


"""simple docstring"""


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCamelCase						:				Tuple             =      os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
UpperCamelCase						:				Optional[int]             =      "    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"



class 			lowerCamelCase__						(   unittest.TestCase			):







      def __a						(							self   :    Dict		):
        A					       = tempfile.mkdtemp()
        os.makedirs(os.path.join(self.diffusers_dir			,			'schedulers/'		)		)
        A					       = self.diffusers_dir
        shutil.copy(
            os.path.join(UpperCamelCase_			,			'src/diffusers/schedulers/scheduling_ddpm.py'		)			,			os.path.join(self.diffusers_dir			,			'schedulers/scheduling_ddpm.py'		)			,			)







      def __a						(							self   :    List[Any]		):
        A					       = "src/diffusers"
        shutil.rmtree(self.diffusers_dir		)







      def __a						(							self   :    List[Any]			,			_lowercase   :    Dict			,			_lowercase   :    Dict			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    Union[str, Any]=None		):
        A					       = comment + f'\nclass {class_name}(nn.Module):\n' + class_code
        if overwrite_result is not None:
          A					       = comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
        A					       = black.Mode(target_versions={black.TargetVersion.PYaa}			,			line_length=119		)
        A					       = black.format_str(UpperCamelCase_			,			mode=UpperCamelCase_		)
        A					       = os.path.join(self.diffusers_dir			,			'new_code.py'		)
        with open(UpperCamelCase_			,			'w'			,			newline='\n'		) as f:
          f.write(UpperCamelCase_		)
        if overwrite_result is None:
          self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase_		)		) == 0		)
        else:
          check_copies.is_copy_consistent(f.name			,			overwrite=UpperCamelCase_		)
          with open(UpperCamelCase_			,			'r'		) as f:
            self.assertTrue(f.read()			,			UpperCamelCase_		)







      def __a						(							self   :    str		):
        A					       = check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'		)
        self.assertEqual(UpperCamelCase_			,			UpperCamelCase_		)







      def __a						(							self   :    Tuple		):
        # Base copy consistency
        self.check_copy_consistency(
            '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'			,			'DDPMSchedulerOutput'			,			REFERENCE_CODE + '\n'			,			)

        # With no empty line at the end
        self.check_copy_consistency(
            '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'			,			'DDPMSchedulerOutput'			,			UpperCamelCase_			,			)

        # Copy consistency with rename
        self.check_copy_consistency(
            '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'			,			'TestSchedulerOutput'			,			re.sub('DDPM'			,			'Test'			,			UpperCamelCase_		)			,			)

        # Copy consistency with a really long name
        A					       = "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
        self.check_copy_consistency(
            f'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'			,			f'{long_class_name}SchedulerOutput'			,			re.sub('Bert'			,			UpperCamelCase_			,			UpperCamelCase_		)			,			)

        # Copy consistency with overwrite
        self.check_copy_consistency(
            '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'			,			'TestSchedulerOutput'			,			UpperCamelCase_			,			overwrite_result=re.sub('DDPM'			,			'Test'			,			UpperCamelCase_		)			,			)









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





