
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


lowercase__          =logging.get_logger(__name__)



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							int     	=				["input_values", "padding_mask"]
   def __init__(self			: Dict ,    snake_case_			: int = 1 ,    snake_case_			: int = 2_4_0_0_0 ,    snake_case_			: float = 0.0 ,    snake_case_			: float = None ,    snake_case_			: float = None ,    **snake_case_			: Dict ,    ):
    super().__init__(feature_size=snake_case_ ,    sampling_rate=snake_case_ ,    padding_value=snake_case_ ,    **snake_case_       )
    __a			:  Union[str, Any]		    =  chunk_length_s
    __a			:  Dict		    =  overlap
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    if self.chunk_length_s is None:
     return None
    else:
     return int(self.chunk_length_s * self.sampling_rate       )
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    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			: List[str] ,    snake_case_			: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,    snake_case_			: Optional[Union[bool, str, PaddingStrategy]] = None ,    snake_case_			: Optional[bool] = False ,    snake_case_			: Optional[int] = None ,    snake_case_			: Optional[Union[str, TensorType]] = None ,    snake_case_			: 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
     __a			:  Tuple		    =  True

    __a			:  Optional[int]		    =  bool(
        isinstance(snake_case_ ,    (list, tuple)       ) and (isinstance(raw_audio[0] ,    (np.ndarray, tuple, list)       ))       )

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

    # always return batch
    if not is_batched:
     __a			:  Union[str, Any]		    =  [np.asarray(snake_case_       ).T]

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

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

        # normal padding on batch
    if padded_inputs is None:
     __a			:  str		    =  self.pad(
         snake_case_ ,    max_length=snake_case_ ,    truncation=snake_case_ ,    padding=snake_case_ ,    return_attention_mask=snake_case_ ,    )
     if padding:
      __a			:  int		    =  padded_inputs.pop('''attention_mask'''       )

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

    __a			:  Optional[Any]		    =  input_values
    if return_tensors is not None:
     __a			:  int		    =  padded_inputs.convert_to_tensors(snake_case_       )

    return padded_inputs


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
