



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{"""vocab_file""": """vocab.txt"""}

UpperCAmelCase_		 =					{
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

UpperCAmelCase_		 =					{
    """YituTech/conv-bert-base""": 5_1_2,
    """YituTech/conv-bert-medium-small""": 5_1_2,
    """YituTech/conv-bert-small""": 5_1_2,
}


UpperCAmelCase_		 =					{
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: int =  VOCAB_FILES_NAMES
 a__					: List[str] =  PRETRAINED_VOCAB_FILES_MAP
 a__					: Optional[Any] =  PRETRAINED_INIT_CONFIGURATION
 a__					: str =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 a__					: int =  ConvBertTokenizer

 def __init__(    self					:					str			,						__lowerCAmelCase					:					Union[str, Any]=None			,						__lowerCAmelCase					:					int=None			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Any="[UNK]"			,						__lowerCAmelCase					:					Optional[Any]="[SEP]"			,						__lowerCAmelCase					:					Optional[int]="[PAD]"			,						__lowerCAmelCase					:					Optional[Any]="[CLS]"			,						__lowerCAmelCase					:					Optional[Any]="[MASK]"			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Any=None			,						**__lowerCAmelCase					:					List[str]			,						)   ->	Tuple:
    super().__init__(
        __lowerCAmelCase			,						tokenizer_file=__lowerCAmelCase			,						do_lower_case=__lowerCAmelCase			,						unk_token=__lowerCAmelCase			,						sep_token=__lowerCAmelCase			,						pad_token=__lowerCAmelCase			,						cls_token=__lowerCAmelCase			,						mask_token=__lowerCAmelCase			,						tokenize_chinese_chars=__lowerCAmelCase			,						strip_accents=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

    _A      =       json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
    if (
        normalizer_state.get('''lowercase'''			,						__lowerCAmelCase      ) != do_lower_case
        or normalizer_state.get('''strip_accents'''			,						__lowerCAmelCase      ) != strip_accents
        or normalizer_state.get('''handle_chinese_chars'''			,						__lowerCAmelCase      ) != tokenize_chinese_chars
    ):
       _A      =       getattr(__lowerCAmelCase			,						normalizer_state.pop('''type'''      )      )
       _A      =       do_lower_case
       _A      =       strip_accents
       _A      =       tokenize_chinese_chars
       _A      =       normalizer_class(**__lowerCAmelCase      )

    _A      =       do_lower_case

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Any=None      )   ->	List[str]:
    _A      =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

    return output

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					List[int]			,						__lowerCAmelCase					:					Optional[List[int]] = None      )   ->	List[int]:
    _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      ) * [0] + len(token_ids_a + sep      ) * [1]

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[str] = None      )   ->	Tuple[str]:
    _A      =       self._tokenizer.model.save(__lowerCAmelCase			,						name=__lowerCAmelCase      )
    return tuple(__lowerCAmelCase      )











"""simple docstring"""



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      :	Optional[int]							):


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




   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/sd2-inpaint/init_image.png"							)
        UpperCAmelCase__						:    Any							=							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"							)

        UpperCAmelCase__						:    str							=							"xvjiarui/stable-diffusion-2-inpainting"
        UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case__							,			safety_checker=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							"Face of a yellow cat, high resolution, sitting on a park bench"

        UpperCAmelCase__						:    List[str]							=							jax.random.PRNGKey(0							)
        UpperCAmelCase__						:    Optional[int]							=							5_0

        UpperCAmelCase__						:    List[Any]							=							jax.device_count()
        UpperCAmelCase__						:    int							=							num_samples * [prompt]
        UpperCAmelCase__						:    str							=							num_samples * [init_image]
        UpperCAmelCase__						:    Optional[Any]							=							num_samples * [mask_image]
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							pipeline.prepare_inputs(snake_case__							,			snake_case__							,			snake_case__							)

        # shard inputs and rng
        UpperCAmelCase__						:    Dict							=							replicate(snake_case__							)
        UpperCAmelCase__						:    Dict							=							jax.random.split(snake_case__							,			jax.device_count()							)
        UpperCAmelCase__						:    List[str]							=							shard(snake_case__							)
        UpperCAmelCase__						:    str							=							shard(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							shard(snake_case__							)

        UpperCAmelCase__						:    Dict							=							pipeline(
            snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			jit=snake_case__							)

        UpperCAmelCase__						:    List[str]							=							output.images.reshape(snake_case__							,			5_1_2							,			5_1_2							,			3							)

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

        UpperCAmelCase__						:    str							=							jnp.asarray(jax.device_get(image_slice.flatten()							)							)
        UpperCAmelCase__						:    int							=							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


