_CASE  :





								"""simple docstring"""




								@staticmethod
								def    lowerCamelCase(*lowerCAmelCase_     ,     **lowerCAmelCase_							):
									pass



@is_pipeline_test
@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[Any]    =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Union[str, Any]	    =   [
		    {
		        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							),
		        """question""": """How many cats are there?""",
		    },
		    {
		        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
		        """question""": """How many cats are there?""",
		    },
		]
		return vqa_pipeline, examples





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   vqa_pipeline(lowerCAmelCase_     ,     top_k=1							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		    ]     ,     )





	@require_torch
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Any	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						Any	    =   """How many cats are there?"""

		A_		:						Optional[int]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question="""How many cats are there?"""     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)





	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						int	    =   pipeline("""visual-question-answering"""     ,     model="""dandelin/vilt-b32-finetuned-vqa"""							)
		A_		:						Tuple	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						int	    =   """How many cats are there?"""

		A_		:						List[Any]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question=lowerCAmelCase_     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Tuple	    =   vqa_pipeline(
		    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2     ,     )





	@require_tf
	@unittest.skip("""Visual question answering not implemented in TF"""							)
	def    lowerCamelCase(self							):
		pass

