



"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class        __snake_case       (	_lowercase):
  snake_case__				:   List[Any]  			=						"Speech2TextFeatureExtractor"
  snake_case__				:   Union[str, Any]  			=						"Speech2TextTokenizer"




  def __init__( self     :						int						,					__lowerCAmelCase     :						Union[str, Any]						,					__lowerCAmelCase     :						Union[str, Any]		):






         """simple docstring"""






         super().__init__(__lowerCAmelCase						,					__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= self.feature_extractor
         _lowerCamelCase						:				str    			= False




  def __call__( self     :						List[Any]						,					*__lowerCAmelCase     :						int						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         if self._in_target_context_manager:
                return self.current_processor(*__lowerCAmelCase						,					**__lowerCAmelCase		)

         if "raw_speech" in kwargs:
                warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''		)
                _lowerCamelCase						:				str    			= kwargs.pop('''raw_speech'''		)
         else:
                _lowerCamelCase						:				Tuple    			= kwargs.pop('''audio'''						,					__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[Any]    			= kwargs.pop('''sampling_rate'''						,					__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= kwargs.pop('''text'''						,					__lowerCAmelCase		)
         if len(__lowerCAmelCase		) > 0:
                _lowerCamelCase						:				List[Any]    			= args[0]
                _lowerCamelCase						:				int    			= args[1:]

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

         if audio is not None:
                _lowerCamelCase						:				List[Any]    			= self.feature_extractor(__lowerCAmelCase						,					*__lowerCAmelCase						,					sampling_rate=__lowerCAmelCase						,					**__lowerCAmelCase		)
         if text is not None:
                _lowerCamelCase						:				List[Any]    			= self.tokenizer(__lowerCAmelCase						,					**__lowerCAmelCase		)

         if text is None:
                return inputs
         elif audio is None:
                return encodings
         else:
                _lowerCamelCase						:				List[str]    			= encodings['''input_ids''']
                return inputs




  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					*__lowerCAmelCase     :						List[Any]						,					**__lowerCAmelCase     :						Tuple		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					*__lowerCAmelCase     :						List[str]						,					**__lowerCAmelCase     :						int		):






         """simple docstring"""






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




  @contextmanager
  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         warnings.warn(
             '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
             '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
             '''your audio inputs, or in a separate call.'''		)
         _lowerCamelCase						:				Union[str, Any]    			= True
         _lowerCamelCase						:				Any    			= self.tokenizer
         yield
         _lowerCamelCase						:				List[str]    			= self.feature_extractor
         _lowerCamelCase						:				Tuple    			= False




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

import black


_A   =     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.
_A   =     "    \"\"\"\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     )				->      str:
							lowerCAmelCase_						=	tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir				,				"schedulers/"     )     )
							lowerCAmelCase_						=	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     )				->      Optional[int]:
							lowerCAmelCase_						=	"src/diffusers"
							shutil.rmtree(self.diffusers_dir     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										lowerCAmelCase_						=	comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							lowerCAmelCase_						=	black.Mode(target_versions={black.TargetVersion.PYaa}				,				line_length=119     )
							lowerCAmelCase_						=	black.format_str(_UpperCamelCase				,				mode=_UpperCamelCase     )
							lowerCAmelCase_						=	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     )				->      Optional[Any]:
							lowerCAmelCase_						=	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
							lowerCAmelCase_						=	"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     )				,				)



