
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     (      snake_case_						):
	lowerCAmelCase__         = 'encodec'



	def __init__(    self						,  lowercase=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]						,  lowercase=24000						,  lowercase=1						,  lowercase=False						,  lowercase=None						,  lowercase=None						,  lowercase=128						,  lowercase=32						,  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=1024						,  lowercase=None						,  lowercase=True						,  **lowercase						,  )				->       Tuple:
					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__(**lowercase )



	@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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

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


