




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


_UpperCAmelCase	:						Dict			=     logging.get_logger(__name__)




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						['''input_values''', '''padding_mask''']






       def __init__(   self:					List[Any]       ,					_lowerCAmelCase:					int = 1       ,					_lowerCAmelCase:					int = 2_40_00       ,					_lowerCAmelCase:					float = 0.0       ,					_lowerCAmelCase:					float = None       ,					_lowerCAmelCase:					float = None       ,					**_lowerCAmelCase:					Union[str, Any]       ,					):
         super().__init__(feature_size=_lowerCAmelCase       ,					sampling_rate=_lowerCAmelCase       ,					padding_value=_lowerCAmelCase       ,					**_lowerCAmelCase				)
         lowercase    :int      	=						chunk_length_s
         lowercase    :Tuple      	=						overlap






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         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]				):
         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:					Union[str, Any]       ,					_lowerCAmelCase:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,					_lowerCAmelCase:					Optional[Union[bool, str, PaddingStrategy]] = None       ,					_lowerCAmelCase:					Optional[bool] = False       ,					_lowerCAmelCase:					Optional[int] = None       ,					_lowerCAmelCase:					Optional[Union[str, TensorType]] = None       ,					_lowerCAmelCase:					Optional[int] = None       ,					):
         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
           lowercase    :List[Any]      	=						True

         lowercase    :int      	=						bool(
             isinstance(_lowerCAmelCase       ,					(list, tuple)				) and (isinstance(raw_audio[0]       ,					(np.ndarray, tuple, list)				))				)

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

         # always return batch
         if not is_batched:
           lowercase    :Tuple      	=						[np.asarray(_lowerCAmelCase				).T]

         # verify inputs are valid
         for idx, example in enumerate(_lowerCAmelCase				):
           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"				)

         lowercase    :Dict      	=						None
         lowercase    :Tuple      	=						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:
             lowercase    :Dict      	=						min(array.shape[0] for array in raw_audio				)
             lowercase    :int      	=						int(np.floor(max_length / self.chunk_stride				)				)
             lowercase    :int      	=						(nb_step - 1) * self.chunk_stride + self.chunk_length
           elif padding:
             lowercase    :str      	=						max(array.shape[0] for array in raw_audio				)
             lowercase    :List[Any]      	=						int(np.ceil(max_length / self.chunk_stride				)				)
             lowercase    :List[str]      	=						(nb_step - 1) * self.chunk_stride + self.chunk_length
             lowercase    :Optional[int]      	=						"max_length"
           else:
             lowercase    :List[Any]      	=						input_values

        # normal padding on batch
         if padded_inputs is None:
           lowercase    :Any      	=						self.pad(
               _lowerCAmelCase       ,					max_length=_lowerCAmelCase       ,					truncation=_lowerCAmelCase       ,					padding=_lowerCAmelCase       ,					return_attention_mask=_lowerCAmelCase       ,					)
           if padding:
             lowercase    :Dict      	=						padded_inputs.pop("attention_mask"				)

         lowercase    :Tuple      	=						[]
         for example in padded_inputs.pop("input_values"				):
           if self.feature_size == 1:
             lowercase    :int      	=						example[..., None]
           input_values.append(example.T				)

         lowercase    :List[Any]      	=						input_values
         if return_tensors is not None:
           lowercase    :str      	=						padded_inputs.convert_to_tensors(_lowerCAmelCase				)

         return padded_inputs









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Optional[Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



