

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      :					Tuple					    =      logging.get_logger(__name__)

_lowerCamelCase      :					int					    =      {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase      :					int					    =      {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

_lowerCamelCase      :					List[str]					    =      {
    '''camembert-base''': 512,
}

_lowerCamelCase      :					str					    =      '''▁'''





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"""]

       def __init__(							self ,   lowercase__ ,   lowercase__="<s>" ,   lowercase__="</s>" ,   lowercase__="</s>" ,   lowercase__="<s>" ,   lowercase__="<unk>" ,   lowercase__="<pad>" ,   lowercase__="<mask>" ,   lowercase__=["<s>NOTUSED", "</s>NOTUSED"] ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(__a ,   lstrip=__a ,   rstrip=__a     ) if isinstance(__a ,   __a     ) else mask_token

             __A    ={} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=__a ,   eos_token=__a ,   unk_token=__a ,   sep_token=__a ,   cls_token=__a ,   pad_token=__a ,   mask_token=__a ,   additional_special_tokens=__a ,   sp_model_kwargs=self.sp_model_kwargs ,   **__a ,   )
             __A    =spm.SentencePieceProcessor(**self.sp_model_kwargs     )
             self.sp_model.Load(str(__a     )     )
             __A    =vocab_file
             # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
             # sentencepiece vocabulary (this is the case for <s> and </s>
             __A    ={'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
             __A    =len(self.fairseq_tokens_to_ids     )
             __A    =len(self.sp_model     ) + len(self.fairseq_tokens_to_ids     )
             __A    ={v: k for k, v in self.fairseq_tokens_to_ids.items()}

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __A    =[self.cls_token_id]
             __A    =[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=__a ,   token_ids_a=__a ,   already_has_special_tokens=__a     )

             if token_ids_a is None:
                   return [1] + ([0] * len(__a     )) + [1]
             return [1] + ([0] * len(__a     )) + [1, 1] + ([0] * len(__a     )) + [1]

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[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]

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return len(self.fairseq_tokens_to_ids     ) + len(self.sp_model     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ={self.convert_ids_to_tokens(__a     ): i for i in range(self.vocab_size     )}
             vocab.update(self.added_tokens_encoder     )
             return vocab

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return self.sp_model.encode(__a ,   out_type=__a     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
             elif self.sp_model.PieceToId(__a     ) == 0:
                   # Convert sentence piece unk token to fairseq unk token index
                   return self.unk_token_id
             return self.fairseq_offset + self.sp_model.PieceToId(__a     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             if index in self.fairseq_ids_to_tokens:
                   return self.fairseq_ids_to_tokens[index]
             return self.sp_model.IdToPiece(index - self.fairseq_offset     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =[]
             __A    =''
             __A    =False
             for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                         if not prev_is_special:
                               out_string += " "
                         out_string += self.sp_model.decode(__a     ) + token
                         __A    =True
                         __A    =[]
                   else:
                         current_sub_tokens.append(__a     )
                         __A    =False
             out_string += self.sp_model.decode(__a     )
             return out_string.strip()

       def __getstate__(							self     ):







             '''simple docstring'''



             __A    =self.__dict__.copy()
             __A    =None
             return state

       def __setstate__(							self ,   lowercase__     ):







             '''simple docstring'''



             __A    =d

             # for backward compatibility
             if not hasattr(self ,   '''sp_model_kwargs'''     ):
                   __A    ={}

             __A    =spm.SentencePieceProcessor(**self.sp_model_kwargs     )
             self.sp_model.Load(self.vocab_file     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



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

             if os.path.abspath(self.vocab_file     ) != os.path.abspath(__a     ) and os.path.isfile(self.vocab_file     ):
                   copyfile(self.vocab_file ,   __a     )
             elif not os.path.isfile(self.vocab_file     ):
                   with open(__a ,   '''wb'''     ) as fi:
                         __A    =self.sp_model.serialized_model_proto()
                         fi.write(__a     )

             return (out_vocab_file,)







'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  snake_case__						(    SCREAMING_SNAKE_CASE_  ,     unittest.TestCase				):
 A__				= KandinskyInpaintPipeline
 A__				= ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 A__				= [
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 A__				= [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 A__				= False
 @property
 def A_   (   self						:							Dict  )      ->   Union[str, Any]:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Optional[int]  )      ->   Tuple:







       '''simple docstring'''
       return 32
 @property
 def A_   (   self						:							Dict  )      ->   str:







       '''simple docstring'''
       return self.time_input_dim
 @property
 def A_   (   self						:							Dict  )      ->   Any:







       '''simple docstring'''
       return self.time_input_dim * 4
 @property
 def A_   (   self						:							List[Any]  )      ->   List[str]:







       '''simple docstring'''
       return 100
 @property
 def A_   (   self						:							int  )      ->   Union[str, Any]:







       '''simple docstring'''
       __snake_case :   List[str]													=    XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
       return tokenizer
 @property
 def A_   (   self						:							Tuple  )      ->   str:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   str													=    MCLIPConfig(
           numDims=self.cross_attention_dim      ,     transformerDimensions=self.text_embedder_hidden_size      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     vocab_size=1005      ,     )

       __snake_case :   List[Any]													=    MultilingualCLIP(__a  )
       __snake_case :   List[str]													=    text_encoder.eval()

       return text_encoder
 @property
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )

