"""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_mobilebert import MobileBertTokenizer


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE								={
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

__SCREAMING_SNAKE_CASE								={"mobilebert-uncased": 512}


__SCREAMING_SNAKE_CASE								={}







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_INIT_CONFIGURATION
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						MobileBertTokenizer
   def __init__(    self  ,__UpperCamelCase=None  ,__UpperCamelCase=None  ,__UpperCamelCase=True  ,__UpperCamelCase="[UNK]"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[PAD]"  ,__UpperCamelCase="[CLS]"  ,__UpperCamelCase="[MASK]"  ,__UpperCamelCase=True  ,__UpperCamelCase=None  ,**__UpperCamelCase  ,)			->   Optional[int]:


        '''simple docstring'''


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

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

        lowercase_			:       Optional[int]									=			do_lower_case
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=None    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       int									=			[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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			[self.sep_token_id]
        lowercase_			:       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     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Tuple[str]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			self._tokenizer.model.save(__UpperCamelCase  ,name=__UpperCamelCase    )
        return tuple(__UpperCamelCase    )


"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						IFImgaImgSuperResolutionPipeline
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}						)
   lowercase							=						PipelineTesterMixin.required_optional_params - {'latents'}
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._get_superresolution_dummy_components()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=0    )			->   List[str]:


        '''simple docstring'''


        if str(__UpperCamelCase    ).startswith('mps'    ):
             lowercase_			:       Any									=			torch.manual_seed(__UpperCamelCase    )
        else:
             lowercase_			:       int									=			torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       List[str]									=			floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			{
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'original_image': original_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    )			->   Optional[Any]:


        '''simple docstring'''


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != 'cuda'  ,reason='float16 requires CUDA'    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        super().test_save_load_floataa(expected_max_diff=1e-1    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self._test_save_load_local()







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


        '''simple docstring'''


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

