


'''simple docstring'''



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available






@dataclass
class  UpperCamelCase__(     lowerCAmelCase ):
    __magic_name__       :   Union[List[np.ndarray], torch.FloatTensor]


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .pipeline_text_to_video_synth import TextToVideoSDPipeline
 from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
 from .pipeline_text_to_video_zero import TextToVideoZeroPipeline









'''simple docstring'''



import math
from typing import Optional

import numpy as np

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


_lowercase    :    Union[str, Any]			     =  logging.get_logger(__name__)

_lowercase    :    List[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  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			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   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




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




				@property
				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""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   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




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





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )





