





import math
from typing import Optional

import numpy as np

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


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)

__lowerCamelCase	:					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  A__       (	__snake_case      ):
      _UpperCAmelCase     :str						   =       'encodec'





      def __init__(			self	,							A_=[1.5, 3.0, 6.0, 12.0, 24.0]	,							A_=2_4000	,							A_=1	,							A_=False	,							A_=None	,							A_=None	,							A_=128	,							A_=32	,							A_=1	,							A_=[8, 5, 4, 2]	,							A_="weight_norm"	,							A_=7	,							A_=7	,							A_=3	,							A_=2	,							A_=True	,							A_="reflect"	,							A_=2	,							A_=2	,							A_=1.0	,							A_=1024	,							A_=None	,							A_=True	,							**A_	,							):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       target_bandwidths
       UpperCamelCase     : Optional[Any]	     =       sampling_rate
       UpperCamelCase     : List[str]	     =       audio_channels
       UpperCamelCase     : Optional[Any]	     =       normalize
       UpperCamelCase     : str	     =       chunk_length_s
       UpperCamelCase     : Optional[int]	     =       overlap
       UpperCamelCase     : List[Any]	     =       hidden_size
       UpperCamelCase     : Dict	     =       num_filters
       UpperCamelCase     : Optional[int]	     =       num_residual_layers
       UpperCamelCase     : str	     =       upsampling_ratios
       UpperCamelCase     : Tuple	     =       norm_type
       UpperCamelCase     : Dict	     =       kernel_size
       UpperCamelCase     : Optional[Any]	     =       last_kernel_size
       UpperCamelCase     : int	     =       residual_kernel_size
       UpperCamelCase     : Optional[Any]	     =       dilation_growth_rate
       UpperCamelCase     : Optional[Any]	     =       use_causal_conv
       UpperCamelCase     : List[Any]	     =       pad_mode
       UpperCamelCase     : List[Any]	     =       compress
       UpperCamelCase     : Union[str, Any]	     =       num_lstm_layers
       UpperCamelCase     : Tuple	     =       trim_right_ratio
       UpperCamelCase     : Dict	     =       codebook_size
       UpperCamelCase     : int	     =       codebook_dim if codebook_dim is not None else hidden_size
       UpperCamelCase     : Union[str, Any]	     =       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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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





      @property
      def 	__UpperCamelCase(			self       ):
       '''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 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       np.prod(self.upsampling_ratios       )
       return math.ceil(self.sampling_rate / hop_length       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




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







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
