"""simple docstring"""







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     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       str						=						RePaintPipeline
   __UpperCAmelCase  :       Any						=						IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
   __UpperCAmelCase  :       List[str]						=						PipelineTesterMixin.required_optional_params - {
       "latents",
       "num_images_per_prompt",
       "callback",
       "callback_steps",
   }
   __UpperCAmelCase  :       Any						=						IMAGE_INPAINTING_BATCH_PARAMS
   __UpperCAmelCase  :       int						=						False







   def       _A							(  self  ):


      '''simple docstring'''

      torch.manual_seed(0  )
      torch.manual_seed(0  )
      __lowerCamelCase								=				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')   ,							)
      __lowerCamelCase								=				RePaintScheduler()
      __lowerCamelCase								=				{'unet': unet, 'scheduler': scheduler}
      return components







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=0  ):


      '''simple docstring'''

      if str(_SCREAMING_SNAKE_CASE  ).startswith('mps'  ):
         __lowerCamelCase								=				torch.manual_seed(_SCREAMING_SNAKE_CASE  )
      else:
         __lowerCamelCase								=				torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				np.random.RandomState(_SCREAMING_SNAKE_CASE  ).standard_normal((1, 3, 32, 32)  )
      __lowerCamelCase								=				torch.from_numpy(_SCREAMING_SNAKE_CASE  ).to(device=_SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa  )
      __lowerCamelCase								=				(image > 0).to(device=_SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa  )
      __lowerCamelCase								=				{
          '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       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
      __lowerCamelCase								=				self.get_dummy_components()
      __lowerCamelCase								=				RePaintPipeline(**_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				sd_pipe.to(_SCREAMING_SNAKE_CASE  )
      sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

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

      assert image.shape == (1, 32, 32, 3)
      __lowerCamelCase								=				np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]  )

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







   @skip_mps
   def       _A							(  self  ):


      '''simple docstring'''

      return super().test_save_load_local()







   @unittest.skip('non-deterministic pipeline'  )
   def       _A							(  self  ):


      '''simple docstring'''

      return super().test_inference_batch_single_identical()







   @skip_mps
   def       _A							(  self  ):


      '''simple docstring'''

      return super().test_dict_tuple_outputs_equivalent()







   @skip_mps
   def       _A							(  self  ):


      '''simple docstring'''

      return super().test_save_load_optional_components()







   @skip_mps
   def       _A							(  self  ):


      '''simple docstring'''

      return super().test_attention_slicing_forward_pass()






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



   '''simple docstring'''



   def       _A							(  self  ):


      '''simple docstring'''

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







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
          'repaint/celeba_hq_256.png'  )
      __lowerCamelCase								=				load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'  )
      __lowerCamelCase								=				load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
          'repaint/celeba_hq_256_result.npy'  )

      __lowerCamelCase								=				'google/ddpm-ema-celebahq-256'
      __lowerCamelCase								=				UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				RePaintScheduler.from_pretrained(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				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()

      __lowerCamelCase								=				torch.manual_seed(0  )
      __lowerCamelCase								=				repaint(
          _SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							num_inference_steps=250   ,							eta=0.0   ,							jump_length=10   ,							jump_n_sample=10   ,							generator=_SCREAMING_SNAKE_CASE   ,							output_type='np'   ,							)
      __lowerCamelCase								=				output.images[0]

      assert image.shape == (256, 256, 3)
      assert np.abs(expected_image - image  ).mean() < 1E-2







"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :		Any        =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE  :		Optional[Any]        =      {
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE  :		int        =      {
    '''microsoft/mpnet-base''': 5_1_2,
}

_SCREAMING_SNAKE_CASE  :		Tuple        =      {
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[int]						=						VOCAB_FILES_NAMES
   __UpperCAmelCase  :       Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase  :       Dict						=						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase  :       Dict						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase  :       Union[str, Any]						=						MPNetTokenizer
   __UpperCAmelCase  :       List[Any]						=						["input_ids", "attention_mask"]







   def __init__(  self   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE="<s>"   ,							_SCREAMING_SNAKE_CASE="</s>"   ,							_SCREAMING_SNAKE_CASE="</s>"   ,							_SCREAMING_SNAKE_CASE="<s>"   ,							_SCREAMING_SNAKE_CASE="[UNK]"   ,							_SCREAMING_SNAKE_CASE="<pad>"   ,							_SCREAMING_SNAKE_CASE="<mask>"   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							**_SCREAMING_SNAKE_CASE   ,							):


      '''simple docstring'''

      super().__init__(
          _SCREAMING_SNAKE_CASE   ,							tokenizer_file=_SCREAMING_SNAKE_CASE   ,							do_lower_case=_SCREAMING_SNAKE_CASE   ,							bos_token=_SCREAMING_SNAKE_CASE   ,							eos_token=_SCREAMING_SNAKE_CASE   ,							sep_token=_SCREAMING_SNAKE_CASE   ,							cls_token=_SCREAMING_SNAKE_CASE   ,							unk_token=_SCREAMING_SNAKE_CASE   ,							pad_token=_SCREAMING_SNAKE_CASE   ,							mask_token=_SCREAMING_SNAKE_CASE   ,							tokenize_chinese_chars=_SCREAMING_SNAKE_CASE   ,							strip_accents=_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE   ,							)

      __lowerCamelCase								=				json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
      if (
          pre_tok_state.get('lowercase'   ,							_SCREAMING_SNAKE_CASE  ) != do_lower_case
          or pre_tok_state.get('strip_accents'   ,							_SCREAMING_SNAKE_CASE  ) != strip_accents
      ):
         __lowerCamelCase								=				getattr(_SCREAMING_SNAKE_CASE   ,							pre_tok_state.pop('type'  )  )
         __lowerCamelCase								=				do_lower_case
         __lowerCamelCase								=				strip_accents
         __lowerCamelCase								=				pre_tok_class(**_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				do_lower_case







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      if self._mask_token is None:
         if self.verbose:
            logger.error('Using mask_token, but it is not set yet.'  )
         return None
      return str(self._mask_token  )







   @mask_token.setter
   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				AddedToken(_SCREAMING_SNAKE_CASE   ,							lstrip=_SCREAMING_SNAKE_CASE   ,							rstrip=_SCREAMING_SNAKE_CASE  ) if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ) else value
      __lowerCamelCase								=				value







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=None  ):


      '''simple docstring'''

      __lowerCamelCase								=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
         return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


      '''simple docstring'''

      __lowerCamelCase								=				[self.sep_token_id]
      __lowerCamelCase								=				[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep  ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE = None  ):


      '''simple docstring'''

      __lowerCamelCase								=				self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,							name=_SCREAMING_SNAKE_CASE  )
      return tuple(_SCREAMING_SNAKE_CASE  )






