

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_12,
    '''squeezebert/squeezebert-mnli''': 5_12,
    '''squeezebert/squeezebert-mnli-headless''': 5_12,
}


lowerCAmelCase_			      =      {
    '''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_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE	:  Optional[Any]		     =							PRETRAINED_INIT_CONFIGURATION
			SCREAMING_SNAKE_CASE	:  int		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE	:  int		     =							SqueezeBertTokenizer







			def __init__(	self					:							List[Any]				,  _UpperCamelCase					:							int=None				,  _UpperCamelCase					:							List[Any]=None				,  _UpperCamelCase					:							Any=True				,  _UpperCamelCase					:							Dict="[UNK]"				,  _UpperCamelCase					:							int="[SEP]"				,  _UpperCamelCase					:							int="[PAD]"				,  _UpperCamelCase					:							List[str]="[CLS]"				,  _UpperCamelCase					:							List[Any]="[MASK]"				,  _UpperCamelCase					:							Optional[int]=True				,  _UpperCamelCase					:							List[str]=None				,  **_UpperCamelCase					:							Optional[int]				,  ) ->List[str]:
									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				,  )

									snake_case_  =      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
									):
															snake_case_  =      getattr(_UpperCamelCase				,  normalizer_state.pop('''type'''				)				)
															snake_case_  =      do_lower_case
															snake_case_  =      strip_accents
															snake_case_  =      tokenize_chinese_chars
															snake_case_  =      normalizer_class(**_UpperCamelCase				)

									snake_case_  =      do_lower_case







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Union[str, Any]=None				) ->Optional[int]:
									snake_case_  =      [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 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							List[int]				,  _UpperCamelCase					:							Optional[List[int]] = None				) ->List[int]:
									snake_case_  =      [self.sep_token_id]
									snake_case_  =      [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 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Optional[str] = None				) ->Tuple[str]:
									snake_case_  =      self._tokenizer.model.save(_UpperCamelCase				,  name=_UpperCamelCase				)
									return tuple(_UpperCamelCase				)

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






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




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


