





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_rembert import RemBertTokenizer
else:
 _lowerCAmelCase				:   List[Any]						       =						None

_lowerCAmelCase				:   int						       =						logging.get_logger(__name__)
_lowerCAmelCase				:   Optional[Any]						       =						{'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCAmelCase				:   Any						       =						{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

_lowerCAmelCase				:   Any						       =						{
    '''google/rembert''': 256,
}

_lowerCAmelCase				:   Any						       =						'''▁'''


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  RemBertTokenizer



     def __init__(				self							:int      ,   snake_case							:str=None      ,   snake_case							:List[Any]=None      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:int=False      ,   snake_case							:Tuple="[CLS]"      ,   snake_case							:Any="[SEP]"      ,   snake_case							:List[Any]="<unk>"      ,   snake_case							:str="[SEP]"      ,   snake_case							:Tuple="<pad>"      ,   snake_case							:List[str]="[CLS]"      ,   snake_case							:str="[MASK]"      ,   **snake_case							:Optional[int]      ,   ):





        '''simple docstring'''




        A_       :					Optional[Any]        =      AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else mask_token

        super().__init__(
            snake_case      ,   tokenizer_file=snake_case      ,   do_lower_case=snake_case      ,   remove_space=snake_case      ,   keep_accents=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   unk_token=snake_case      ,   sep_token=snake_case      ,   pad_token=snake_case      ,   cls_token=snake_case      ,   mask_token=snake_case      ,   **snake_case      ,   )

        A_       :					List[Any]        =      do_lower_case
        A_       :					int        =      remove_space
        A_       :					Dict        =      keep_accents
        A_       :					int        =      vocab_file
        A_       :					Dict        =      False if not self.vocab_file else True



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None      ,   snake_case							:bool = False	):





        '''simple docstring'''




        if already_has_special_tokens:
           if token_ids_a is not None:
              raise ValueError(
                  "You should not supply a second sequence if the provided sequence of "
                  "ids is already formatted with special tokens for the model."	)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
           return [1] + ([0] * len(snake_case	)) + [1] + ([0] * len(snake_case	)) + [1]
        return [1] + ([0] * len(snake_case	)) + [1]



     def     SCREAMING_SNAKE_CASE		(				self							:Any      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [self.sep_token_id]
        A_       :					List[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	) * [0] + len(token_ids_a + sep	) * [1]





     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




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

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

        return (out_vocab_file,)







import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, 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        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        super().tearDown()
        gc.collect()



     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_   ,    A_       :					Optional[Any]        =      FlaxStableDiffusionPipeline.from_pretrained(
            "stabilityai/stable-diffusion-2"      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )

        A_       :					List[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					int        =      jax.device_count()
        A_       :					Union[str, Any]        =      num_samples * [prompt]
        A_       :					Dict        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					List[Any]        =      replicate(snake_case	)
        A_       :					str        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Dict        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					int        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[str]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Dict        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					List[str]        =      jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					str        =      "stabilityai/stable-diffusion-2"
        A_   ,    A_       :					Dict        =      FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case      ,   subfolder="scheduler"	)
        A_   ,    A_       :					Any        =      FlaxStableDiffusionPipeline.from_pretrained(
            snake_case      ,   scheduler=snake_case      ,   revision="bf16"      ,   dtype=jnp.bfloataa      ,   )
        A_       :					Any        =      scheduler_params

        A_       :					Optional[Any]        =      "A painting of a squirrel eating a burger"
        A_       :					Dict        =      jax.device_count()
        A_       :					List[str]        =      num_samples * [prompt]
        A_       :					int        =      sd_pipe.prepare_inputs(snake_case	)

        A_       :					Optional[Any]        =      replicate(snake_case	)
        A_       :					Dict        =      shard(snake_case	)

        A_       :					Any        =      jax.random.PRNGKey(0	)
        A_       :					Union[str, Any]        =      jax.random.split(snake_case      ,   jax.device_count()	)

        A_       :					Union[str, Any]        =      sd_pipe(snake_case      ,   snake_case      ,   snake_case      ,   num_inference_steps=25      ,   jit=snake_case	)[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        A_       :					List[Any]        =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]	)
        A_       :					Union[str, Any]        =      images[0, 253:256, 253:256, -1]

        A_       :					Tuple        =      jnp.asarray(jax.device_get(image_slice.flatten()	)	)
        A_       :					str        =      jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]	)
        print(f"output_slice: {output_slice}"	)
        assert jnp.abs(output_slice - expected_slice	).max() < 1e-2
