


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A      :    Tuple		=					{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    Optional[int]		=					["RoFormerTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    int		=					[
        "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "RoFormerForCausalLM",
        "RoFormerForMaskedLM",
        "RoFormerForMultipleChoice",
        "RoFormerForQuestionAnswering",
        "RoFormerForSequenceClassification",
        "RoFormerForTokenClassification",
        "RoFormerLayer",
        "RoFormerModel",
        "RoFormerPreTrainedModel",
        "load_tf_weights_in_roformer",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    List[Any]		=					[
        "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFRoFormerForCausalLM",
        "TFRoFormerForMaskedLM",
        "TFRoFormerForMultipleChoice",
        "TFRoFormerForQuestionAnswering",
        "TFRoFormerForSequenceClassification",
        "TFRoFormerForTokenClassification",
        "TFRoFormerLayer",
        "TFRoFormerModel",
        "TFRoFormerPreTrainedModel",
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A      :    Any		=					[
        "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
        "FlaxRoFormerForMaskedLM",
        "FlaxRoFormerForMultipleChoice",
        "FlaxRoFormerForQuestionAnswering",
        "FlaxRoFormerForSequenceClassification",
        "FlaxRoFormerForTokenClassification",
        "FlaxRoFormerModel",
        "FlaxRoFormerPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
    from .tokenization_roformer import RoFormerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_roformer_fast import RoFormerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roformer import (
            ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            RoFormerForCausalLM,
            RoFormerForMaskedLM,
            RoFormerForMultipleChoice,
            RoFormerForQuestionAnswering,
            RoFormerForSequenceClassification,
            RoFormerForTokenClassification,
            RoFormerLayer,
            RoFormerModel,
            RoFormerPreTrainedModel,
            load_tf_weights_in_roformer,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roformer import (
            TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForMultipleChoice,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerLayer,
            TFRoFormerModel,
            TFRoFormerPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roformer import (
            FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerModel,
            FlaxRoFormerPreTrainedModel,
        )


else:
    import sys

    A      :    Any		=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''







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 			_a  (	__a ):
       __a				:     Union[str, Any]   				=					"""encodec"""
       def __init__(	self						:	Tuple   ,				lowercase						:	List[str]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,				lowercase						:	Any=24_000   ,				lowercase						:	str=1   ,				lowercase						:	Optional[int]=False   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str=None   ,				lowercase						:	Tuple=128   ,				lowercase						:	Union[str, Any]=32   ,				lowercase						:	Union[str, Any]=1   ,				lowercase						:	Optional[Any]=[8, 5, 4, 2]   ,				lowercase						:	Any="weight_norm"   ,				lowercase						:	Tuple=7   ,				lowercase						:	int=7   ,				lowercase						:	Dict=3   ,				lowercase						:	List[Any]=2   ,				lowercase						:	str=True   ,				lowercase						:	List[str]="reflect"   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	int=1.0   ,				lowercase						:	Dict=1_024   ,				lowercase						:	str=None   ,				lowercase						:	Union[str, Any]=True   ,				**lowercase						:	Optional[int]   ,				):



           '''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__(**lowercase				)
       @property
       def     A      (	self						:	Dict				):



           '''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						:	Union[str, Any]				):



           '''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						:	Any				):



           '''simple docstring'''

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







       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)




