
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''input_values''', '''padding_mask''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = 1    ,    __SCREAMING_SNAKE_CASE = 2_4_0_0_0    ,    __SCREAMING_SNAKE_CASE = 0.0    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Any:




           """simple docstring"""







           super().__init__(feature_size=__SCREAMING_SNAKE_CASE    ,    sampling_rate=__SCREAMING_SNAKE_CASE    ,    padding_value=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Optional[Any]											=					chunk_length_s
           UpperCamelCase__     : Any											=					overlap






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           if self.chunk_length_s is None:
                 return None
           else:
                 return int(self.chunk_length_s * self.sampling_rate	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           if self.chunk_length_s is None or self.overlap is None:
                 return None
           else:
                 return max(1    ,    int((1.0 - self.overlap) * self.chunk_length	)	)






     def __call__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    )	-> BatchFeature:




           """simple docstring"""







           if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                           F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                           F''' {self.sampling_rate} and not {sampling_rate}.'''	)
           else:
                 logger.warning(
                     '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                     '''Failing to do so can result in silent errors that might be hard to debug.'''	)

           if padding and truncation:
                 raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''	)
           elif padding is None:
                 # by default let's pad the inputs
                 UpperCamelCase__     : Any											=					True

           UpperCamelCase__     : List[str]											=					bool(
               isinstance(__SCREAMING_SNAKE_CASE    ,    (list, tuple)	) and (isinstance(raw_audio[0]    ,    (np.ndarray, tuple, list)	))	)

           if is_batched:
                 UpperCamelCase__     : Union[str, Any]											=					[np.asarray(__SCREAMING_SNAKE_CASE    ,    dtype=np.floataa	).T for audio in raw_audio]
           elif not is_batched and not isinstance(__SCREAMING_SNAKE_CASE    ,    np.ndarray	):
                 UpperCamelCase__     : Any											=					np.asarray(__SCREAMING_SNAKE_CASE    ,    dtype=np.floataa	)
           elif isinstance(__SCREAMING_SNAKE_CASE    ,    np.ndarray	) and raw_audio.dtype is np.dtype(np.floataa	):
                 UpperCamelCase__     : Union[str, Any]											=					raw_audio.astype(np.floataa	)

           # always return batch
           if not is_batched:
                 UpperCamelCase__     : int											=					[np.asarray(__SCREAMING_SNAKE_CASE	).T]

           # verify inputs are valid
           for idx, example in enumerate(__SCREAMING_SNAKE_CASE	):
                 if example.ndim > 2:
                       raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''	)
                 if self.feature_size == 1 and example.ndim != 1:
                       raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''	)
                 if self.feature_size == 2 and example.shape[-1] != 2:
                       raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''	)

           UpperCamelCase__     : str											=					None
           UpperCamelCase__     : Optional[int]											=					BatchFeature({'''input_values''': raw_audio}	)
           if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                 if truncation:
                       UpperCamelCase__     : List[str]											=					min(array.shape[0] for array in raw_audio	)
                       UpperCamelCase__     : str											=					int(np.floor(max_length / self.chunk_stride	)	)
                       UpperCamelCase__     : int											=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 elif padding:
                       UpperCamelCase__     : Any											=					max(array.shape[0] for array in raw_audio	)
                       UpperCamelCase__     : List[Any]											=					int(np.ceil(max_length / self.chunk_stride	)	)
                       UpperCamelCase__     : Optional[int]											=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                       UpperCamelCase__     : Union[str, Any]											=					'''max_length'''
                 else:
                       UpperCamelCase__     : List[Any]											=					input_values

        # normal padding on batch
           if padded_inputs is None:
                 UpperCamelCase__     : Optional[int]											=					self.pad(
                     __SCREAMING_SNAKE_CASE    ,    max_length=__SCREAMING_SNAKE_CASE    ,    truncation=__SCREAMING_SNAKE_CASE    ,    padding=__SCREAMING_SNAKE_CASE    ,    return_attention_mask=__SCREAMING_SNAKE_CASE    ,    )
                 if padding:
                       UpperCamelCase__     : str											=					padded_inputs.pop('''attention_mask'''	)

           UpperCamelCase__     : Optional[Any]											=					[]
           for example in padded_inputs.pop('''input_values'''	):
                 if self.feature_size == 1:
                       UpperCamelCase__     : Optional[Any]											=					example[..., None]
                 input_values.append(example.T	)

           UpperCamelCase__     : List[Any]											=					input_values
           if return_tensors is not None:
                 UpperCamelCase__     : Tuple											=					padded_inputs.convert_to_tensors(__SCREAMING_SNAKE_CASE	)

           return padded_inputs




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase  ={
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase  =[
        "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LukeForEntityClassification",
        "LukeForEntityPairClassification",
        "LukeForEntitySpanClassification",
        "LukeForMultipleChoice",
        "LukeForQuestionAnswering",
        "LukeForSequenceClassification",
        "LukeForTokenClassification",
        "LukeForMaskedLM",
        "LukeModel",
        "LukePreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    lowerCamelCase  =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


