




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


lowerCamelCase__				:     Tuple			       =							logging.get_logger(__name__)

lowerCamelCase__				:     List[str]			       =							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__				:     Tuple			       =							{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

lowerCamelCase__				:     int			       =							{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

lowerCamelCase__				:     Any			       =							{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 lowercase_       					=					VOCAB_FILES_NAMES
 lowercase_       					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase_       					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_       					=					["input_ids", "attention_mask", "token_type_ids"]
 lowercase_       					=					DebertaTokenizer



 def __init__(							self						:	Union[str, Any]       ,       _lowerCAmelCase						:	Any=None       ,       _lowerCAmelCase						:	List[Any]=None       ,       _lowerCAmelCase						:	Optional[Any]=None       ,       _lowerCAmelCase						:	Tuple="replace"       ,       _lowerCAmelCase						:	Dict="[CLS]"       ,       _lowerCAmelCase						:	Dict="[SEP]"       ,       _lowerCAmelCase						:	Tuple="[SEP]"       ,       _lowerCAmelCase						:	Union[str, Any]="[CLS]"       ,       _lowerCAmelCase						:	Any="[UNK]"       ,       _lowerCAmelCase						:	Any="[PAD]"       ,       _lowerCAmelCase						:	int="[MASK]"       ,       _lowerCAmelCase						:	int=False       ,       **_lowerCAmelCase						:	Union[str, Any]       ,       ):
       super().__init__(
           _lowerCAmelCase       ,       _lowerCAmelCase       ,       tokenizer_file=_lowerCAmelCase       ,       errors=_lowerCAmelCase       ,       bos_token=_lowerCAmelCase       ,       eos_token=_lowerCAmelCase       ,       unk_token=_lowerCAmelCase       ,       sep_token=_lowerCAmelCase       ,       cls_token=_lowerCAmelCase       ,       pad_token=_lowerCAmelCase       ,       mask_token=_lowerCAmelCase       ,       add_prefix_space=_lowerCAmelCase       ,       **_lowerCAmelCase       ,       )
       SCREAMING_SNAKE_CASE_					=    kwargs.pop('add_bos_token'       ,       _lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
       if pre_tok_state.get('add_prefix_space'       ,       _lowerCAmelCase		) != add_prefix_space:
             SCREAMING_SNAKE_CASE_					=    getattr(_lowerCAmelCase       ,       pre_tok_state.pop('type'		)		)
             SCREAMING_SNAKE_CASE_					=    add_prefix_space
             SCREAMING_SNAKE_CASE_					=    pre_tok_class(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    add_prefix_space



 @property
 def    lowerCAmelCase_						(							self						:	int		):
       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    lowerCAmelCase_						(							self						:	Dict       ,       _lowerCAmelCase						:	Tuple		):
       SCREAMING_SNAKE_CASE_					=    AddedToken(_lowerCAmelCase       ,       lstrip=_lowerCAmelCase       ,       rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase       ,       _lowerCAmelCase		) else value
       SCREAMING_SNAKE_CASE_					=    value



 def    lowerCAmelCase_						(							self						:	List[Any]       ,       _lowerCAmelCase						:	List[int]       ,       _lowerCAmelCase						:	Optional[List[int]] = None		):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       SCREAMING_SNAKE_CASE_					=    [self.cls_token_id]
       SCREAMING_SNAKE_CASE_					=    [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep



 def    lowerCAmelCase_						(							self						:	Dict       ,       _lowerCAmelCase						:	List[int]       ,       _lowerCAmelCase						:	Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_					=    [self.sep_token_id]
       SCREAMING_SNAKE_CASE_					=    [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		) * [0] + len(token_ids_a + sep		) * [1]



 def    lowerCAmelCase_						(							self						:	Union[str, Any]       ,       *_lowerCAmelCase						:	Any       ,       **_lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    kwargs.get('is_split_into_words'       ,       _lowerCAmelCase		)
       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(*_lowerCAmelCase       ,       **_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       *_lowerCAmelCase						:	Dict       ,       **_lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    kwargs.get('is_split_into_words'       ,       _lowerCAmelCase		)

       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(*_lowerCAmelCase       ,       **_lowerCAmelCase		)



 def    lowerCAmelCase_						(							self						:	Any       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Optional[str] = None		):
       SCREAMING_SNAKE_CASE_					=    self._tokenizer.model.save(_lowerCAmelCase       ,       name=_lowerCAmelCase		)
       return tuple(_lowerCAmelCase		)




 def    lowerCAmelCase_						(							self						:	List[str]       ,       _lowerCAmelCase						:	"Conversation"		):
       SCREAMING_SNAKE_CASE_					=    []
       for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(_lowerCAmelCase       ,       add_special_tokens=_lowerCAmelCase		) + [self.eos_token_id]		)

       if len(_lowerCAmelCase		) > self.model_max_length:
             SCREAMING_SNAKE_CASE_					=    input_ids[-self.model_max_length :]
       return input_ids





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_							(   _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					RePaintPipeline
 lowercase_       					=					IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
 lowercase_       					=					PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_       					=					IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	int		):
       torch.manual_seed(0		)
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    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')       ,       )
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler()
       SCREAMING_SNAKE_CASE_					=    {'unet': unet, 'scheduler': scheduler}
       return components



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

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

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_					=    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    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_inference_batch_single_identical()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_optional_components()




 @skip_mps
 def    lowerCAmelCase_						(							self						:	Tuple		):
       return super().test_attention_slicing_forward_pass()





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


 '''simple docstring'''







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




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

       SCREAMING_SNAKE_CASE_					=    'google/ddpm-ema-celebahq-256'
       SCREAMING_SNAKE_CASE_					=    UNetaDModel.from_pretrained(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler.from_pretrained(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(unet=_lowerCAmelCase       ,       scheduler=_lowerCAmelCase		).to(_lowerCAmelCase		)
       repaint.set_progress_bar_config(disable=_lowerCAmelCase		)
       repaint.enable_attention_slicing()

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

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