





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 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Tuple:







          '''simple docstring'''

          return F'gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase_) for s in shape])}.npy'

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase=0 ,  lowercase=(4, 4, 64, 64) ,  lowercase=False)      ->						List[str]:







          '''simple docstring'''

          a__						:     Optional[Any]			=    jnp.bfloataa if fpaa else jnp.floataa
          a__						:     List[str]			=    jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_ ,  lowerCamelCase_)) ,  dtype=lowerCamelCase_)
          return image

   def 							__lowercase		( self ,  lowercase=False ,  lowercase="CompVis/stable-diffusion-v1-4")      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     int			=    jnp.bfloataa if fpaa else jnp.floataa
          a__						:     Optional[Any]			=    """bf16""" if fpaa else None

          a__						:     Any			=    FlaxUNetaDConditionModel.from_pretrained(
              lowerCamelCase_ ,  subfolder='unet' ,  dtype=lowerCamelCase_ ,  revision=lowerCamelCase_)
          return model, params

   def 							__lowercase		( self ,  lowercase=0 ,  lowercase=(4, 77, 768) ,  lowercase=False)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    jnp.bfloataa if fpaa else jnp.floataa
          a__						:     Any			=    jnp.array(load_hf_numpy(self.get_file_format(lowerCamelCase_ ,  lowerCamelCase_)) ,  dtype=lowerCamelCase_)
          return hidden_states

   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.23_23, -0.13_04, 0.08_13, -0.30_93, -0.09_19, -0.15_71, -0.11_25, -0.58_06]],
           [17, 0.55, [-0.08_31, -0.24_43, 0.09_01, -0.09_19, 0.33_96, 0.01_03, -0.37_43, 0.07_01]],
           [8, 0.89, [-0.48_63, 0.08_59, 0.08_75, -0.16_58, 0.91_99, -0.01_14, 0.48_39, 0.46_39]],
           [3, 1000, [-0.56_49, 0.24_02, -0.55_18, 0.12_48, 1.13_28, -0.24_43, -0.03_25, -1.00_78]],
           # fmt: on
       ])
   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     List[str]			=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4' ,  fpaa=lowerCamelCase_)
          a__						:     Tuple			=    self.get_latents(lowerCamelCase_ ,  fpaa=lowerCamelCase_)
          a__						:     Union[str, Any]			=    self.get_encoder_hidden_states(lowerCamelCase_ ,  fpaa=lowerCamelCase_)

          a__						:     int			=    model.apply(
              {'params': params} ,  lowerCamelCase_ ,  jnp.array(lowerCamelCase_ ,  dtype=jnp.intaa) ,  encoder_hidden_states=lowerCamelCase_ ,  ).sample

          assert sample.shape == latents.shape

          a__						:     Any			=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())) ,  dtype=jnp.floataa)
          a__						:     Dict			=    jnp.array(lowerCamelCase_ ,  dtype=jnp.floataa)

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

   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.15_14, 0.08_07, 0.16_24, 0.10_16, -0.18_96, 0.02_63, 0.06_77, 0.23_10]],
           [17, 0.55, [0.11_64, -0.02_16, 0.01_70, 0.15_89, -0.31_20, 0.10_05, -0.05_81, -0.14_58]],
           [8, 0.89, [-0.17_58, -0.01_69, 0.10_04, -0.14_11, 0.13_12, 0.11_03, -0.19_96, 0.21_39]],
           [3, 1000, [0.12_14, 0.03_52, -0.07_31, -0.15_62, -0.09_94, -0.09_06, -0.23_40, -0.05_39]],
           # fmt: on
       ])
   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     int			=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2' ,  fpaa=lowerCamelCase_)
          a__						:     List[Any]			=    self.get_latents(lowerCamelCase_ ,  shape=(4, 4, 96, 96) ,  fpaa=lowerCamelCase_)
          a__						:     Dict			=    self.get_encoder_hidden_states(lowerCamelCase_ ,  shape=(4, 77, 1024) ,  fpaa=lowerCamelCase_)

          a__						:     List[Any]			=    model.apply(
              {'params': params} ,  lowerCamelCase_ ,  jnp.array(lowerCamelCase_ ,  dtype=jnp.intaa) ,  encoder_hidden_states=lowerCamelCase_ ,  ).sample

          assert sample.shape == latents.shape

          a__						:     List[str]			=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())) ,  dtype=jnp.floataa)
          a__						:     Dict			=    jnp.array(lowerCamelCase_ ,  dtype=jnp.floataa)

