


'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


a__    :   List[Any]								=			logging.get_logger(__name__)

a__    :   Optional[Any]								=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a__    :   List[Any]								=			{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

a__    :   str								=			{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}
class    lowercase_	(       lowerCamelCase__  ):
    __UpperCAmelCase       =      VOCAB_FILES_NAMES
    __UpperCAmelCase       =      PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase       =      ['input_ids', 'attention_mask']
    __UpperCAmelCase       =      LongformerTokenizer


    def __init__(	self    ,   a=None    ,   a=None    ,   a=None    ,   a="replace"    ,   a="<s>"    ,   a="</s>"    ,   a="</s>"    ,   a="<s>"    ,   a="<unk>"    ,   a="<pad>"    ,   a="<mask>"    ,   a=False    ,   a=True    ,   **a    ,   ):
          super().__init__(
              __UpperCamelCase    ,   __UpperCamelCase    ,   tokenizer_file=__UpperCamelCase    ,   errors=__UpperCamelCase    ,   bos_token=__UpperCamelCase    ,   eos_token=__UpperCamelCase    ,   sep_token=__UpperCamelCase    ,   cls_token=__UpperCamelCase    ,   unk_token=__UpperCamelCase    ,   pad_token=__UpperCamelCase    ,   mask_token=__UpperCamelCase    ,   add_prefix_space=__UpperCamelCase    ,   trim_offsets=__UpperCamelCase    ,   **__UpperCamelCase    ,   )

          UpperCamelCase__      =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
          if pre_tok_state.get("add_prefix_space"    ,   __UpperCamelCase       ) != add_prefix_space:
                UpperCamelCase__      =							getattr(__UpperCamelCase    ,   pre_tok_state.pop("type"       )       )
                UpperCamelCase__      =							add_prefix_space
                UpperCamelCase__      =							pre_tok_class(**__UpperCamelCase       )

          UpperCamelCase__      =							add_prefix_space

          UpperCamelCase__      =							"post_processor"
          UpperCamelCase__      =							getattr(self.backend_tokenizer    ,   __UpperCamelCase    ,   __UpperCamelCase       )
          if tokenizer_component_instance:
                UpperCamelCase__      =							json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                      UpperCamelCase__      =							tuple(state["sep"]       )
                if "cls" in state:
                      UpperCamelCase__      =							tuple(state["cls"]       )

                UpperCamelCase__      =							False

                if state.get("add_prefix_space"    ,   __UpperCamelCase       ) != add_prefix_space:
                      UpperCamelCase__      =							add_prefix_space
                      UpperCamelCase__      =							True

                if state.get("trim_offsets"    ,   __UpperCamelCase       ) != trim_offsets:
                      UpperCamelCase__      =							trim_offsets
                      UpperCamelCase__      =							True

                if changes_to_apply:
                      UpperCamelCase__      =							getattr(__UpperCamelCase    ,   state.pop("type"       )       )
                      UpperCamelCase__      =							component_class(**__UpperCamelCase       )
                      setattr(self.backend_tokenizer    ,   __UpperCamelCase    ,   __UpperCamelCase       )


    @property
    def   __a				(	self       ):
          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    ,   a       ):
          UpperCamelCase__      =							AddedToken(__UpperCamelCase    ,   lstrip=__UpperCamelCase    ,   rstrip=__UpperCamelCase       ) if isinstance(__UpperCamelCase    ,   __UpperCamelCase       ) else value
          UpperCamelCase__      =							value


    def   __a				(	self    ,   *a    ,   **a       ):
          UpperCamelCase__      =							kwargs.get("is_split_into_words"    ,   __UpperCamelCase       )
          assert self.add_prefix_space or not is_split_into_words, (
              f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*__UpperCamelCase    ,   **__UpperCamelCase       )


    def   __a				(	self    ,   *a    ,   **a       ):
          UpperCamelCase__      =							kwargs.get("is_split_into_words"    ,   __UpperCamelCase       )

          assert self.add_prefix_space or not is_split_into_words, (
              f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*__UpperCamelCase    ,   **__UpperCamelCase       )


    def   __a				(	self    ,   a    ,   a = None       ):
          UpperCamelCase__      =							self._tokenizer.model.save(__UpperCamelCase    ,   name=__UpperCamelCase       )
          return tuple(__UpperCamelCase       )


    def   __a				(	self    ,   a    ,   a=None       ):
          UpperCamelCase__      =							[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    ,   a    ,   a = None       ):
          UpperCamelCase__      =							[self.sep_token_id]
          UpperCamelCase__      =							[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]









'''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       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					RePaintPipeline
     UpperCAmelCase			       =					IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     UpperCAmelCase			       =					IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase			       =					False






     def 			a_       (      self		)							->     int:
          torch.manual_seed(0		)
          torch.manual_seed(0		)
          _a            =		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")  ,				)
          _a            =		RePaintScheduler()
          _a            =		{"unet": unet, "scheduler": scheduler}
          return components






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Tuple:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		np.random.RandomState(__UpperCamelCase		).standard_normal((1, 3, 32, 32)		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		(image > 0).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		{
              "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		)							->     Optional[Any]:
          _a            =		"cpu"  # ensure determinism for the device-dependent torch.Generator
          _a            =		self.get_dummy_components()
          _a            =		RePaintPipeline(**__UpperCamelCase		)
          _a            =		sd_pipe.to(__UpperCamelCase		)
          sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

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

          assert image.shape == (1, 32, 32, 3)
          _a            =		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

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






     @skip_mps
     def 			a_       (      self		)							->     Union[str, Any]:
          return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"		)
     def 			a_       (      self		)							->     int:
          return super().test_inference_batch_single_identical()






     @skip_mps
     def 			a_       (      self		)							->     str:
          return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 			a_       (      self		)							->     int:
          return super().test_save_load_optional_components()






     @skip_mps
     def 			a_       (      self		)							->     Optional[int]:
          return super().test_attention_slicing_forward_pass()







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






     def 			a_       (      self		)							->     List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Union[str, Any]:
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
          _a            =		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"		)

          _a            =		"google/ddpm-ema-celebahq-256"
          _a            =		UNetaDModel.from_pretrained(__UpperCamelCase		)
          _a            =		RePaintScheduler.from_pretrained(__UpperCamelCase		)

          _a            =		RePaintPipeline(unet=__UpperCamelCase  ,				scheduler=__UpperCamelCase		).to(__UpperCamelCase		)
          repaint.set_progress_bar_config(disable=__UpperCamelCase		)
          repaint.enable_attention_slicing()

          _a            =		torch.manual_seed(0		)
          _a            =		repaint(
              __UpperCamelCase  ,				__UpperCamelCase  ,				num_inference_steps=250  ,				eta=0.0  ,				jump_length=10  ,				jump_n_sample=10  ,				generator=__UpperCamelCase  ,				output_type="np"  ,				)
          _a            =		output.images[0]

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

