


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_snake_case					:		Tuple      =    logging.get_logger(__name__)


if is_vision_available():
			import PIL

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  ["pixel_values"]

							def __init__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  PILImageResampling = PILImageResampling.BICUBIC   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[int, float] = 1 / 255   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  bool = True   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   None:
														super().__init__(**lowerCamelCase    )
														__snake_case		:      Optional[int]										=       size if size is not None else {"shortest_edge": 224}
														__snake_case		:      Dict										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )
														__snake_case		:      Tuple										=       crop_size if crop_size is not None else {"height": 224, "width": 224}
														__snake_case		:      int										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase   ,     param_name="crop_size"    )

														__snake_case		:      Tuple										=       do_resize
														__snake_case		:      Union[str, Any]										=       size
														__snake_case		:      str										=       resample
														__snake_case		:      List[Any]										=       do_center_crop
														__snake_case		:      str										=       crop_size
														__snake_case		:      Optional[int]										=       do_rescale
														__snake_case		:      List[Any]										=       rescale_factor
														__snake_case		:      List[str]										=       do_normalize
														__snake_case		:      Optional[Any]										=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														__snake_case		:      Tuple										=       image_std if image_std is not None else OPENAI_CLIP_STD
														__snake_case		:      List[Any]										=       do_convert_rgb

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Dict[str, int]   ,     lowerCamelCase	:  PILImageResampling = PILImageResampling.BICUBIC   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Dict   ,     )     ->   np.ndarray:
														__snake_case		:      Dict										=       get_size_dict(lowerCamelCase   ,     default_to_square=lowerCamelCase    )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'    )
														__snake_case		:      Optional[int]										=       get_resize_output_image_size(lowerCamelCase   ,     size=size["shortest_edge"]   ,     default_to_square=lowerCamelCase    )
														return resize(lowerCamelCase   ,     size=lowerCamelCase   ,     resample=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Dict[str, int]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Optional[int]   ,     )     ->   np.ndarray:
														__snake_case		:      str										=       get_size_dict(lowerCamelCase    )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'    )
														return center_crop(lowerCamelCase   ,     size=(size["height"], size["width"])   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Union[int, float]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  Tuple   ,     )     ->   Optional[Any]:
														return rescale(lowerCamelCase   ,     scale=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  np.ndarray   ,     lowerCamelCase	:  Union[float, List[float]]   ,     lowerCamelCase	:  Union[float, List[float]]   ,     lowerCamelCase	:  Optional[Union[str, ChannelDimension]] = None   ,     **lowerCamelCase	:  List[str]   ,     )     ->   np.ndarray:
														return normalize(lowerCamelCase   ,     mean=lowerCamelCase   ,     std=lowerCamelCase   ,     data_format=lowerCamelCase   ,     **lowerCamelCase    )






							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  ImageInput   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Dict[str, int] = None   ,     lowerCamelCase	:  PILImageResampling = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  int = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  float = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  Optional[Union[float, List[float]]] = None   ,     lowerCamelCase	:  bool = None   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     lowerCamelCase	:  Optional[ChannelDimension] = ChannelDimension.FIRST   ,     **lowerCamelCase	:  List[str]   ,     )     ->   PIL.Image.Image:
														__snake_case		:      Any										=       do_resize if do_resize is not None else self.do_resize
														__snake_case		:      Optional[int]										=       size if size is not None else self.size
														__snake_case		:      List[Any]										=       get_size_dict(lowerCamelCase   ,     param_name="size"   ,     default_to_square=lowerCamelCase    )
														__snake_case		:      str										=       resample if resample is not None else self.resample
														__snake_case		:      List[Any]										=       do_center_crop if do_center_crop is not None else self.do_center_crop
														__snake_case		:      Tuple										=       crop_size if crop_size is not None else self.crop_size
														__snake_case		:      List[str]										=       get_size_dict(lowerCamelCase   ,     param_name="crop_size"   ,     default_to_square=lowerCamelCase    )
														__snake_case		:      List[Any]										=       do_rescale if do_rescale is not None else self.do_rescale
														__snake_case		:      Union[str, Any]										=       rescale_factor if rescale_factor is not None else self.rescale_factor
														__snake_case		:      List[str]										=       do_normalize if do_normalize is not None else self.do_normalize
														__snake_case		:      Tuple										=       image_mean if image_mean is not None else self.image_mean
														__snake_case		:      Dict										=       image_std if image_std is not None else self.image_std
														__snake_case		:      Tuple										=       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														__snake_case		:      Any										=       make_list_of_images(lowerCamelCase    )

