
"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)


if is_vision_available():
		import PIL







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   ['pixel_values']





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

												__snake_case     			=       do_resize
												__snake_case     			=       size
												__snake_case     			=       resample
												__snake_case     			=       do_center_crop
												__snake_case     			=       crop_size
												__snake_case     			=       do_rescale
												__snake_case     			=       rescale_factor
												__snake_case     			=       do_normalize
												__snake_case     			=       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
												__snake_case     			=       image_std if image_std is not None else OPENAI_CLIP_STD
												__snake_case     			=       do_convert_rgb





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





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							np.ndarray							,						snake_case_   :							Dict[str, int]							,						snake_case_   :							Optional[Union[str, ChannelDimension]] = None							,						**snake_case_   :							List[str]							,						):
												__snake_case     			=       get_size_dict(snake_case_    )
												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(snake_case_							,						size=(size["height"], size["width"])							,						data_format=snake_case_							,						**snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]							,						snake_case_   :							np.ndarray							,						snake_case_   :							Union[int, float]							,						snake_case_   :							Optional[Union[str, ChannelDimension]] = None							,						**snake_case_   :							Optional[Any]							,						):
												return rescale(snake_case_							,						scale=snake_case_							,						data_format=snake_case_							,						**snake_case_    )





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





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

												__snake_case     			=       make_list_of_images(snake_case_    )

												if not valid_images(snake_case_    ):
																			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     			=       [convert_to_rgb(snake_case_    ) for image in images]

												# All transformations expect numpy arrays.
												__snake_case     			=       [to_numpy_array(snake_case_    ) for image in images]

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

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

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

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

												__snake_case     			=       [to_channel_dimension_format(snake_case_							,						snake_case_    ) for image in images]

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


"""simple docstring"""







import math

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   'data2vec-audio'





					def __init__(				self   :							Any							,						snake_case_   :							Optional[Any]=32							,						snake_case_   :							Tuple=768							,						snake_case_   :							Optional[int]=12							,						snake_case_   :							str=12							,						snake_case_   :							int=3072							,						snake_case_   :							Tuple="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							str=0.1							,						snake_case_   :							int=0.1							,						snake_case_   :							int=0.0							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Dict=0.1							,						snake_case_   :							Dict=0.02							,						snake_case_   :							Optional[int]=1e-5							,						snake_case_   :							Optional[Any]="gelu"							,						snake_case_   :							Tuple=(512, 512, 512, 512, 512, 512, 512)							,						snake_case_   :							int=(5, 2, 2, 2, 2, 2, 2)							,						snake_case_   :							Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							int=16							,						snake_case_   :							List[Any]=19							,						snake_case_   :							str=5							,						snake_case_   :							List[Any]=0.05							,						snake_case_   :							Dict=10							,						snake_case_   :							Dict=2							,						snake_case_   :							Dict=0.0							,						snake_case_   :							Union[str, Any]=10							,						snake_case_   :							Optional[Any]=0							,						snake_case_   :							Optional[Any]="sum"							,						snake_case_   :							List[str]=False							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Optional[int]=256							,						snake_case_   :							int=(512, 512, 512, 512, 1500)							,						snake_case_   :							List[Any]=(5, 3, 3, 1, 1)							,						snake_case_   :							Any=(1, 2, 3, 1, 1)							,						snake_case_   :							List[Any]=512							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							Dict=1							,						snake_case_   :							List[str]=2							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							Optional[Any]=3							,						snake_case_   :							Tuple=2							,						snake_case_   :							Any=3							,						snake_case_   :							Union[str, Any]=None							,						**snake_case_   :							int							,						):
												super().__init__(**snake_case_							,						pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						eos_token_id=snake_case_    )
												__snake_case     			=       hidden_size
												__snake_case     			=       feat_extract_activation
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       conv_bias
												__snake_case     			=       num_conv_pos_embeddings
												__snake_case     			=       num_conv_pos_embedding_groups
												__snake_case     			=       conv_pos_kernel_size
												__snake_case     			=       len(self.conv_dim    )
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       feat_proj_dropout
												__snake_case     			=       final_dropout
												__snake_case     			=       layerdrop
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												__snake_case     			=       vocab_size
												__snake_case     			=       use_weighted_layer_sum

												if (
												    (len(self.conv_stride    ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																			    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																			    F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
																			    F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__snake_case     			=       mask_time_prob
												__snake_case     			=       mask_time_length
												__snake_case     			=       mask_time_min_masks
												__snake_case     			=       mask_feature_prob
												__snake_case     			=       mask_feature_length
												__snake_case     			=       mask_feature_min_masks

												# ctc loss
												__snake_case     			=       ctc_loss_reduction
												__snake_case     			=       ctc_zero_infinity

												# adapter
												__snake_case     			=       add_adapter
												__snake_case     			=       adapter_kernel_size
												__snake_case     			=       adapter_stride
												__snake_case     			=       num_adapter_layers
												__snake_case     			=       output_hidden_size or hidden_size

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												__snake_case     			=       classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       xvector_output_dim





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return math.prod(self.conv_stride    )