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









import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowercase				:	List[Any]      						=  logging.get_logger(__name__)


lowercase				:	Any      						=  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowercase				:	Optional[Any]      						=  {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

lowercase				:	List[str]      						=  {
    """RUCAIBox/mvp""": 1_0_2_4,
}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Dict	  =  VOCAB_FILES_NAMES
   __A       :    str	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    Tuple	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Any	  =  ['''input_ids''', '''attention_mask''']
   __A       :    Tuple	  =  MvpTokenizer

   def __init__( self ,  lowercase=None ,  lowercase=None ,  lowercase=None ,  lowercase="replace" ,  lowercase="<s>" ,  lowercase="</s>" ,  lowercase="</s>" ,  lowercase="<s>" ,  lowercase="<unk>" ,  lowercase="<pad>" ,  lowercase="<mask>" ,  lowercase=False ,  lowercase=True ,  **lowercase ,  )      ->						str:







          '''simple docstring'''

          super().__init__(
              lowercase ,  lowercase ,  tokenizer_file=lowercase ,  errors=lowercase ,  bos_token=lowercase ,  eos_token=lowercase ,  sep_token=lowercase ,  cls_token=lowercase ,  unk_token=lowercase ,  pad_token=lowercase ,  mask_token=lowercase ,  add_prefix_space=lowercase ,  trim_offsets=lowercase ,  **lowercase ,  )

          a__						:     Optional[int]			=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
          if pre_tok_state.get('add_prefix_space' ,  lowercase) != add_prefix_space:
                 a__						:     Dict			=    getattr(lowercase ,  pre_tok_state.pop('type'))
                 a__						:     str			=    add_prefix_space
                 a__						:     Union[str, Any]			=    pre_tok_class(**lowercase)

          a__						:     List[Any]			=    add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          a__						:     Optional[int]			=    'post_processor'
          a__						:     Optional[int]			=    getattr(self.backend_tokenizer ,  lowercase ,  lowercase)
          if tokenizer_component_instance:
                 a__						:     str			=    json.loads(tokenizer_component_instance.__getstate__())

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                        a__						:     Any			=    tuple(state['sep'])
                 if "cls" in state:
                        a__						:     str			=    tuple(state['cls'])

                 a__						:     List[str]			=    False

                 if state.get('add_prefix_space' ,  lowercase) != add_prefix_space:
                        a__						:     List[str]			=    add_prefix_space
                        a__						:     List[str]			=    True

                 if state.get('trim_offsets' ,  lowercase) != trim_offsets:
                        a__						:     Optional[int]			=    trim_offsets
                        a__						:     List[str]			=    True

                 if changes_to_apply:
                        a__						:     Optional[int]			=    getattr(lowercase ,  state.pop('type'))
                        a__						:     Tuple			=    component_class(**lowercase)
                        setattr(self.backend_tokenizer ,  lowercase ,  lowercase)

   @property
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          if self._mask_token is None:
                 if self.verbose:
                        logger.error('Using mask_token, but it is not set yet.')
                 return None
          return str(self._mask_token)

   @mask_token.setter
   def 							__lowercase		( self ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    AddedToken(lowercase ,  lstrip=lowercase ,  rstrip=lowercase) if isinstance(lowercase ,  lowercase) else value
          a__						:     str			=    value

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     Optional[Any]			=    kwargs.get('is_split_into_words' ,  lowercase)

          if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                     'to use it with pretokenized inputs.')

          return super()._batch_encode_plus(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  *lowercase ,  **lowercase)      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     List[str]			=    kwargs.get('is_split_into_words' ,  lowercase)

          if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                     'to use it with pretokenized inputs.')

          return super()._encode_plus(*lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

          a__						:     List[str]			=    self._tokenizer.model.save(lowercase ,  name=lowercase)
          return tuple(lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase=None)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     List[Any]			=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                 return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          a__						:     int			=    [self.sep_token_id]
          a__						:     Any			=    [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]


