




from ...processing_utils import ProcessorMixin
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					Optional[int]     		=["""image_processor""", """feature_extractor"""]
				UpperCamelCase__				:					Union[str, Any]     		="""TvltImageProcessor"""
				UpperCamelCase__				:					List[Any]     		="""TvltFeatureExtractor"""




				def __init__(						self					:						List[str],					__lowercase					:						List[Any],					__lowercase					:						List[str]						):
							super().__init__(image_processor=__A,					feature_extractor=__A						)

							lowercase__					  =				image_processor
							lowercase__					  =				feature_extractor




				def __call__(						self					:						List[str],					__lowercase					:						Any=None,					__lowercase					:						Any=None,					__lowercase					:						Optional[int]=None,					__lowercase					:						str=None,					__lowercase					:						List[Any]=False,					__lowercase					:						Tuple=False,					*__lowercase					:						List[str],					**__lowercase					:						Optional[Any],					):

							if images is None and audio is None:
										raise ValueError("You need to specify either an `images` or `audio` input to process."						)

							lowercase__					  =				None
							if images is not None:
										lowercase__					  =				self.image_processor(__A,					mask_pixel=__A,					*__A,					**__A						)
							if images_mixed is not None:
										lowercase__					  =				self.image_processor(__A,					is_mixed=__A,					*__A,					**__A						)
							if audio is not None:
										lowercase__					  =				self.feature_extractor(
										    __A,					*__A,					sampling_rate=__A,					mask_audio=__A,					**__A						)

							lowercase__					  =				{}
							if audio is not None:
										output_dict.update(__A						)
							if images is not None:
										output_dict.update(__A						)
							if images_mixed_dict is not None:
										output_dict.update(__A						)
							return output_dict




				@property
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processor.model_input_names
							lowercase__					  =				self.feature_extractor.model_input_names
							return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)









import re
from pathlib import Path
from unittest import TestCase

import pytest
@pytest.mark.integration
class 				_snake_case			(					lowercase__):




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						str						):

							with open(__lowercase,					encoding="utf-8"						) as input_file:
										lowercase__					  =				re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"						)
										lowercase__					  =				input_file.read()
										lowercase__					  =				regexp.search(__lowercase						)

							return match




				def 							A__      (						self					:						str,					__lowercase					:						str						):

							with open(__lowercase,					encoding="utf-8"						) as input_file:
										lowercase__					  =				re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()",					re.DOTALL						)
										lowercase__					  =				input_file.read()
										# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
										lowercase__					  =				regexp.finditer(__lowercase						)

							lowercase__					  =				[match for match in matches if match is not None and match.group(1						) is not None]
							return matches[0] if matches else None




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				Path("./datasets"						)
							lowercase__					  =				list(dataset_paths.absolute().glob("**/*.py"						)						)

							for dataset in dataset_files:
										if self._no_encoding_on_file_open(str(__lowercase						)						):
													raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''						)




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				Path("./datasets"						)
							lowercase__					  =				list(dataset_paths.absolute().glob("**/*.py"						)						)

							for dataset in dataset_files:
										if self._no_print_statements(str(__lowercase						)						):
													raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''						)




