



'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp



@slow
@require_flax
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							int      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return f'''gaussian_noise_s={seed}_shape={"_".join([str(__a  ) for s in shape]  )}.npy'''
 def A_   (   self						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def A_   (   self						:							List[Any]      ,     __a						:							Union[str, Any]=0      ,     __a						:							List[str]=(4, 4, 64, 64)      ,     __a						:							Optional[Any]=False  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   List[Any]													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return image
 def A_   (   self						:							Any      ,     __a						:							Any=False      ,     __a						:							Dict="CompVis/stable-diffusion-v1-4"  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   int													=    'bf16' if fpaa else None

       __snake_case							,     __snake_case :   Union[str, Any]													=    FlaxUNetaDConditionModel.from_pretrained(
           __a      ,     subfolder='unet'      ,     dtype=__a      ,     revision=__a  )
       return model, params
 def A_   (   self						:							Any      ,     __a						:							Dict=0      ,     __a						:							Dict=(4, 77, 768)      ,     __a						:							List[str]=False  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   Any													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return hidden_states
 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
         [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
         [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
         [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
         # fmt: on
     ]  )
 def A_   (   self						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,     fpaa=__a  )
       __snake_case :   Tuple													=    self.get_latents(__a      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_encoder_hidden_states(__a      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   str													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )





 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
         [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
         [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
         [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
         # fmt: on
     ]  )
 def A_   (   self						:							str      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_latents(__a      ,     shape=(4, 4, 96, 96)      ,     fpaa=__a  )
       __snake_case :   Optional[Any]													=    self.get_encoder_hidden_states(__a      ,     shape=(4, 77, 1024)      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   int													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )








'''simple docstring'''





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


A__    :   Tuple           =					logging.get_logger(__name__)

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

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

A__    :   List[str]           =					{
    '''camembert-base''': 5_1_2,
}

A__    :   str           =					'''▁'''



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= VOCAB_FILES_NAMES
 A__				= PRETRAINED_VOCAB_FILES_MAP
 A__				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 A__				= ['''input_ids''', '''attention_mask''']
 def __init__(   self						:							List[Any]      ,     __a						:							int      ,     __a						:							str="<s>"      ,     __a						:							Dict="</s>"      ,     __a						:							Union[str, Any]="</s>"      ,     __a						:							List[str]="<s>"      ,     __a						:							Optional[int]="<unk>"      ,     __a						:							Optional[Any]="<pad>"      ,     __a						:							str="<mask>"      ,     __a						:							Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]      ,     __a						:							Optional[Dict[str, Any]] = None      ,     **__a						:							Any      ,     )      ->   None:







       '''simple docstring'''
       # Mask token behave like a normal word, i.e. include the space before it
       __snake_case :   Optional[int]													=    AddedToken(__a      ,     lstrip=__a      ,     rstrip=__a  ) if isinstance(__a      ,     __a  ) else mask_token

       __snake_case :   Optional[Any]													=    {} 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      ,     )
       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(str(__a  )  )
       __snake_case :   str													=    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>
       __snake_case :   Any													=    {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
       __snake_case :   Optional[int]													=    len(self.fairseq_tokens_to_ids  )
       __snake_case :   str													=    len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
       __snake_case :   Dict													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
 def A_   (   self						:							List[str]      ,     __a						:							List[int]      ,     __a						:							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]
       __snake_case :   Dict													=    [self.cls_token_id]
       __snake_case :   Optional[int]													=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None      ,     __a						:							bool = False  )      ->   List[int]:







       '''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 A_   (   self						:							int      ,     __a						:							List[int]      ,     __a						:							Optional[List[int]] = None  )      ->   List[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    [self.sep_token_id]
       __snake_case :   Optional[int]													=    [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 A_   (   self						:							Dict  )      ->   List[str]:







       '''simple docstring'''
       return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )
 def A_   (   self						:							List[str]  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    {self.convert_ids_to_tokens(__a  ): i for i in range(self.vocab_size  )}
       vocab.update(self.added_tokens_encoder  )
       return vocab
 def A_   (   self						:							Dict      ,     __a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return self.sp_model.encode(__a      ,     out_type=__a  )
 def A_   (   self						:							Optional[Any]      ,     __a						:							List[str]  )      ->   List[Any]:







       '''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 A_   (   self						:							Optional[int]      ,     __a						:							List[Any]  )      ->   Optional[Any]:







       '''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 A_   (   self						:							Dict      ,     __a						:							Union[str, Any]  )      ->   Tuple:







       '''simple docstring'''
       __snake_case :   Tuple													=    []
       __snake_case :   List[Any]													=    ''
       __snake_case :   Optional[int]													=    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
                   __snake_case :   Optional[int]													=    True
                   __snake_case :   Any													=    []
             else:
                   current_sub_tokens.append(__a  )
                   __snake_case :   Dict													=    False
       out_string += self.sp_model.decode(__a  )
       return out_string.strip()
 def __getstate__(   self						:							str  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.__dict__.copy()
       __snake_case :   int													=    None
       return state
 def __setstate__(   self						:							List[str]      ,     __a						:							List[str]  )      ->   Dict:







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

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

       __snake_case :   Optional[Any]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
       self.sp_model.Load(self.vocab_file  )





 def A_   (   self						:							Tuple      ,     __a						:							str      ,     __a						:							Optional[str] = None  )      ->   Tuple[str]:







       '''simple docstring'''
       if not os.path.isdir(__a  ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
             return
       __snake_case :   Dict													=    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:
                   __snake_case :   Union[str, Any]													=    self.sp_model.serialized_model_proto()
                   fi.write(__a  )

       return (out_vocab_file,)



