






import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     __a						(					lowerCamelCase_ ,     unittest.TestCase ):
 _lowerCAmelCase : Optional[Any] 				=				RePaintPipeline
 _lowerCAmelCase : List[Any] 				=				IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
 _lowerCAmelCase : List[Any] 				=				PipelineTesterMixin.required_optional_params - {
     """latents""",
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 _lowerCAmelCase : List[Any] 				=				IMAGE_INPAINTING_BATCH_PARAMS
 _lowerCAmelCase : List[str] 				=				False







 def       __lowercase      (	self      :     Union[str, Any]		):


      '''simple docstring'''






      torch.manual_seed(0		)
      torch.manual_seed(0		)
      UpperCamelCase__    :     Union[str, Any]        =						UNetaDModel(
          block_out_channels=(32, 64)							,     layers_per_block=2							,     sample_size=32							,     in_channels=3							,     out_channels=3							,     down_block_types=("DownBlock2D", "AttnDownBlock2D")							,     up_block_types=("AttnUpBlock2D", "UpBlock2D")							,     )
      UpperCamelCase__    :     Union[str, Any]        =						RePaintScheduler()
      UpperCamelCase__    :     Optional[Any]        =						{"unet": unet, "scheduler": scheduler}
      return components







 def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     str=0		):


      '''simple docstring'''






      if str(SCREAMING_SNAKE_CASE		).startswith("mps"		):
           UpperCamelCase__    :     str        =						torch.manual_seed(SCREAMING_SNAKE_CASE		)
      else:
           UpperCamelCase__    :     List[Any]        =						torch.Generator(device=SCREAMING_SNAKE_CASE		).manual_seed(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Any        =						np.random.RandomState(SCREAMING_SNAKE_CASE		).standard_normal((1, 3, 32, 32)		)
      UpperCamelCase__    :     Any        =						torch.from_numpy(SCREAMING_SNAKE_CASE		).to(device=SCREAMING_SNAKE_CASE							,     dtype=torch.floataa		)
      UpperCamelCase__    :     List[str]        =						(image > 0).to(device=SCREAMING_SNAKE_CASE							,     dtype=torch.floataa		)
      UpperCamelCase__    :     int        =						{
          "image": image,
          "mask_image": mask,
          "generator": generator,
          "num_inference_steps": 5,
          "eta": 0.0,
          "jump_length": 2,
          "jump_n_sample": 2,
          "output_type": "numpy",
      }
      return inputs







 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						"cpu"  # ensure determinism for the device-dependent torch.Generator
      UpperCamelCase__    :     Any        =						self.get_dummy_components()
      UpperCamelCase__    :     Optional[int]        =						RePaintPipeline(**SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[Any]        =						sd_pipe.to(SCREAMING_SNAKE_CASE		)
      sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Any        =						self.get_dummy_inputs(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Dict        =						sd_pipe(**SCREAMING_SNAKE_CASE		).images
      UpperCamelCase__    :     Optional[int]        =						image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      UpperCamelCase__    :     List[str]        =						np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]		)

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







 @skip_mps
 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      return super().test_save_load_local()







 @unittest.skip("non-deterministic pipeline"		)
 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      return super().test_inference_batch_single_identical()







 @skip_mps
 def       __lowercase      (	self      :     Tuple		):


      '''simple docstring'''






      return super().test_dict_tuple_outputs_equivalent()







 @skip_mps
 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      return super().test_save_load_optional_components()







 @skip_mps
 def       __lowercase      (	self      :     Tuple		):


      '''simple docstring'''






      return super().test_attention_slicing_forward_pass()







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







 def       __lowercase      (	self      :     Dict		):


      '''simple docstring'''






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







 def       __lowercase      (	self      :     Union[str, Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[int]        =						load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
          "repaint/celeba_hq_256.png"		)
      UpperCamelCase__    :     Any        =						load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
      UpperCamelCase__    :     List[Any]        =						load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
          "repaint/celeba_hq_256_result.npy"		)

      UpperCamelCase__    :     str        =						"google/ddpm-ema-celebahq-256"
      UpperCamelCase__    :     Optional[Any]        =						UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     str        =						RePaintPipeline(unet=SCREAMING_SNAKE_CASE							,     scheduler=SCREAMING_SNAKE_CASE		).to(SCREAMING_SNAKE_CASE		)
      repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE		)
      repaint.enable_attention_slicing()

      UpperCamelCase__    :     Union[str, Any]        =						torch.manual_seed(0		)
      UpperCamelCase__    :     int        =						repaint(
          SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     num_inference_steps=2_50							,     eta=0.0							,     jump_length=10							,     jump_n_sample=10							,     generator=SCREAMING_SNAKE_CASE							,     output_type="np"							,     )
      UpperCamelCase__    :     List[Any]        =						output.images[0]

      assert image.shape == (2_56, 2_56, 3)
      assert np.abs(expected_image - image		).mean() < 1e-2








import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class     __a						(					A__ ,     unittest.TestCase ):
 _lowerCAmelCase : str 				=				MPNetTokenizer
 _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
 _lowerCAmelCase : List[Any] 				=				True
 _lowerCAmelCase : Union[str, Any] 				=				True







 def       __lowercase      (	self      :     List[Any]		):


      '''simple docstring'''






      super().setUp()

      UpperCamelCase__    :     List[Any]        =						[
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
      with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)







 def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
      UpperCamelCase__    :     List[str]        =						"unwanted, running"
      return input_text, output_text







 def       __lowercase      (	self      :     Any		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)

      UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
      self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)







 @slow
 def       __lowercase      (	self      :     str		):


      '''simple docstring'''






      UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)

      UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
      UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)

      assert encoded_sentence == [0] + text + [2]
      assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]