
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				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ["""image_processor""", """tokenizer"""]
			__UpperCamelCase      :						Any					      = """Pix2StructImageProcessor"""
			__UpperCamelCase      :						Union[str, Any]					      = ("""T5Tokenizer""", """T5TokenizerFast""")

			def __init__(       self :       Tuple						,	snake_case_ :       List[Any]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						False
							super().__init__(snake_case_						,	snake_case_      )

			def __call__(       self :       Optional[Any]						,	snake_case_ :       List[Any]=None						,	snake_case_ :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,	snake_case_ :       bool = True						,	snake_case_ :       Union[bool, str, PaddingStrategy] = False						,	snake_case_ :       Union[bool, str, TruncationStrategy] = None						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       Optional[int] = 2048						,	snake_case_ :       int = 0						,	snake_case_ :       Optional[int] = None						,	snake_case_ :       Optional[bool] = None						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = False						,	snake_case_ :       bool = True						,	snake_case_ :       Optional[Union[str, TensorType]] = None						,	**snake_case_ :       Any						,	):
							if images is None and text is None:
											raise ValueError("""You have to specify either images or text."""      )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
											UpperCamelCase_:							str	=						self.tokenizer
											UpperCamelCase_:							Tuple	=						self.tokenizer(
											    text=snake_case_						,	add_special_tokens=snake_case_						,	padding=snake_case_						,	truncation=snake_case_						,	max_length=snake_case_						,	stride=snake_case_						,	pad_to_multiple_of=snake_case_						,	return_attention_mask=snake_case_						,	return_overflowing_tokens=snake_case_						,	return_special_tokens_mask=snake_case_						,	return_offsets_mapping=snake_case_						,	return_token_type_ids=snake_case_						,	return_length=snake_case_						,	verbose=snake_case_						,	return_tensors=snake_case_						,	**snake_case_						,	)
											return text_encoding

							if not self.image_processor.is_vqa:
											# add pixel_values
											UpperCamelCase_:							Tuple	=						self.image_processor(
											    snake_case_						,	return_tensors=snake_case_						,	max_patches=snake_case_						,	**snake_case_      )
							else:
											# add pixel_values and bbox
											UpperCamelCase_:							Tuple	=						self.image_processor(
											    snake_case_						,	return_tensors=snake_case_						,	max_patches=snake_case_						,	header_text=snake_case_						,	**snake_case_      )

							if text is not None and not self.image_processor.is_vqa:
											UpperCamelCase_:							str	=						self.tokenizer(
											    text=snake_case_						,	add_special_tokens=snake_case_						,	padding=snake_case_						,	truncation=snake_case_						,	max_length=snake_case_						,	stride=snake_case_						,	pad_to_multiple_of=snake_case_						,	return_attention_mask=snake_case_						,	return_overflowing_tokens=snake_case_						,	return_special_tokens_mask=snake_case_						,	return_offsets_mapping=snake_case_						,	return_token_type_ids=snake_case_						,	return_length=snake_case_						,	verbose=snake_case_						,	return_tensors=snake_case_						,	**snake_case_						,	)

											if "attention_mask" in text_encoding:
															UpperCamelCase_:							Any	=						text_encoding.pop("""attention_mask"""      )
											if "input_ids" in text_encoding:
															UpperCamelCase_:							str	=						text_encoding.pop("""input_ids"""      )
							else:
											UpperCamelCase_:							int	=						None

							if text_encoding is not None:
											encoding_image_processor.update(snake_case_      )

							return encoding_image_processor

			def       lowerCAmelCase__	(       self :       List[Any]						,	*snake_case_ :       Tuple						,	**snake_case_ :       List[str]      ):
							return self.tokenizer.batch_decode(*snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	*snake_case_ :       Optional[int]						,	**snake_case_ :       List[Any]      ):
							return self.tokenizer.decode(*snake_case_						,	**snake_case_      )




			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Union[str, Any]	=						self.tokenizer.model_input_names
							UpperCamelCase_:							List[str]	=						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  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

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

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

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

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

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

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	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       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

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






