



import math
from typing import Optional

import numpy as np

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


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    '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		):
							_lowerCamelCase						=		'encodec'



							def __init__(						self				,    lowercase_=[1.5, 3.0, 6.0, 12.0, 24.0]				,    lowercase_=24_000				,    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_=1_024				,    lowercase_=None				,    lowercase_=True				,    **lowercase_				,    ):
													_snake_case						:					int    =   target_bandwidths
													_snake_case						:					int    =   sampling_rate
													_snake_case						:					Union[str, Any]    =   audio_channels
													_snake_case						:					List[str]    =   normalize
													_snake_case						:					List[str]    =   chunk_length_s
													_snake_case						:					Union[str, Any]    =   overlap
													_snake_case						:					Tuple    =   hidden_size
													_snake_case						:					Optional[int]    =   num_filters
													_snake_case						:					str    =   num_residual_layers
													_snake_case						:					Tuple    =   upsampling_ratios
													_snake_case						:					Union[str, Any]    =   norm_type
													_snake_case						:					Union[str, Any]    =   kernel_size
													_snake_case						:					Tuple    =   last_kernel_size
													_snake_case						:					Optional[int]    =   residual_kernel_size
													_snake_case						:					List[Any]    =   dilation_growth_rate
													_snake_case						:					Any    =   use_causal_conv
													_snake_case						:					Tuple    =   pad_mode
													_snake_case						:					Tuple    =   compress
													_snake_case						:					Union[str, Any]    =   num_lstm_layers
													_snake_case						:					Any    =   trim_right_ratio
													_snake_case						:					str    =   codebook_size
													_snake_case						:					Dict    =   codebook_dim if codebook_dim is not None else hidden_size
													_snake_case						:					List[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 	UpperCamelCase	(						self    ):
													if self.chunk_length_s is None:
																			return None
													else:
																			return int(self.chunk_length_s * self.sampling_rate    )



							@property
							def 	UpperCamelCase	(						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 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   np.prod(self.upsampling_ratios    )
													return math.ceil(self.sampling_rate / hop_length    )



							@property
							def 	UpperCamelCase	(						self    ):
													return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs