


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class     __lowerCAmelCase     ( a ,  a ,  a ,  unittest.TestCase					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       StableDiffusionImageVariationPipeline
  _SCREAMING_SNAKE_CASE		    =       IMAGE_VARIATION_PARAMS
  _SCREAMING_SNAKE_CASE		    =       IMAGE_VARIATION_BATCH_PARAMS
  _SCREAMING_SNAKE_CASE		    =       frozenset([]					)
  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  _SCREAMING_SNAKE_CASE		    =       frozenset([]					)



  def 					lowerCAmelCase__  (     self       :       Dict  )   -> List[str]:




     """simple docstring"""
     torch.manual_seed(0  )
     snake_case_					    =					UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=4    ,				out_channels=4    ,				down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,				up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,				cross_attention_dim=3_2    ,				)
     snake_case_					    =					PNDMScheduler(skip_prk_steps=_lowerCAmelCase  )
     torch.manual_seed(0  )
     snake_case_					    =					AutoencoderKL(
         block_out_channels=[3_2, 6_4]    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,				up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,				latent_channels=4    ,				)
     torch.manual_seed(0  )
     snake_case_					    =					CLIPVisionConfig(
         hidden_size=3_2    ,				projection_dim=3_2    ,				intermediate_size=3_7    ,				layer_norm_eps=1e-05    ,				num_attention_heads=4    ,				num_hidden_layers=5    ,				image_size=3_2    ,				patch_size=4    ,				)
     snake_case_					    =					CLIPVisionModelWithProjection(_lowerCAmelCase  )
     snake_case_					    =					CLIPImageProcessor(crop_size=3_2    ,				size=3_2  )

     snake_case_					    =					{
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "image_encoder": image_encoder,
         "feature_extractor": feature_extractor,
         "safety_checker": None,
     }
     return components



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				_lowerCAmelCase       :       Optional[int]    ,				_lowerCAmelCase       :       int=0  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					floats_tensor((1, 3, 3_2, 3_2)    ,				rng=random.Random(_lowerCAmelCase  )  )
     snake_case_					    =					image.cpu().permute(0    ,				2    ,				3    ,				1  )[0]
     snake_case_					    =					Image.fromarray(np.uinta(_lowerCAmelCase  )  ).convert("RGB"  ).resize((3_2, 3_2)  )
     if str(_lowerCAmelCase  ).startswith("mps"  ):
        snake_case_					    =					torch.manual_seed(_lowerCAmelCase  )
     else:
        snake_case_					    =					torch.Generator(device=_lowerCAmelCase  ).manual_seed(_lowerCAmelCase  )
     snake_case_					    =					{
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }
     return inputs



  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> Optional[Any]:




     """simple docstring"""
     snake_case_					    =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_					    =					self.get_dummy_components()
     snake_case_					    =					StableDiffusionImageVariationPipeline(**_lowerCAmelCase  )
     snake_case_					    =					sd_pipe.to(_lowerCAmelCase  )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

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

     assert image.shape == (1, 6_4, 6_4, 3)
     snake_case_					    =					np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921]  )

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



  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> Tuple:




     """simple docstring"""
     snake_case_					    =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     snake_case_					    =					self.get_dummy_components()
     snake_case_					    =					StableDiffusionImageVariationPipeline(**_lowerCAmelCase  )
     snake_case_					    =					sd_pipe.to(_lowerCAmelCase  )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

     snake_case_					    =					self.get_dummy_inputs(_lowerCAmelCase  )
     snake_case_					    =					2 * [inputs["image"]]
     snake_case_					    =					sd_pipe(**_lowerCAmelCase  )

     snake_case_					    =					output.images

     snake_case_					    =					image[-1, -3:, -3:, -1]

     assert image.shape == (2, 6_4, 6_4, 3)
     snake_case_					    =					np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289]  )

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



  def 					lowerCAmelCase__  (     self       :       int  )   -> List[str]:




     """simple docstring"""
     super().test_inference_batch_single_identical(expected_max_diff=3e-3  )






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






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       Union[str, Any]  )   -> List[str]:




     """simple docstring"""
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



  def 					lowerCAmelCase__  (     self       :       Tuple    ,				_lowerCAmelCase       :       str    ,				_lowerCAmelCase       :       int="cpu"    ,				_lowerCAmelCase       :       Optional[Any]=torch.floataa    ,				_lowerCAmelCase       :       Optional[int]=0  )   -> int:




     """simple docstring"""
     snake_case_					    =					torch.Generator(device=_lowerCAmelCase  ).manual_seed(_lowerCAmelCase  )
     snake_case_					    =					load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
         "/stable_diffusion_imgvar/input_image_vermeer.png"  )
     snake_case_					    =					np.random.RandomState(_lowerCAmelCase  ).standard_normal((1, 4, 6_4, 6_4)  )
     snake_case_					    =					torch.from_numpy(_lowerCAmelCase  ).to(device=_lowerCAmelCase    ,				dtype=_lowerCAmelCase  )
     snake_case_					    =					{
         "image": init_image,
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 7.5,
         "output_type": "numpy",
     }
     return inputs



  def 					lowerCAmelCase__  (     self       :       int  )   -> int:




