


'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
   __UpperCamelCase:   List[str]    =	None


__UpperCamelCase:   Any    =	logging.get_logger(__name__)

__UpperCamelCase:   Any    =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase:   Union[str, Any]    =	{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase:   Optional[Any]    =	{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  str     	=							VOCAB_FILES_NAMES
      __UpperCamelCase	:  List[str]     	=							PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	:  Optional[Any]     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	:  Optional[Any]     	=							["input_ids", "attention_mask"]
      __UpperCamelCase	:  Optional[Any]     	=							XLMRobertaTokenizer






      def __init__(			self      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Tuple=None					,						SCREAMING_SNAKE_CASE      :int="<s>"					,						SCREAMING_SNAKE_CASE      :Optional[Any]="</s>"					,						SCREAMING_SNAKE_CASE      :List[str]="</s>"					,						SCREAMING_SNAKE_CASE      :int="<s>"					,						SCREAMING_SNAKE_CASE      :int="<unk>"					,						SCREAMING_SNAKE_CASE      :Optional[int]="<pad>"					,						SCREAMING_SNAKE_CASE      :Any="<mask>"					,						**SCREAMING_SNAKE_CASE      :List[str]					,						)	-> str:




             '''simple docstring'''
             _a   :			List[str]					 =AddedToken(SCREAMING_SNAKE_CASE					,						lstrip=SCREAMING_SNAKE_CASE					,						rstrip=SCREAMING_SNAKE_CASE     ) if isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ) else mask_token

             super().__init__(
                 SCREAMING_SNAKE_CASE					,						tokenizer_file=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					,						**SCREAMING_SNAKE_CASE					,						)

             _a   :			Any					 =vocab_file
             _a   :			List[str]					 =False if not self.vocab_file else True






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :List[int]					,						SCREAMING_SNAKE_CASE      :Optional[List[int]] = None     )	-> List[int]:




             '''simple docstring'''

             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             _a   :			List[str]					 =[self.cls_token_id]
             _a   :			Union[str, Any]					 =[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep






      def __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :List[int]					,						SCREAMING_SNAKE_CASE      :Optional[List[int]] = None     )	-> List[int]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =[self.sep_token_id]
             _a   :			str					 =[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 __UpperCAmelCase	(			self      :str					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[str] = None     )	-> Tuple[str]:




             '''simple docstring'''
             if not self.can_save_slow_tokenizer:
                    raise ValueError(
                        """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                        """tokenizer."""     )

             if not os.path.isdir(SCREAMING_SNAKE_CASE     ):
                    logger.error(f"Vocabulary path ({save_directory}) should be a directory."     )
                    return
             _a   :			Optional[Any]					 =os.path.join(
                 SCREAMING_SNAKE_CASE					,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

             if os.path.abspath(self.vocab_file     ) != os.path.abspath(SCREAMING_SNAKE_CASE     ):
                    copyfile(self.vocab_file					,						SCREAMING_SNAKE_CASE     )

             return (out_vocab_file,)






'''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 A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  str     	=							RePaintPipeline
      __UpperCamelCase	:  Dict     	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
      __UpperCamelCase	:  List[str]     	=							PipelineTesterMixin.required_optional_params - {
          "latents",
          "num_images_per_prompt",
          "callback",
          "callback_steps",
      }
      __UpperCamelCase	:  List[Any]     	=							IMAGE_INPAINTING_BATCH_PARAMS
      __UpperCamelCase	:  Tuple     	=							False






      def __UpperCAmelCase	(			self      :List[Any]     )	-> List[str]:




             '''simple docstring'''
             torch.manual_seed(0     )
             torch.manual_seed(0     )
             _a   :			Tuple					 =UNetaDModel(
                 block_out_channels=(3_2, 6_4)					,						layers_per_block=2					,						sample_size=3_2					,						in_channels=3					,						out_channels=3					,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")					,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")					,						)
             _a   :			Any					 =RePaintScheduler()
             _a   :			Optional[Any]					 ={"""unet""": unet, """scheduler""": scheduler}
             return components






      def __UpperCAmelCase	(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Optional[Any]=0     )	-> Any:




             '''simple docstring'''
             if str(SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
                    _a   :			Optional[Any]					 =torch.manual_seed(SCREAMING_SNAKE_CASE     )
             else:
                    _a   :			Any					 =torch.Generator(device=SCREAMING_SNAKE_CASE     ).manual_seed(SCREAMING_SNAKE_CASE     )
             _a   :			int					 =np.random.RandomState(SCREAMING_SNAKE_CASE     ).standard_normal((1, 3, 3_2, 3_2)     )
             _a   :			Union[str, Any]					 =torch.from_numpy(SCREAMING_SNAKE_CASE     ).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			List[Any]					 =(image > 0).to(device=SCREAMING_SNAKE_CASE					,						dtype=torch.floataa     )
             _a   :			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 __UpperCAmelCase	(			self      :Dict     )	-> Dict:




             '''simple docstring'''
             _a   :			Tuple					 ="""cpu"""  # ensure determinism for the device-dependent torch.Generator
             _a   :			Tuple					 =self.get_dummy_components()
             _a   :			str					 =RePaintPipeline(**SCREAMING_SNAKE_CASE     )
             _a   :			int					 =sd_pipe.to(SCREAMING_SNAKE_CASE     )
             sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE     )

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

             assert image.shape == (1, 3_2, 3_2, 3)
             _a   :			str					 =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 __UpperCAmelCase	(			self      :Union[str, Any]     )	-> Optional[int]:




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






      @unittest.skip("""non-deterministic pipeline"""     )
      def __UpperCAmelCase	(			self      :Tuple     )	-> int:




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






      @skip_mps
      def __UpperCAmelCase	(			self      :Any     )	-> Tuple:




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






      @skip_mps
      def __UpperCAmelCase	(			self      :str     )	-> Optional[Any]:




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






      @skip_mps
      def __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




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







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






      def __UpperCAmelCase	(			self      :Tuple     )	-> Optional[int]:




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






      def __UpperCAmelCase	(			self      :str     )	-> int:




             '''simple docstring'''
             _a   :			Optional[int]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256.png"""     )
             _a   :			Optional[Any]					 =load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""     )
             _a   :			Union[str, Any]					 =load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
                 """repaint/celeba_hq_256_result.npy"""     )

             _a   :			str					 ="""google/ddpm-ema-celebahq-256"""
             _a   :			Union[str, Any]					 =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE     )
             _a   :			Dict					 =RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE     )

             _a   :			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()

             _a   :			Dict					 =torch.manual_seed(0     )
             _a   :			Any					 =repaint(
                 SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						num_inference_steps=2_5_0					,						eta=0.0					,						jump_length=1_0					,						jump_n_sample=1_0					,						generator=SCREAMING_SNAKE_CASE					,						output_type="""np"""					,						)
             _a   :			Any					 =output.images[0]

             assert image.shape == (2_5_6, 2_5_6, 3)
             assert np.abs(expected_image - image     ).mean() < 1e-2