														if not valid_images(lowerCamelCase    ):
																					raise ValueError(
																					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																					    "torch.Tensor, tf.Tensor or jax.ndarray."    )

														if do_resize and size is None:
																					raise ValueError("Size must be specified if do_resize is True."    )

														if do_center_crop and crop_size is None:
																					raise ValueError("Crop size must be specified if do_center_crop is True."    )

														if do_rescale and rescale_factor is None:
																					raise ValueError("Rescale factor must be specified if do_rescale is True."    )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("Image mean and std must be specified if do_normalize is True."    )

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					__snake_case		:      Any										=       [convert_to_rgb(lowerCamelCase    ) for image in images]

														# All transformations expect numpy arrays.
														__snake_case		:      Optional[int]										=       [to_numpy_array(lowerCamelCase    ) for image in images]

														if do_resize:
																					__snake_case		:      str										=       [self.resize(image=lowerCamelCase   ,     size=lowerCamelCase   ,     resample=lowerCamelCase    ) for image in images]

														if do_center_crop:
																					__snake_case		:      List[Any]										=       [self.center_crop(image=lowerCamelCase   ,     size=lowerCamelCase    ) for image in images]

														if do_rescale:
																					__snake_case		:      str										=       [self.rescale(image=lowerCamelCase   ,     scale=lowerCamelCase    ) for image in images]

														if do_normalize:
																					__snake_case		:      List[Any]										=       [self.normalize(image=lowerCamelCase   ,     mean=lowerCamelCase   ,     std=lowerCamelCase    ) for image in images]

														__snake_case		:      int										=       [to_channel_dimension_format(lowerCamelCase   ,     lowerCamelCase    ) for image in images]

														__snake_case		:      Tuple										=       {"pixel_values": images}
														return BatchFeature(data=lowerCamelCase   ,     tensor_type=lowerCamelCase    )





import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

							def __init__(							self	:  Any   ,     lowerCamelCase	:  Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,     lowerCamelCase	:  List[str]=24000   ,     lowerCamelCase	:  int=1   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Optional[int]=128   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  str=[8, 5, 4, 2]   ,     lowerCamelCase	:  List[str]="weight_norm"   ,     lowerCamelCase	:  Any=7   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="reflect"   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  int=1.0   ,     lowerCamelCase	:  Optional[Any]=1024   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=True   ,     **lowerCamelCase	:  Dict   ,     )     ->   Any:
														__snake_case		:      Tuple										=       target_bandwidths
														__snake_case		:      Union[str, Any]										=       sampling_rate
														__snake_case		:      Union[str, Any]										=       audio_channels
														__snake_case		:      Dict										=       normalize
														__snake_case		:      List[Any]										=       chunk_length_s
														__snake_case		:      Tuple										=       overlap
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      List[Any]										=       num_filters
														__snake_case		:      Union[str, Any]										=       num_residual_layers
														__snake_case		:      Optional[int]										=       upsampling_ratios
														__snake_case		:      List[str]										=       norm_type
														__snake_case		:      Optional[int]										=       kernel_size
														__snake_case		:      Dict										=       last_kernel_size
														__snake_case		:      Tuple										=       residual_kernel_size
														__snake_case		:      List[Any]										=       dilation_growth_rate
														__snake_case		:      Optional[int]										=       use_causal_conv
														__snake_case		:      Tuple										=       pad_mode
														__snake_case		:      Union[str, Any]										=       compress
														__snake_case		:      Union[str, Any]										=       num_lstm_layers
														__snake_case		:      int										=       trim_right_ratio
														__snake_case		:      Tuple										=       codebook_size
														__snake_case		:      Optional[Any]										=       codebook_dim if codebook_dim is not None else hidden_size
														__snake_case		:      int										=       use_conv_shortcut

														if self.norm_type not in ["weight_norm", "time_group_norm"]:
																					raise ValueError(
																					    F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'    )

														super().__init__(**lowerCamelCase    )

							@property
							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   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    )    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )

