

from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Tuple       				=  ["image_processor", "tokenizer"]
	_lowercase  :   Any       				=  "BridgeTowerImageProcessor"
	_lowercase  :   int       				=  ("RobertaTokenizer", "RobertaTokenizerFast")







	def __init__(   self: int  , __UpperCamelCase: Dict  , __UpperCamelCase: int      ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase  , __UpperCamelCase      )







	def __call__(   self: Tuple  , __UpperCamelCase: str  , __UpperCamelCase: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  , __UpperCamelCase: bool = True  , __UpperCamelCase: Union[bool, str, PaddingStrategy] = False  , __UpperCamelCase: Union[bool, str, TruncationStrategy] = None  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: int = 0  , __UpperCamelCase: Optional[int] = None  , __UpperCamelCase: Optional[bool] = None  , __UpperCamelCase: Optional[bool] = None  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = True  , __UpperCamelCase: Optional[Union[str, TensorType]] = None  , **__UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer(
						    text=__UpperCamelCase  , add_special_tokens=__UpperCamelCase  , padding=__UpperCamelCase  , truncation=__UpperCamelCase  , max_length=__UpperCamelCase  , stride=__UpperCamelCase  , pad_to_multiple_of=__UpperCamelCase  , return_token_type_ids=__UpperCamelCase  , return_attention_mask=__UpperCamelCase  , return_overflowing_tokens=__UpperCamelCase  , return_special_tokens_mask=__UpperCamelCase  , return_offsets_mapping=__UpperCamelCase  , return_length=__UpperCamelCase  , verbose=__UpperCamelCase  , return_tensors=__UpperCamelCase  , **__UpperCamelCase  , )
						# add pixel_values + pixel_mask
						__magic_name__							=    self.image_processor(
						    __UpperCamelCase  , return_tensors=__UpperCamelCase  , do_normalize=__UpperCamelCase  , do_center_crop=__UpperCamelCase  , **__UpperCamelCase      )
						encoding.update(__UpperCamelCase      )

						return encoding







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , *__UpperCamelCase: Any  , **__UpperCamelCase: List[str]      ):

						'''simple docstring'''





						return self.tokenizer.batch_decode(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *__UpperCamelCase: Optional[int]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						return self.tokenizer.decode(*__UpperCamelCase  , **__UpperCamelCase      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.tokenizer.model_input_names
						__magic_name__							=    self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    'laion/clap-htsat-unfused'
						__magic_name__							=    tempfile.mkdtemp()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_tokenizer()
						__magic_name__							=    self.get_feature_extractor()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						processor.save_pretrained(self.tmpdirname      )
						__magic_name__							=    ClapProcessor.from_pretrained(self.tmpdirname      )

						self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer.get_vocab()      )
						self.assertIsInstance(processor.tokenizer  , __UpperCamelCase      )

						self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor.to_json_string()      )
						self.assertIsInstance(processor.feature_extractor  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    ClapProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()      )
						processor.save_pretrained(self.tmpdirname      )

						__magic_name__							=    self.get_tokenizer(bos_token='(BOS)'  , eos_token='(EOS)'      )
						__magic_name__							=    self.get_feature_extractor(do_normalize=__UpperCamelCase  , padding_value=1.0      )

						__magic_name__							=    ClapProcessor.from_pretrained(
						    self.tmpdirname  , bos_token='(BOS)'  , eos_token='(EOS)'  , do_normalize=__UpperCamelCase  , padding_value=1.0      )

						self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer_add_kwargs.get_vocab()      )
						self.assertIsInstance(processor.tokenizer  , __UpperCamelCase      )

						self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor_add_kwargs.to_json_string()      )
						self.assertIsInstance(processor.feature_extractor  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    floats_list((3, 10_00)      )

						__magic_name__							=    feature_extractor(__UpperCamelCase  , return_tensors='np'      )
						__magic_name__							=    processor(audios=__UpperCamelCase  , return_tensors='np'      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    'This is a test string'

						__magic_name__							=    processor(text=__UpperCamelCase      )

						__magic_name__							=    tokenizer(__UpperCamelCase      )

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]  , encoded_processor[key]      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__magic_name__							=    processor.batch_decode(__UpperCamelCase      )
						__magic_name__							=    tokenizer.batch_decode(__UpperCamelCase      )

						self.assertListEqual(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						self.assertListEqual(
						    processor.model_input_names[2:]  , feature_extractor.model_input_names  , msg='`processor` and `feature_extractor` model input names do not match'  , )