       __snake_case :   Tuple													=    {
           'in_channels': 9,
           # Out channels is double in channels because predicts mean and variance
           'out_channels': 8,
           'addition_embed_type': 'text_image',
           'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
           'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
           'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
           'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
           'layers_per_block': 1,
           'encoder_hid_dim': self.text_embedder_hidden_size,
           'encoder_hid_dim_type': 'text_image_proj',
           'cross_attention_dim': self.cross_attention_dim,
           'attention_head_dim': 4,
           'resnet_time_scale_shift': 'scale_shift',
           'class_embed_type': None,
       }

       __snake_case :   Any													=    UNetaDConditionModel(**__a  )
       return model
 @property
 def A_   (   self						:							List[str]  )      ->   List[Any]:







       '''simple docstring'''
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }
 @property
 def A_   (   self						:							int  )      ->   List[str]:







       '''simple docstring'''
       torch.manual_seed(0  )
       __snake_case :   Optional[int]													=    VQModel(**self.dummy_movq_kwargs  )
       return model
 def A_   (   self						:							Dict  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    self.dummy_text_encoder
       __snake_case :   str													=    self.dummy_tokenizer
       __snake_case :   Any													=    self.dummy_unet
       __snake_case :   Optional[Any]													=    self.dummy_movq

       __snake_case :   Optional[Any]													=    DDIMScheduler(
           num_train_timesteps=1000      ,     beta_schedule='linear'      ,     beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     clip_sample=__a      ,     set_alpha_to_one=__a      ,     steps_offset=1      ,     prediction_type='epsilon'      ,     thresholding=__a      ,     )

       __snake_case :   List[str]													=    {
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }

       return components
 def A_   (   self						:							List[str]      ,     __a						:							Dict      ,     __a						:							int=0  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   int													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   str													=    floats_tensor((1, self.cross_attention_dim)      ,     rng=random.Random(seed + 1  )  ).to(__a  )
       # create init_image
       __snake_case :   Any													=    floats_tensor((1, 3, 64, 64)      ,     rng=random.Random(__a  )  ).to(__a  )
       __snake_case :   Optional[int]													=    image.cpu().permute(0      ,     2      ,     3      ,     1  )[0]
       __snake_case :   List[str]													=    Image.fromarray(np.uinta(__a  )  ).convert('RGB'  ).resize((256, 256)  )
       # create mask
       __snake_case :   Optional[int]													=    np.ones((64, 64)      ,     dtype=np.floataa  )
       __snake_case :   List[Any]													=    0

       if str(__a  ).startswith('mps'  ):
             __snake_case :   Any													=    torch.manual_seed(__a  )
       else:
             __snake_case :   Optional[int]													=    torch.Generator(device=__a  ).manual_seed(__a  )
       __snake_case :   int													=    {
           'prompt': 'horse',
           'image': init_image,
           'mask_image': mask,
           'image_embeds': image_embeds,
           'negative_image_embeds': negative_image_embeds,
           'generator': generator,
           'height': 64,
           'width': 64,
           'num_inference_steps': 2,
           'guidance_scale': 4.0,
           'output_type': 'np',
       }
       return inputs
 def A_   (   self						:							Any  )      ->   int:







       '''simple docstring'''
       __snake_case :   List[str]													=    'cpu'

       __snake_case :   Tuple													=    self.get_dummy_components()

       __snake_case :   Optional[int]													=    self.pipeline_class(**__a  )
       __snake_case :   Union[str, Any]													=    pipe.to(__a  )

       pipe.set_progress_bar_config(disable=__a  )

       __snake_case :   Optional[Any]													=    pipe(**self.get_dummy_inputs(__a  )  )
       __snake_case :   Dict													=    output.images

       __snake_case :   Union[str, Any]													=    pipe(
           **self.get_dummy_inputs(__a  )      ,     return_dict=__a      ,     )[0]

       __snake_case :   int													=    image[0, -3:, -3:, -1]
       __snake_case :   Optional[int]													=    image_from_tuple[0, -3:, -3:, -1]

       print(f'''image.shape {image.shape}'''  )

       assert image.shape == (1, 64, 64, 3)

       __snake_case :   List[Any]													=    np.array(
           [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]  )

       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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







       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3e-3  )



@slow
@require_torch_gpu
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def A_   (   self						:							Optional[int]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'  )

       __snake_case :   Tuple													=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
       __snake_case :   Tuple													=    np.ones((768, 768)      ,     dtype=np.floataa  )
       __snake_case :   Tuple													=    0

       __snake_case :   Tuple													=    'a hat'

       __snake_case :   Optional[Any]													=    KandinskyPriorPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-prior'      ,     torch_dtype=torch.floataa  )
       pipe_prior.to(__a  )

       __snake_case :   int													=    KandinskyInpaintPipeline.from_pretrained(
           'kandinsky-community/kandinsky-2-1-inpaint'      ,     torch_dtype=torch.floataa  )
       __snake_case :   Optional[Any]													=    pipeline.to(__a  )
       pipeline.set_progress_bar_config(disable=__a  )

       __snake_case :   Any													=    torch.Generator(device='cpu'  ).manual_seed(0  )
       __snake_case							,     __snake_case :   Union[str, Any]													=    pipe_prior(
           __a      ,     generator=__a      ,     num_inference_steps=5      ,     negative_prompt=''      ,     ).to_tuple()

       __snake_case :   List[str]													=    pipeline(
           __a      ,     image=__a      ,     mask_image=__a      ,     image_embeds=__a      ,     negative_image_embeds=__a      ,     generator=__a      ,     num_inference_steps=100      ,     height=768      ,     width=768      ,     output_type='np'      ,     )

       __snake_case :   int													=    output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__a      ,     __a  )