     """simple docstring"""
     snake_case_					    =					StableDiffusionImageVariationPipeline.from_pretrained(
         "lambdalabs/sd-image-variations-diffusers"    ,				safety_checker=_lowerCAmelCase  )
     snake_case_					    =					sd_pipe.to(_lowerCAmelCase  )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

     snake_case_					    =					self.get_inputs(_lowerCAmelCase  )
     snake_case_					    =					sd_pipe(**_lowerCAmelCase  ).images
     snake_case_					    =					image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     snake_case_					    =					np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379]  )
     assert np.abs(image_slice - expected_slice  ).max() < 6e-3



  def 					lowerCAmelCase__  (     self       :       int  )   -> List[str]:




     """simple docstring"""
     snake_case_					    =					0

     def callback_fn(_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       torch.FloatTensor  ) -> None:
        snake_case_					    =					True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           snake_case_					    =					latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           snake_case_					    =					latents[0, -3:, -3:, -1]
           snake_case_					    =					np.array(
               [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309]  )

           assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5e-2
        elif step == 2:
           snake_case_					    =					latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           snake_case_					    =					latents[0, -3:, -3:, -1]
           snake_case_					    =					np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273]  )

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

     snake_case_					    =					False

     snake_case_					    =					StableDiffusionImageVariationPipeline.from_pretrained(
         "fusing/sd-image-variations-diffusers"    ,				safety_checker=_lowerCAmelCase    ,				torch_dtype=torch.floataa    ,				)
     pipe.to(_lowerCAmelCase  )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase  )
     pipe.enable_attention_slicing()

     snake_case_					    =					self.get_inputs(_lowerCAmelCase    ,				dtype=torch.floataa  )
     pipe(**_lowerCAmelCase    ,				callback=_lowerCAmelCase    ,				callback_steps=1  )
     assert callback_fn.has_been_called
     assert number_of_steps == inputs["num_inference_steps"]



  def 					lowerCAmelCase__  (     self       :       Dict  )   -> List[str]:




     """simple docstring"""
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     snake_case_					    =					"fusing/sd-image-variations-diffusers"
     snake_case_					    =					StableDiffusionImageVariationPipeline.from_pretrained(
         _lowerCAmelCase    ,				safety_checker=_lowerCAmelCase    ,				torch_dtype=torch.floataa  )
     snake_case_					    =					pipe.to(_lowerCAmelCase  )
     pipe.set_progress_bar_config(disable=_lowerCAmelCase  )
     pipe.enable_attention_slicing(1  )
     pipe.enable_sequential_cpu_offload()

     snake_case_					    =					self.get_inputs(_lowerCAmelCase    ,				dtype=torch.floataa  )
     snake_case_					    =					pipe(**_lowerCAmelCase  )

     snake_case_					    =					torch.cuda.max_memory_allocated()
     # make sure that less than 2.6 GB is allocated
     assert mem_bytes < 2.6 * 1_0**9






@nightly
@require_torch_gpu
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       Optional[int]  )   -> Tuple:




     """simple docstring"""
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



  def 					lowerCAmelCase__  (     self       :       Dict    ,				_lowerCAmelCase       :       Optional[Any]    ,				_lowerCAmelCase       :       Optional[Any]="cpu"    ,				_lowerCAmelCase       :       Union[str, Any]=torch.floataa    ,				_lowerCAmelCase       :       Any=0  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					torch.Generator(device=_lowerCAmelCase  ).manual_seed(_lowerCAmelCase  )
     snake_case_					    =					load_image(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
         "/stable_diffusion_imgvar/input_image_vermeer.png"  )
     snake_case_					    =					np.random.RandomState(_lowerCAmelCase  ).standard_normal((1, 4, 6_4, 6_4)  )
     snake_case_					    =					torch.from_numpy(_lowerCAmelCase  ).to(device=_lowerCAmelCase    ,				dtype=_lowerCAmelCase  )
     snake_case_					    =					{
         "image": init_image,
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 5_0,
         "guidance_scale": 7.5,
         "output_type": "numpy",
     }
     return inputs



  def 					lowerCAmelCase__  (     self       :       Optional[int]  )   -> List[Any]:




