


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


__UpperCamelCase					:	Tuple          =				logging.get_logger(__name__)

__UpperCamelCase					:	Optional[int]          =				{'vocab_file': 'vocab.txt'}

__UpperCamelCase					:	List[Any]          =				{
    'vocab_file': {
        'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt',
        'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt',
        'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt',
        'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt',
    }
}

__UpperCamelCase					:	List[Any]          =				{
    'tau/splinter-base': 512,
    'tau/splinter-base-qass': 512,
    'tau/splinter-large': 512,
    'tau/splinter-large-qass': 512,
}

__UpperCamelCase					:	Tuple          =				{
    'tau/splinter-base': {'do_lower_case': False},
    'tau/splinter-base-qass': {'do_lower_case': False},
    'tau/splinter-large': {'do_lower_case': False},
    'tau/splinter-large-qass': {'do_lower_case': False},
}





class 			lowerCAmelCase__(     snake_case__    ):






    '''simple docstring'''





    A_	:  Optional[Any]								=			VOCAB_FILES_NAMES
    A_	:  Any								=			PRETRAINED_VOCAB_FILES_MAP
    A_	:  str								=			PRETRAINED_INIT_CONFIGURATION
    A_	:  Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A_	:  Any								=			SplinterTokenizer





    def __init__(						self					: Dict							, __snake_case					: Dict=None							, __snake_case					: List[Any]=None							, __snake_case					: Optional[int]=True							, __snake_case					: Dict="[UNK]"							, __snake_case					: str="[SEP]"							, __snake_case					: int="[PAD]"							, __snake_case					: int="[CLS]"							, __snake_case					: Union[str, Any]="[MASK]"							, __snake_case					: Union[str, Any]="[QUESTION]"							, __snake_case					: str=True							, __snake_case					: List[Any]=None							, **__snake_case					: Dict							, ):





       '''simple docstring'''
       super().__init__(
           __snake_case							, tokenizer_file=__snake_case							, do_lower_case=__snake_case							, unk_token=__snake_case							, sep_token=__snake_case							, pad_token=__snake_case							, cls_token=__snake_case							, mask_token=__snake_case							, tokenize_chinese_chars=__snake_case							, strip_accents=__snake_case							, additional_special_tokens=(question_token,)							, **__snake_case							, )

       UpperCAmelCase_				:   str							= json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
       if (
           pre_tok_state.get('''lowercase'''							, __snake_case						) != do_lower_case
           or pre_tok_state.get('''strip_accents'''							, __snake_case						) != strip_accents
       ):
          UpperCAmelCase_				:   Union[str, Any]							= getattr(__snake_case							, pre_tok_state.pop('''type'''						)						)
          UpperCAmelCase_				:   str							= do_lower_case
          UpperCAmelCase_				:   List[str]							= strip_accents
          UpperCAmelCase_				:   Optional[Any]							= pre_tok_class(**__snake_case						)

       UpperCAmelCase_				:   List[str]							= do_lower_case





    @property
    def        _lowerCamelCase			(						self					: str						):





       '''simple docstring'''
       return self.convert_tokens_to_ids(self.question_token						)





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: List[int]							, __snake_case					: Optional[List[int]] = None						):





       '''simple docstring'''
       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       UpperCAmelCase_				:   int							= [self.cls_token_id]
       UpperCAmelCase_				:   int							= [self.sep_token_id]
       UpperCAmelCase_				:   List[Any]							= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''						)]
       if self.padding_side == "right":
          # Input is question-then-context
          return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
       else:
          # Input is context-then-question
          return cls + token_ids_a + sep + token_ids_a + question_suffix + sep





    def        _lowerCamelCase			(						self					: Optional[Any]							, __snake_case					: List[int]							, __snake_case					: Optional[List[int]] = None						):





       '''simple docstring'''
       UpperCAmelCase_				:   Dict							= [self.sep_token_id]
       UpperCAmelCase_				:   List[str]							= [self.cls_token_id]
       UpperCAmelCase_				:   Any							= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''						)]
       if token_ids_a is None:
          return len(cls + token_ids_a + sep						) * [0]

       if self.padding_side == "right":
          # Input is question-then-context
          return len(cls + token_ids_a + question_suffix + sep						) * [0] + len(token_ids_a + sep						) * [1]
       else:
          # Input is context-then-question
          return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + question_suffix + sep						) * [1]





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: str							, __snake_case					: Optional[str] = None						):





       '''simple docstring'''
       UpperCAmelCase_				:   Union[str, Any]							= self._tokenizer.model.save(__snake_case							, name=__snake_case						)
       return tuple(__snake_case						)



