


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


A							:					int     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	["""input_values""", """padding_mask"""]


					def __init__(						self	:		str     ,    SCREAMING_SNAKE_CASE	:		int = 1     ,    SCREAMING_SNAKE_CASE	:		int = 2_4_0_0_0     ,    SCREAMING_SNAKE_CASE	:		float = 0.0     ,    SCREAMING_SNAKE_CASE	:		float = None     ,    SCREAMING_SNAKE_CASE	:		float = None     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(feature_size=SCREAMING_SNAKE_CASE     ,    sampling_rate=SCREAMING_SNAKE_CASE     ,    padding_value=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		chunk_length_s
											__snake_case				=		overlap


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	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	:		List[str]						)			->	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	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[bool, str, PaddingStrategy]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[bool] = False     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[Union[str, TensorType]] = None     ,    SCREAMING_SNAKE_CASE	:		Optional[int] = 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
																	__snake_case				=		True

											__snake_case				=		bool(
											    isinstance(SCREAMING_SNAKE_CASE     ,    (list, tuple)						) and (isinstance(raw_audio[0]     ,    (np.ndarray, tuple, list)						))						)

											if is_batched:
																	__snake_case				=		[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						):
																	__snake_case				=		np.asarray(SCREAMING_SNAKE_CASE     ,    dtype=np.floataa						)
											elif isinstance(SCREAMING_SNAKE_CASE     ,    np.ndarray						) and raw_audio.dtype is np.dtype(np.floataa						):
																	__snake_case				=		raw_audio.astype(np.floataa						)

											# always return batch
											if not is_batched:
																	__snake_case				=		[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'''						)

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

        # normal padding on batch
											if padded_inputs is None:
																	__snake_case				=		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:
																							__snake_case				=		padded_inputs.pop("attention_mask"						)

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

											__snake_case				=		input_values
											if return_tensors is not None:
																	__snake_case				=		padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE						)

											return padded_inputs









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

A							:					Tuple     						=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""data2vec-vision"""


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_2     ,    SCREAMING_SNAKE_CASE	:		int=1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.02     ,    SCREAMING_SNAKE_CASE	:		List[str]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2_2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=True     ,    SCREAMING_SNAKE_CASE	:		List[str]=[3, 5, 7, 1_1]     ,    SCREAMING_SNAKE_CASE	:		int=[1, 2, 3, 6]     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_5_5     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	List[str]:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps

											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		use_mask_token
											__snake_case				=		use_absolute_position_embeddings
											__snake_case				=		use_relative_position_bias
											__snake_case				=		use_shared_relative_position_bias
											__snake_case				=		layer_scale_init_value
											__snake_case				=		drop_path_rate
											__snake_case				=		use_mean_pooling
											# decode head attributes (semantic segmentation)
											__snake_case				=		out_indices
											__snake_case				=		pool_scales
											# auxiliary head attributes (semantic segmentation)
											__snake_case				=		use_auxiliary_head
											__snake_case				=		auxiliary_loss_weight
											__snake_case				=		auxiliary_channels
											__snake_case				=		auxiliary_num_convs
											__snake_case				=		auxiliary_concat_input
											__snake_case				=		semantic_loss_ignore_index






class     UpperCamelCase(  _a   ):
					snake_case_		:				Any										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)






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




											'''simple docstring'''

											return 1e-4