     """simple docstring"""
     snake_case_					    =					StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"  )
     sd_pipe.to(_lowerCAmelCase  )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

     snake_case_					    =					self.get_inputs(_lowerCAmelCase  )
     snake_case_					    =					sd_pipe(**_lowerCAmelCase  ).images[0]

     snake_case_					    =					load_numpy(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
         "/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"  )
     snake_case_					    =					np.abs(expected_image - image  ).max()
     assert max_diff < 1e-3



  def 					lowerCAmelCase__  (     self       :       str  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					StableDiffusionImageVariationPipeline.from_pretrained("fusing/sd-image-variations-diffusers"  )
     snake_case_					    =					DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config  )
     sd_pipe.to(_lowerCAmelCase  )
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase  )

     snake_case_					    =					self.get_inputs(_lowerCAmelCase  )
     snake_case_					    =					2_5
     snake_case_					    =					sd_pipe(**_lowerCAmelCase  ).images[0]

     snake_case_					    =					load_numpy(
         "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
         "/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"  )
     snake_case_					    =					np.abs(expected_image - image  ).max()
     assert max_diff < 1e-3








import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


SCREAMING_SNAKE_CASE	:Tuple  		=     -1 if torch_device == '''cpu''' else 0


@is_pipeline_test
class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  _SCREAMING_SNAKE_CASE		    =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



  def 					lowerCAmelCase__  (     self       :       List[Any]    ,				_lowerCAmelCase       :       Tuple    ,				_lowerCAmelCase       :       List[str]    ,				_lowerCAmelCase       :       List[str]  )   -> Dict:




     """simple docstring"""
     snake_case_					    =					SummarizationPipeline(model=_lowerCAmelCase    ,				tokenizer=_lowerCAmelCase  )
     return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]



  def 					lowerCAmelCase__  (     self       :       Union[str, Any]    ,				_lowerCAmelCase       :       int    ,				_lowerCAmelCase       :       int  )   -> Union[str, Any]:




     """simple docstring"""
     snake_case_					    =					summarizer.model

     snake_case_					    =					summarizer("(CNN)The Palestinian Authority officially became"  )
     self.assertEqual(_lowerCAmelCase    ,				[{"summary_text": ANY(_lowerCAmelCase  )}]  )

     snake_case_					    =					summarizer(
         "(CNN)The Palestinian Authority officially became "    ,				num_beams=2    ,				min_length=2    ,				max_length=5    ,				)
     self.assertEqual(_lowerCAmelCase    ,				[{"summary_text": ANY(_lowerCAmelCase  )}]  )

     # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
     snake_case_					    =					[
         "SwitchTransformersConfig",
         "T5Config",
         "LongT5Config",
         "LEDConfig",
         "PegasusXConfig",
         "FSMTConfig",
         "M2M100Config",
         "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
     ]
     if model.config.__class__.__name__ not in model_can_handle_longer_seq:
        # Too long and exception is expected.
        # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
        # the embedding layer.
        if not (
            isinstance(_lowerCAmelCase    ,				_lowerCAmelCase  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
           with self.assertRaises(_lowerCAmelCase  ):
              snake_case_					    =					summarizer("This " * 1_0_0_0  )
     snake_case_					    =					summarizer("This " * 1_0_0_0    ,				truncation=TruncationStrategy.ONLY_FIRST  )



  @require_torch
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> str:




     """simple docstring"""
     snake_case_					    =					pipeline(task="summarization"    ,				model="sshleifer/tiny-mbart"    ,				framework="pt"  )
     snake_case_					    =					summarizer("This is a small test"  )
     self.assertEqual(
         _lowerCAmelCase    ,				[
             {
                 "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
             }
         ]    ,				)



  @require_tf
  def 					lowerCAmelCase__  (     self       :       Dict  )   -> Optional[int]:




     """simple docstring"""
     snake_case_					    =					pipeline(task="summarization"    ,				model="sshleifer/tiny-mbart"    ,				framework="tf"  )
     snake_case_					    =					summarizer("This is a small test"  )
     self.assertEqual(
         _lowerCAmelCase    ,				[
             {
                 "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
             }
         ]    ,				)



  @require_torch
  @slow
  def 					lowerCAmelCase__  (     self       :       List[Any]  )   -> Union[str, Any]:




     """simple docstring"""
     snake_case_					    =					pipeline(task="summarization"    ,				device=_lowerCAmelCase  )
     snake_case_					    =					(
         " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
         " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
         " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
         " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
         " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
         " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
         " situation in Palestinian territories, paving the way for possible war crimes investigations against"
         " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
         " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
         " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
         " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
         " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
         " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
         " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
         " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
         " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
         " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
         " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
         " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
         " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
         " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
         " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
         " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
         " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
         " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
         " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
         " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
         " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
         " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
         " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
         " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
         " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
         " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
         " will include alleged war crimes committed since June. The International Criminal Court was set up in"
         " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
         " and Faith Karimi contributed to this report."
     )
     snake_case_					    =					(
         " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
         " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
         " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
         " says governments seeking to penalize Palestine should end pressure ."
     )
     snake_case_					    =					summarizer(_lowerCAmelCase  )
     self.assertEqual(result[0]["summary_text"]    ,				_lowerCAmelCase  )




