


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_barthez import BarthezTokenizer
else:
  UpperCAmelCase__   	=							None

UpperCAmelCase__   	=							logging.get_logger(__name__)

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

UpperCAmelCase__   	=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__   	=							{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

UpperCAmelCase__   	=							'''▁'''







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__      			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__      			=			['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__      			=			BarthezTokenizer





     def __init__(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase="<s>"		,     UpperCAmelCase="</s>"		,     UpperCAmelCase="</s>"		,     UpperCAmelCase="<s>"		,     UpperCAmelCase="<unk>"		,     UpperCAmelCase="<pad>"		,     UpperCAmelCase="<mask>"		,     **UpperCAmelCase		,     )     ->   List[str]:
      # Mask token behave like a normal word, i.e. include the space before it
      _lowercase							      =AddedToken(UpperCAmelCase		,     lstrip=UpperCAmelCase		,     rstrip=UpperCAmelCase       ) if isinstance(UpperCAmelCase		,     UpperCAmelCase       ) else mask_token

      super().__init__(
          UpperCAmelCase		,     tokenizer_file=UpperCAmelCase		,     bos_token=UpperCAmelCase		,     eos_token=UpperCAmelCase		,     unk_token=UpperCAmelCase		,     sep_token=UpperCAmelCase		,     cls_token=UpperCAmelCase		,     pad_token=UpperCAmelCase		,     mask_token=UpperCAmelCase		,     **UpperCAmelCase		,     )

      _lowercase							      =vocab_file
      _lowercase							      =False if not self.vocab_file else True





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[int]:

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   List[int]:
      _lowercase							      =[self.sep_token_id]
      _lowercase							      =[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      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   Tuple[str]:
      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(UpperCAmelCase       ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
       return
      _lowercase							      =os.path.join(
          UpperCAmelCase		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

      return (out_vocab_file,)





import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard







@slow
@require_flax
class       lowerCamelCase__   (       unittest.TestCase):





     def      __A					(self       )     ->   List[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''       )
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )

      _lowercase							      ='''xvjiarui/stable-diffusion-2-inpainting'''
      _lowercase      ,    _lowercase							      =FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase		,     safety_checker=UpperCAmelCase       )

      _lowercase							      ='''Face of a yellow cat, high resolution, sitting on a park bench'''

      _lowercase							      =jax.random.PRNGKey(0       )
      _lowercase							      =5_0

      _lowercase							      =jax.device_count()
      _lowercase							      =num_samples * [prompt]
      _lowercase							      =num_samples * [init_image]
      _lowercase							      =num_samples * [mask_image]
      _lowercase      ,    _lowercase      ,    _lowercase							      =pipeline.prepare_inputs(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )

      # shard inputs and rng
      _lowercase							      =replicate(UpperCAmelCase       )
      _lowercase							      =jax.random.split(UpperCAmelCase		,     jax.device_count()       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )

      _lowercase							      =pipeline(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     jit=UpperCAmelCase       )

      _lowercase							      =output.images.reshape(UpperCAmelCase		,     5_1_2		,     5_1_2		,     3       )

      _lowercase							      =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      _lowercase							      =jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      _lowercase							      =jnp.array(
          [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]       )
      print(f"output_slice: {output_slice}"       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2

