

from ...processing_utils import ProcessorMixin






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='SpeechT5FeatureExtractor'
			lowerCamelCase__							     ='SpeechT5Tokenizer'
			def __init__(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			List[str]						)				->							Optional[Any]:

				"""simple docstring"""

				super().__init__(a   ,   a						)
			def __call__(      self				:			Optional[int]   ,   *a				:			Optional[Any]   ,   **a				:			List[Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							kwargs.pop("audio"   ,   a						)
				SCREAMING_SNAKE_CASE :   Any					  =							kwargs.pop("text"   ,   a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							kwargs.pop("text_target"   ,   a						)
				SCREAMING_SNAKE_CASE :   str					  =							kwargs.pop("audio_target"   ,   a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							kwargs.pop("sampling_rate"   ,   a						)

				if audio is not None and text is not None:
					raise ValueError(
					    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"						)
				if audio_target is not None and text_target is not None:
					raise ValueError(
					    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"						)
				if audio is None and audio_target is None and text is None and text_target is None:
					raise ValueError(
					    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."						)

				if audio is not None:
					SCREAMING_SNAKE_CASE :   Tuple					  =							self.feature_extractor(a   ,   *a   ,   sampling_rate=a   ,   **a						)
				elif text is not None:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.tokenizer(a   ,   **a						)
				else:
					SCREAMING_SNAKE_CASE :   Any					  =							None

				if audio_target is not None:
					SCREAMING_SNAKE_CASE :   Dict					  =							self.feature_extractor(audio_target=a   ,   *a   ,   sampling_rate=a   ,   **a						)
					SCREAMING_SNAKE_CASE :   str					  =							targets["input_values"]
				elif text_target is not None:
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self.tokenizer(a   ,   **a						)
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							targets["input_ids"]
				else:
					SCREAMING_SNAKE_CASE :   List[str]					  =							None

				if inputs is None:
					return targets

				if targets is not None:
					SCREAMING_SNAKE_CASE :   str					  =							labels

					SCREAMING_SNAKE_CASE :   List[Any]					  =							targets.get("attention_mask"						)
					if decoder_attention_mask is not None:
						SCREAMING_SNAKE_CASE :   Optional[int]					  =							decoder_attention_mask

				return inputs
			def 				__UpperCamelCase			(      self				:			Tuple   ,   *a				:			int   ,   **a				:			int						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							kwargs.pop("input_values"   ,   a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							kwargs.pop("input_ids"   ,   a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							kwargs.pop("labels"   ,   a						)

				if input_values is not None and input_ids is not None:
					raise ValueError("Cannot process both `input_values` and `input_ids` inputs."						)
				if input_values is None and input_ids is None and labels is None:
					raise ValueError(
					    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."						)

				if input_values is not None:
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.feature_extractor.pad(a   ,   *a   ,   **a						)
				elif input_ids is not None:
					SCREAMING_SNAKE_CASE :   str					  =							self.tokenizer.pad(a   ,   **a						)
				else:
					SCREAMING_SNAKE_CASE :   Tuple					  =							None

				if labels is not None:
					if "input_ids" in labels or (isinstance(a   ,   a						) and "input_ids" in labels[0]):
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.tokenizer.pad(a   ,   **a						)
						SCREAMING_SNAKE_CASE :   List[str]					  =							targets["input_ids"]
					else:
						SCREAMING_SNAKE_CASE :   Dict					  =							self.feature_extractor.feature_size
						SCREAMING_SNAKE_CASE :   List[Any]					  =							self.feature_extractor.num_mel_bins
						SCREAMING_SNAKE_CASE :   str					  =							self.feature_extractor.pad(a   ,   *a   ,   **a						)
						SCREAMING_SNAKE_CASE :   Dict					  =							feature_size_hack
						SCREAMING_SNAKE_CASE :   Tuple					  =							targets["input_values"]
				else:
					SCREAMING_SNAKE_CASE :   List[str]					  =							None

				if inputs is None:
					return targets

				if targets is not None:
					SCREAMING_SNAKE_CASE :   int					  =							labels

					SCREAMING_SNAKE_CASE :   Dict					  =							targets.get("attention_mask"						)
					if decoder_attention_mask is not None:
						SCREAMING_SNAKE_CASE :   Dict					  =							decoder_attention_mask

				return inputs
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   *a				:			int   ,   **a				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

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






			def 				__UpperCamelCase			(      self				:			Optional[int]   ,   *a				:			Any   ,   **a				:			Dict						)				->							List[str]:

				"""simple docstring"""

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


from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 			lowerCamelCase__			(       _a):
	return getitem, k

def 			lowerCamelCase__			(       _a				,      _a):
	return setitem, k, v

def 			lowerCamelCase__			(       _a):
	return delitem, k

def 			lowerCamelCase__			(       _a				,      _a				,      *_a):
	try:
		return fun(_a				,      *_a), None
	except Exception as e:
		return None, e


a_      =     (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

a_      =     [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]

@pytest.mark.parametrize(
    "operations"				,      (
        pytest.param(_add_items				,      id="add items"),
        pytest.param(_overwrite_items				,      id="overwrite items"),
        pytest.param(_delete_items				,      id="delete items"),
        pytest.param(_access_absent_items				,      id="access absent items"),
        pytest.param(_add_with_resize_up				,      id="add with resize up"),
        pytest.param(_add_with_resize_down				,      id="add with resize down"),
    )				,      )
def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							HashMap(initial_block_size=4)
	SCREAMING_SNAKE_CASE :   List[str]					  =							{}
	for _, (fun, *args) in enumerate(_a):
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							_run_operation(_a				,      _a				,      *_a)
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							_run_operation(_a				,      _a				,      *_a)
		assert my_res == py_res
		assert str(_a) == str(_a)
		assert set(_a) == set(_a)
		assert len(_a) == len(_a)
		assert set(my.items()) == set(py.items())


def 			lowerCamelCase__			(       ):
	def is_public(_a) -> bool:
		return not name.startswith("_")

	SCREAMING_SNAKE_CASE :   List[str]					  =							{name for name in dir({}) if is_public(_a)}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{name for name in dir(HashMap()) if is_public(_a)}

	assert dict_public_names > hash_public_names