import math
from typing import Optional

import numpy as np

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


lowercase__     :							List[str]            =      logging.get_logger(__name__)

lowercase__     :							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   lowercase_    (    __snake_case		):



	"""simple docstring"""





	UpperCAmelCase_    :   List[Any]  =							"""encodec"""


	def __init__(				self						,     __SCREAMING_SNAKE_CASE=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]						,     __SCREAMING_SNAKE_CASE=24000						,     __SCREAMING_SNAKE_CASE=1						,     __SCREAMING_SNAKE_CASE=False						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE=128						,     __SCREAMING_SNAKE_CASE=32						,     __SCREAMING_SNAKE_CASE=1						,     __SCREAMING_SNAKE_CASE=[8, 5, 4, 2]						,     __SCREAMING_SNAKE_CASE="weight_norm"						,     __SCREAMING_SNAKE_CASE=7						,     __SCREAMING_SNAKE_CASE=7						,     __SCREAMING_SNAKE_CASE=3						,     __SCREAMING_SNAKE_CASE=2						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE="reflect"						,     __SCREAMING_SNAKE_CASE=2						,     __SCREAMING_SNAKE_CASE=2						,     __SCREAMING_SNAKE_CASE=1.0						,     __SCREAMING_SNAKE_CASE=1024						,     __SCREAMING_SNAKE_CASE=None						,     __SCREAMING_SNAKE_CASE=True						,     **__SCREAMING_SNAKE_CASE						,     )		->Any:
								lowerCAmelCase      						=						target_bandwidths
								lowerCAmelCase      						=						sampling_rate
								lowerCAmelCase      						=						audio_channels
								lowerCAmelCase      						=						normalize
								lowerCAmelCase      						=						chunk_length_s
								lowerCAmelCase      						=						overlap
								lowerCAmelCase      						=						hidden_size
								lowerCAmelCase      						=						num_filters
								lowerCAmelCase      						=						num_residual_layers
								lowerCAmelCase      						=						upsampling_ratios
								lowerCAmelCase      						=						norm_type
								lowerCAmelCase      						=						kernel_size
								lowerCAmelCase      						=						last_kernel_size
								lowerCAmelCase      						=						residual_kernel_size
								lowerCAmelCase      						=						dilation_growth_rate
								lowerCAmelCase      						=						use_causal_conv
								lowerCAmelCase      						=						pad_mode
								lowerCAmelCase      						=						compress
								lowerCAmelCase      						=						num_lstm_layers
								lowerCAmelCase      						=						trim_right_ratio
								lowerCAmelCase      						=						codebook_size
								lowerCAmelCase      						=						codebook_dim if codebook_dim is not None else hidden_size
								lowerCAmelCase      						=						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      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:







								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							)		->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      SCREAMING_SNAKE_CASE_							(				self							)		->int:
								lowerCAmelCase      						=						np.prod(self.upsampling_ratios							)
								return math.ceil(self.sampling_rate / hop_length							)




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







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	 =      logging.get_logger(__name__)

__a	 =      {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''mobilenet_v1'''





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=3       ,							__lowerCamelCase    :       Dict=2_2_4       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Optional[int]=8       ,							__lowerCamelCase    :       Optional[int]="relu6"       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       List[str]=0.999       ,							__lowerCamelCase    :       Optional[Any]=0.02       ,							__lowerCamelCase    :       str=0.001       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

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

         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  depth_multiplier
         UpperCAmelCase      					=  min_depth
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  tf_padding
         UpperCAmelCase      					=  classifier_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  layer_norm_eps






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							version.parse('''1.11'''	)





      @property
      def 							_lowercase					(		self    :       Optional[int]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







         return OrderedDict([("""pixel_values""", {0: """batch"""})]							)





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> Mapping[str, Mapping[int, str]]:






         """simple docstring"""







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





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> float:






         """simple docstring"""







         return 1e-4