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 			lowerCAmelCase__(     snake_case__     ,     unittest.TestCase    ):






    '''simple docstring'''





    A_	:  Tuple								=			RePaintPipeline
    A_	:  Tuple								=			IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
    A_	:  str								=			PipelineTesterMixin.required_optional_params - {
        'latents',
        'num_images_per_prompt',
        'callback',
        'callback_steps',
    }
    A_	:  Optional[Any]								=			IMAGE_INPAINTING_BATCH_PARAMS
    A_	:  Union[str, Any]								=			False





    def        _lowerCamelCase			(						self					: Any						):





       '''simple docstring'''
       torch.manual_seed(0						)
       torch.manual_seed(0						)
       UpperCAmelCase_				:   int							= 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_				:   str							= RePaintScheduler()
       UpperCAmelCase_				:   Optional[Any]							= {'''unet''': unet, '''scheduler''': scheduler}
       return components





    def        _lowerCamelCase			(						self					: List[str]							, __snake_case					: Optional[Any]							, __snake_case					: int=0						):





       '''simple docstring'''
       if str(__snake_case						).startswith('''mps'''						):
          UpperCAmelCase_				:   Optional[Any]							= torch.manual_seed(__snake_case						)
       else:
          UpperCAmelCase_				:   Optional[Any]							= torch.Generator(device=__snake_case						).manual_seed(__snake_case						)
       UpperCAmelCase_				:   Optional[Any]							= np.random.RandomState(__snake_case						).standard_normal((1, 3, 32, 32)						)
       UpperCAmelCase_				:   Optional[int]							= torch.from_numpy(__snake_case						).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   Any							= (image > 0).to(device=__snake_case							, dtype=torch.floataa						)
       UpperCAmelCase_				:   List[str]							= {
           '''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        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       UpperCAmelCase_				:   str							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_				:   Optional[int]							= self.get_dummy_components()
       UpperCAmelCase_				:   Dict							= RePaintPipeline(**__snake_case						)
       UpperCAmelCase_				:   int							= sd_pipe.to(__snake_case						)
       sd_pipe.set_progress_bar_config(disable=__snake_case						)

       UpperCAmelCase_				:   List[Any]							= self.get_dummy_inputs(__snake_case						)
       UpperCAmelCase_				:   Union[str, Any]							= sd_pipe(**__snake_case						).images
       UpperCAmelCase_				:   Optional[Any]							= image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase_				:   Tuple							= np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]						)

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





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





       '''simple docstring'''
       return super().test_save_load_local()





    @unittest.skip('''non-deterministic pipeline'''						)
    def        _lowerCamelCase			(						self					: Optional[Any]						):





       '''simple docstring'''
       return super().test_inference_batch_single_identical()





    @skip_mps
    def        _lowerCamelCase			(						self					: Dict						):





       '''simple docstring'''
       return super().test_dict_tuple_outputs_equivalent()





    @skip_mps
    def        _lowerCamelCase			(						self					: int						):





       '''simple docstring'''
       return super().test_save_load_optional_components()





    @skip_mps
    def        _lowerCamelCase			(						self					: Union[str, Any]						):





       '''simple docstring'''
       return super().test_attention_slicing_forward_pass()





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






    '''simple docstring'''





    def        _lowerCamelCase			(						self					: Tuple						):





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





    def        _lowerCamelCase			(						self					: List[str]						):





       '''simple docstring'''
       UpperCAmelCase_				:   List[Any]							= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
           '''repaint/celeba_hq_256.png'''						)
       UpperCAmelCase_				:   int							= 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_				:   List[str]							= '''google/ddpm-ema-celebahq-256'''
       UpperCAmelCase_				:   Union[str, Any]							= UNetaDModel.from_pretrained(__snake_case						)
       UpperCAmelCase_				:   Dict							= RePaintScheduler.from_pretrained(__snake_case						)

       UpperCAmelCase_				:   Optional[int]							= RePaintPipeline(unet=__snake_case							, scheduler=__snake_case						).to(__snake_case						)
       repaint.set_progress_bar_config(disable=__snake_case						)
       repaint.enable_attention_slicing()

       UpperCAmelCase_				:   Optional[int]							= torch.manual_seed(0						)
       UpperCAmelCase_				:   List[str]							= repaint(
           __snake_case							, __snake_case							, num_inference_steps=250							, eta=0.0							, jump_length=10							, jump_n_sample=10							, generator=__snake_case							, output_type='''np'''							, )
       UpperCAmelCase_				:   Tuple							= output.images[0]

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