

import math
from typing import Optional

import numpy as np

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


A											=     logging.get_logger(__name__)

A											=     {
    '''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 SCREAMING_SNAKE_CASE  (		__a							):






			"""simple docstring"""




			__A						  =  '''encodec'''






			def __init__(    self ,				__UpperCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0] ,				__UpperCamelCase=2_40_00 ,				__UpperCamelCase=1 ,				__UpperCamelCase=False ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=1_28 ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=[8, 5, 4, 2] ,				__UpperCamelCase="weight_norm" ,				__UpperCamelCase=7 ,				__UpperCamelCase=7 ,				__UpperCamelCase=3 ,				__UpperCamelCase=2 ,				__UpperCamelCase=True ,				__UpperCamelCase="reflect" ,				__UpperCamelCase=2 ,				__UpperCamelCase=2 ,				__UpperCamelCase=1.0 ,				__UpperCamelCase=10_24 ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= target_bandwidths
				snake_case_      		= sampling_rate
				snake_case_      		= audio_channels
				snake_case_      		= normalize
				snake_case_      		= chunk_length_s
				snake_case_      		= overlap
				snake_case_      		= hidden_size
				snake_case_      		= num_filters
				snake_case_      		= num_residual_layers
				snake_case_      		= upsampling_ratios
				snake_case_      		= norm_type
				snake_case_      		= kernel_size
				snake_case_      		= last_kernel_size
				snake_case_      		= residual_kernel_size
				snake_case_      		= dilation_growth_rate
				snake_case_      		= use_causal_conv
				snake_case_      		= pad_mode
				snake_case_      		= compress
				snake_case_      		= num_lstm_layers
				snake_case_      		= trim_right_ratio
				snake_case_      		= codebook_size
				snake_case_      		= codebook_dim if codebook_dim is not None else hidden_size
				snake_case_      		= 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__(**snake_case__							)






			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				if self.chunk_length_s is None:
					return None
				else:
					return int(self.chunk_length_s * self.sampling_rate							)






			@property
			def 			__lowerCAmelCase (    self							):

				"""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							)							)






			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= np.prod(self.upsampling_ratios							)
				return math.ceil(self.sampling_rate / hop_length							)







			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)							)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
