


import math
from typing import Optional

import numpy as np

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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						'''encodec'''


	def __init__(    self				,     lowercase__=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]				,     lowercase__=2_4_0_0_0				,     lowercase__=1				,     lowercase__=False				,     lowercase__=None				,     lowercase__=None				,     lowercase__=1_2_8				,     lowercase__=3_2				,     lowercase__=1				,     lowercase__=[8, 5, 4, 2]				,     lowercase__="weight_norm"				,     lowercase__=7				,     lowercase__=7				,     lowercase__=3				,     lowercase__=2				,     lowercase__=True				,     lowercase__="reflect"				,     lowercase__=2				,     lowercase__=2				,     lowercase__=1.0				,     lowercase__=1_0_2_4				,     lowercase__=None				,     lowercase__=True				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Union[str, Any]	    =	target_bandwidths
							__UpperCAmelCase			:							Optional[Any]	    =	sampling_rate
							__UpperCAmelCase			:							Union[str, Any]	    =	audio_channels
							__UpperCAmelCase			:							Union[str, Any]	    =	normalize
							__UpperCAmelCase			:							Optional[Any]	    =	chunk_length_s
							__UpperCAmelCase			:							Dict	    =	overlap
							__UpperCAmelCase			:							List[Any]	    =	hidden_size
							__UpperCAmelCase			:							List[str]	    =	num_filters
							__UpperCAmelCase			:							List[str]	    =	num_residual_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	upsampling_ratios
							__UpperCAmelCase			:							Any	    =	norm_type
							__UpperCAmelCase			:							Optional[int]	    =	kernel_size
							__UpperCAmelCase			:							List[str]	    =	last_kernel_size
							__UpperCAmelCase			:							Any	    =	residual_kernel_size
							__UpperCAmelCase			:							str	    =	dilation_growth_rate
							__UpperCAmelCase			:							Dict	    =	use_causal_conv
							__UpperCAmelCase			:							Union[str, Any]	    =	pad_mode
							__UpperCAmelCase			:							Tuple	    =	compress
							__UpperCAmelCase			:							List[str]	    =	num_lstm_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	trim_right_ratio
							__UpperCAmelCase			:							Tuple	    =	codebook_size
							__UpperCAmelCase			:							Dict	    =	codebook_dim if codebook_dim is not None else hidden_size
							__UpperCAmelCase			:							str	    =	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__(**lowercase__)


	@property
	def  A(    self):
							if self.chunk_length_s is None:
													return None
							else:
													return int(self.chunk_length_s * self.sampling_rate)


	@property
	def  A(    self):
							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  A(    self):
							__UpperCAmelCase			:							Dict	    =	np.prod(self.upsampling_ratios)
							return math.ceil(self.sampling_rate / hop_length)


	@property
	def  A(    self):
							return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0))





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


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						'''mobilenet_v2'''


	def __init__(    self				,     lowercase__=3				,     lowercase__=2_2_4				,     lowercase__=1.0				,     lowercase__=8				,     lowercase__=8				,     lowercase__=6				,     lowercase__=3_2				,     lowercase__=True				,     lowercase__=True				,     lowercase__="relu6"				,     lowercase__=True				,     lowercase__=0.8				,     lowercase__=0.0_2				,     lowercase__=0.0_0_1				,     lowercase__=2_5_5				,     **lowercase__				,     ):
							super().__init__(**lowercase__)

							if depth_multiplier <= 0:
													raise ValueError('''depth_multiplier must be greater than zero.''')

							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Dict	    =	depth_multiplier
							__UpperCAmelCase			:							Optional[Any]	    =	depth_divisible_by
							__UpperCAmelCase			:							Union[str, Any]	    =	min_depth
							__UpperCAmelCase			:							Dict	    =	expand_ratio
							__UpperCAmelCase			:							int	    =	output_stride
							__UpperCAmelCase			:							Tuple	    =	first_layer_is_expansion
							__UpperCAmelCase			:							Union[str, Any]	    =	finegrained_output
							__UpperCAmelCase			:							int	    =	hidden_act
							__UpperCAmelCase			:							Tuple	    =	tf_padding
							__UpperCAmelCase			:							List[str]	    =	classifier_dropout_prob
							__UpperCAmelCase			:							Optional[Any]	    =	initializer_range
							__UpperCAmelCase			:							List[str]	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	semantic_loss_ignore_index






class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						version.parse('''1.11''' )


	@property
	def  A(    self):
							return OrderedDict([('''pixel_values''', {0: '''batch'''})])


	@property
	def  A(    self):
							if self.task == "image-classification":
													return OrderedDict([('''logits''', {0: '''batch'''})])
							else:
													return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])


	@property
	def  A(    self):
							return 1e-4

