
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__     :		int        = logging.get_logger(__name__)

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

lowerCamelCase__     :		int        = {
    """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__     :		Dict        = {
    """squeezebert/squeezebert-uncased""": 5_1_2,
    """squeezebert/squeezebert-mnli""": 5_1_2,
    """squeezebert/squeezebert-mnli-headless""": 5_1_2,
}


lowerCamelCase__     :		List[str]        = {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[Any]	   =      VOCAB_FILES_NAMES
 __lowerCAmelCase	:  str	   =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase	:  List[str]	   =      PRETRAINED_INIT_CONFIGURATION
 __lowerCAmelCase	:  Union[str, Any]	   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase	:  Dict	   =      SqueezeBertTokenizer


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_="[UNK]"       ,	SCREAMING_SNAKE_CASE_="[SEP]"       ,	SCREAMING_SNAKE_CASE_="[PAD]"       ,	SCREAMING_SNAKE_CASE_="[CLS]"       ,	SCREAMING_SNAKE_CASE_="[MASK]"       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




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

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

   lowercase__	: Optional[int]     		=   do_lower_case


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: Dict     		=   [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       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: Dict     		=   [self.sep_token_id]
   lowercase__	: 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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




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













"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )
  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )

  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }

  return inputs



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()				,       reason='XFormers attention is only available with CUDA and `xformers` installed'				,       )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_save_load_floataa(expected_max_diff=1E-1     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(
      expected_max_diff=1E-2				,       )





