code: 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
  
  
  lowerCamelCase__           =				-1 if torch_device == 'cpu' else 0
  
  
  
  
  @is_pipeline_test
  class 					UpperCamelCase	(     unittest.TestCase      ):
  					__UpperCamelCase						=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  					__UpperCamelCase						=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     SummarizationPipeline(model=_lowerCAmelCase      ,tokenizer=_lowerCAmelCase						)
  							return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""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_000						)
  							__snake_case		       =     summarizer("This " * 1_000      ,truncation=TruncationStrategy.ONLY_FIRST						)
  
  
  
  
  					@require_torch
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""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 							UpperCamelCase_						( self :					Any						):
  
  
  							"""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 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""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						)
  
  
  
  
  
  
style_context: import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  class 					UpperCamelCase	(     snake_case__			,      unittest.TestCase      ):
  					__UpperCamelCase						=   PaintByExamplePipeline
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  					__UpperCamelCase						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  					__UpperCamelCase						=   frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							torch.manual_seed(0						)
  							__snake_case		       =     UNetaDConditionModel(
  							    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=9      ,out_channels=4      ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,cross_attention_dim=32      ,)
  							__snake_case		       =     PNDMScheduler(skip_prk_steps=_lowerCAmelCase						)
  							torch.manual_seed(0						)
  							__snake_case		       =     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						)
  							__snake_case		       =     CLIPVisionConfig(
  							    hidden_size=32      ,projection_dim=32      ,intermediate_size=37      ,layer_norm_eps=1E-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,image_size=32      ,patch_size=4      ,)
  							__snake_case		       =     PaintByExampleImageEncoder(_lowerCAmelCase      ,proj_size=32						)
  							__snake_case		       =     CLIPImageProcessor(crop_size=32      ,size=32						)
  
  							__snake_case		       =     {
  							    "unet": unet,
  							    "scheduler": scheduler,
  							    "vae": vae,
  							    "image_encoder": image_encoder,
  							    "safety_checker": None,
  							    "feature_extractor": feature_extractor,
  							}
  							return components
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     np.array(image.convert("RGB"						)						)
  							__snake_case		       =     image[None].transpose(0      ,3      ,1      ,2						)
  							__snake_case		       =     torch.from_numpy(_lowerCAmelCase						).to(dtype=torch.floataa						) / 1_2_7.5 - 1.0
  							return image
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Any="cpu"      ,_lowerCAmelCase :					List[str]=0						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     floats_tensor((1, 3, 32, 32)      ,rng=random.Random(_lowerCAmelCase						)						).to(_lowerCAmelCase						)
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(image + 4						)						).convert("RGB"						).resize((64, 64)						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						).resize((32, 32)						)
  
  							if str(_lowerCAmelCase						).startswith("mps"						):
  									__snake_case		       =     torch.manual_seed(_lowerCAmelCase						)
  							else:
  									__snake_case		       =     torch.Generator(device=_lowerCAmelCase						).manual_seed(_lowerCAmelCase						)
  							__snake_case		       =     {
  							    "example_image": example_image,
  							    "image": init_image,
  							    "mask_image": mask_image,
  							    "generator": generator,
  							    "num_inference_steps": 2,
  							    "guidance_scale": 6.0,
  							    "output_type": "numpy",
  							}
  							return inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.get_dummy_components()
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**_lowerCAmelCase						)
  							__snake_case		       =     pipe.to("cpu"						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     self.get_dummy_inputs()
  							__snake_case		       =     pipe(**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 64, 64, 3)
  							__snake_case		       =     np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     "cpu"
  							__snake_case		       =     self.get_dummy_inputs()
  							inputs.pop("mask_image"						)
  							__snake_case		       =     self.convert_to_pt(inputs.pop("image"						)						)
  							__snake_case		       =     image.clamp(0      ,1						) / 2
  
  							# make sure here that pndm scheduler skips prk
  							__snake_case		       =     PaintByExamplePipeline(**self.get_dummy_components()						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     pipe(image=_lowerCAmelCase      ,mask_image=mask_image[:, 0]      ,**_lowerCAmelCase						)
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image.cpu().permute(0      ,2      ,3      ,1						)[0]
  							__snake_case		       =     mask_image.cpu().permute(0      ,2      ,3      ,1						)[0]
  
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  							__snake_case		       =     Image.fromarray(np.uinta(_lowerCAmelCase						)						).convert("RGB"						)
  
  							__snake_case		       =     pipe(**self.get_dummy_inputs()						)
  							__snake_case		       =     output.images
  
  							assert out_a.shape == (1, 64, 64, 3)
  							assert np.abs(out_a.flatten() - out_a.flatten()						).max() < 5E-2
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().test_inference_batch_single_identical(expected_max_diff=3E-3						)
  
  
  
  
  @slow
  @require_torch_gpu
  class 					UpperCamelCase	(     unittest.TestCase      ):
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().tearDown()
  							gc.collect()
  							torch.cuda.empty_cache()
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/dog_in_bucket.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/mask.png"						)
  							__snake_case		       =     load_image(
  							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  							    "/paint_by_example/panda.jpg"						)
  
  							__snake_case		       =     PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"						)
  							__snake_case		       =     pipe.to(_lowerCAmelCase						)
  							pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
  
  							__snake_case		       =     torch.manual_seed(321						)
  							__snake_case		       =     pipe(
  							    image=_lowerCAmelCase      ,mask_image=_lowerCAmelCase      ,example_image=_lowerCAmelCase      ,generator=_lowerCAmelCase      ,guidance_scale=5.0      ,num_inference_steps=50      ,output_type="np"      ,)
  
  							__snake_case		       =     output.images
  
  							__snake_case		       =     image[0, -3:, -3:, -1]
  
  							assert image.shape == (1, 512, 512, 3)
  							__snake_case		       =     np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]						)
  
  							assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
  
  
  
  
  
  