'''simple docstring'''




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Tuple     =  {
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						"t5"
      __UpperCamelCase:  Optional[int]						=						["past_key_values"]
      __UpperCamelCase:  Dict						=						{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




      def __init__(       self						:				str						,					A						:				Optional[Any]=32128						,					A						:				List[Any]=512						,					A						:				int=64						,					A						:				Union[str, Any]=2048						,					A						:				Dict=6						,					A						:				int=None						,					A						:				Any=8						,					A						:				List[str]=32						,					A						:				Optional[Any]=128						,					A						:				Tuple=0.1						,					A						:				Any=1E-6						,					A						:				List[Any]=1.0						,					A						:				List[Any]="relu"						,					A						:				int=True						,					A						:				Any=True						,					A						:				List[str]=0						,					A						:				Optional[Any]=1						,					**A						:				int						,					):
             _UpperCAmelCase     :  List[Any]							      =    vocab_size
             _UpperCAmelCase     :  Optional[int]							      =    d_model
             _UpperCAmelCase     :  Tuple							      =    d_kv
             _UpperCAmelCase     :  List[str]							      =    d_ff
             _UpperCAmelCase     :  Optional[Any]							      =    num_layers
             _UpperCAmelCase     :  List[str]							      =    (
                 num_decoder_layers if num_decoder_layers is not None else self.num_layers
             )  # default = symmetry
             _UpperCAmelCase     :  Optional[Any]							      =    num_heads
             _UpperCAmelCase     :  int							      =    relative_attention_num_buckets
             _UpperCAmelCase     :  str							      =    relative_attention_max_distance
             _UpperCAmelCase     :  str							      =    dropout_rate
             _UpperCAmelCase     :  int							      =    layer_norm_epsilon
             _UpperCAmelCase     :  Dict							      =    initializer_factor
             _UpperCAmelCase     :  str							      =    feed_forward_proj
             _UpperCAmelCase     :  List[str]							      =    use_cache

             _UpperCAmelCase     :  Union[str, Any]							      =    self.feed_forward_proj.split("-"       )
             _UpperCAmelCase     :  Optional[int]							      =    act_info[-1]
             _UpperCAmelCase     :  str							      =    act_info[0] == "gated"

             if len(A       ) > 1 and act_info[0] != "gated" or len(A       ) > 2:
                    raise ValueError(
                        F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                        "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                        "'gated-gelu' or 'relu'"       )

             # for backwards compatibility
             if feed_forward_proj == "gated-gelu":
                    _UpperCAmelCase     :  Union[str, Any]							      =    "gelu_new"

             super().__init__(
                 pad_token_id=A						,					eos_token_id=A						,					is_encoder_decoder=A						,					**A						,					)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @property
      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Tuple							      =    {
                 "input_ids": {0: "batch", 1: "encoder_sequence"},
                 "attention_mask": {0: "batch", 1: "encoder_sequence"},
             }
             if self.use_past:
                    _UpperCAmelCase     :  Dict							      =    "past_encoder_sequence + sequence"
                    _UpperCAmelCase     :  Union[str, Any]							      =    {0: "batch"}
                    _UpperCAmelCase     :  Optional[int]							      =    {0: "batch", 1: "past_decoder_sequence + sequence"}
             else:
                    _UpperCAmelCase     :  int							      =    {0: "batch", 1: "decoder_sequence"}
                    _UpperCAmelCase     :  Any							      =    {0: "batch", 1: "decoder_sequence"}

             if self.use_past:
                    self.fill_with_past_key_values_(A						,					direction="inputs"       )

             return common_inputs





      @property
      def      _A	(       self						:				Optional[Any]       ):
             return 13


'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    codebook_dim if codebook_dim is not None else hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    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__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             if self.chunk_length_s is None:
                    return None
             else:
                    return int(self.chunk_length_s * self.sampling_rate       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             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						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





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

