
'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCAmelCase         =    {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCAmelCase         =    {
    """squeezebert/squeezebert-uncased""": 5_1_2,
    """squeezebert/squeezebert-mnli""": 5_1_2,
    """squeezebert/squeezebert-mnli-headless""": 5_1_2,
}


__lowerCAmelCase         =    {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[int]   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				List[str]   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Optional[int]   =					PRETRAINED_INIT_CONFIGURATION
  __UpperCAmelCase				:				int   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				List[str]   =					SqueezeBertTokenizer


  def __init__(		self      :     List[str]							,_a      :     str=None							,_a      :     int=None							,_a      :     int=True							,_a      :     Any="[UNK]"							,_a      :     str="[SEP]"							,_a      :     Any="[PAD]"							,_a      :     List[str]="[CLS]"							,_a      :     int="[MASK]"							,_a      :     Any=True							,_a      :     Any=None							,**_a      :     Tuple							,):


     '''simple docstring'''






     super().__init__(
         _a							,tokenizer_file=_a							,do_lower_case=_a							,unk_token=_a							,sep_token=_a							,pad_token=_a							,cls_token=_a							,mask_token=_a							,tokenize_chinese_chars=_a							,strip_accents=_a							,**_a							,)

     _a				:	Any			=   json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
     if (
         normalizer_state.get('lowercase'							,_a       ) != do_lower_case
         or normalizer_state.get('strip_accents'							,_a       ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'							,_a       ) != tokenize_chinese_chars
     ):
        _a				:	List[str]			=   getattr(_a							,normalizer_state.pop('type'       )       )
        _a				:	Any			=   do_lower_case
        _a				:	Union[str, Any]			=   strip_accents
        _a				:	Any			=   tokenize_chinese_chars
        _a				:	Optional[int]			=   normalizer_class(**_a       )

     _a				:	List[str]			=   do_lower_case


  def   __lowercase		(		self      :     Tuple							,_a      :     Any							,_a      :     Dict=None       ):


     '''simple docstring'''






     _a				:	Tuple			=   [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   __lowercase		(		self      :     int							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Tuple							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self._tokenizer.model.save(_a							,name=_a       )
     return tuple(_a       )





'''simple docstring'''







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   VQModel(
         block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,latent_channels=3							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   CLIPTextConfig(
         bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=1000							,)
     return CLIPTextModel(_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.dummy_uncond_unet
     _a				:	List[Any]			=   DDIMScheduler()
     _a				:	List[Any]			=   self.dummy_vq_model

     _a				:	str			=   LDMPipeline(unet=_a							,vqvae=_a							,scheduler=_a       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	List[str]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Tuple			=   image[0, -3:, -3:, -1]
     _a				:	Optional[Any]			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _a				:	int			=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
     _a				:	Any			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Dict			=   ldm(generator=_a							,num_inference_steps=5							,output_type='numpy'       ).images

     _a				:	str			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     _a				:	Optional[int]			=   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
     _a				:	int			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance



