





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     _snake_case  :   Tuple						   =  'platform'

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )

def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any=None,     lowerCAmelCase_				:      Optional[int]=None,     lowerCAmelCase_				:      Dict=None,     lowerCAmelCase_				:      Optional[int]=None,     lowerCAmelCase_				:      Any=None,     lowerCAmelCase_				:      Optional[int]=None,     ):
   if attention_mask is None:
      __lowerCAmelCase							     =     np.where(input_ids != config.pad_token_id,     1,     0     )
   if decoder_attention_mask is None:
      __lowerCAmelCase							     =     np.where(decoder_input_ids != config.pad_token_id,     1,     0     )
   if head_mask is None:
      __lowerCAmelCase							     =     np.ones((config.encoder_layers, config.encoder_attention_heads)     )
   if decoder_head_mask is None:
      __lowerCAmelCase							     =     np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   if cross_attn_head_mask is None:
      __lowerCAmelCase							     =     np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[int]=1_3 ,  lowerCAmelCase_			:		Dict=7 ,  lowerCAmelCase_			:		int=True ,  lowerCAmelCase_			:		List[Any]=False ,  lowerCAmelCase_			:		Union[str, Any]=9_9 ,  lowerCAmelCase_			:		int=1_6 ,  lowerCAmelCase_			:		List[Any]=2 ,  lowerCAmelCase_			:		List[Any]=4 ,  lowerCAmelCase_			:		Any=4 ,  lowerCAmelCase_			:		int="gelu" ,  lowerCAmelCase_			:		Union[str, Any]=0.1 ,  lowerCAmelCase_			:		Union[str, Any]=0.1 ,  lowerCAmelCase_			:		List[str]=3_2 ,  lowerCAmelCase_			:		str=2 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		List[str]=0 ,  lowerCAmelCase_			:		List[Any]=0.02 ,  )							->						int:
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     batch_size
     __lowerCAmelCase							     =     seq_length
     __lowerCAmelCase							     =     is_training
     __lowerCAmelCase							     =     use_labels
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     intermediate_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     hidden_dropout_prob
     __lowerCAmelCase							     =     attention_probs_dropout_prob
     __lowerCAmelCase							     =     max_position_embeddings
     __lowerCAmelCase							     =     eos_token_id
     __lowerCAmelCase							     =     pad_token_id
     __lowerCAmelCase							     =     bos_token_id
     __lowerCAmelCase							     =     initializer_range






  def     lowercase		(    self			:		Union[str, Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size					) ,  3 ,  self.vocab_size					)
     __lowerCAmelCase							     =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,  dtype=np.intaa					)) ,  -1					)

     __lowerCAmelCase							     =     shift_tokens_right(lowerCAmelCase_ ,  1 ,  2					)

     __lowerCAmelCase							     =     BlenderbotConfig(
         vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_id=self.eos_token_id ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  initializer_range=self.initializer_range ,  use_cache=lowerCAmelCase_ ,  )
     __lowerCAmelCase							     =     prepare_blenderbot_inputs_dict(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     return config, inputs_dict






  def     lowercase		(    self			:		Optional[int]					)							->						Optional[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.prepare_config_and_inputs()
     return config, inputs_dict






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		List[str]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     2_0
     __lowerCAmelCase							     =     model_class_name(lowerCAmelCase_					)

     __lowerCAmelCase							     =     model.encode(inputs_dict['input_ids']					)

     __lowerCAmelCase     ,     __lowerCAmelCase							     =     (
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     __lowerCAmelCase							     =     model.init_cache(decoder_input_ids.shape[0] ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype='i4'					)

     __lowerCAmelCase							     =     jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
     __lowerCAmelCase							     =     model.decode(
         decoder_input_ids[:, :-1] ,  lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_ ,  past_key_values=lowerCAmelCase_ ,  decoder_position_ids=lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4'					)
     __lowerCAmelCase							     =     model.decode(
         decoder_input_ids[:, -1:] ,  lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_ ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     model.decode(lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
     self.parent.assertTrue(diff < 1e-3 ,  msg=f"""Max diff is {diff}"""					)






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		int					)							->						Optional[int]:
     __lowerCAmelCase							     =     2_0
     __lowerCAmelCase							     =     model_class_name(lowerCAmelCase_					)

     __lowerCAmelCase							     =     model.encode(inputs_dict['input_ids']					)

     __lowerCAmelCase     ,     __lowerCAmelCase							     =     (
         inputs_dict['decoder_input_ids'],
         inputs_dict['decoder_attention_mask'],
     )

     __lowerCAmelCase							     =     jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
         ] ,  axis=-1 ,  )

     __lowerCAmelCase							     =     model.init_cache(decoder_input_ids.shape[0] ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

     __lowerCAmelCase							     =     model.decode(
         decoder_input_ids[:, :-1] ,  lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_ ,  past_key_values=lowerCAmelCase_ ,  decoder_position_ids=lowerCAmelCase_ ,  )
     __lowerCAmelCase							     =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype='i4'					)
     __lowerCAmelCase							     =     model.decode(
         decoder_input_ids[:, -1:] ,  lowerCAmelCase_ ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=lowerCAmelCase_ ,  decoder_position_ids=lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     model.decode(lowerCAmelCase_ ,  lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_					)

     __lowerCAmelCase							     =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])					)					)
     self.parent.assertTrue(diff < 1e-3 ,  msg=f"""Max diff is {diff}"""					)





@require_flax
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  a_      =			99






  def     lowercase		(    self			:		List[Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     np.array(
         [
             [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
             [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
             [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
             [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
             [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
             [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
             [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
             [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
             [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
             [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
             [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
             [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
             [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
         ] ,  dtype=np.intaa ,  )

     __lowerCAmelCase							     =     input_ids.shape[0]
     __lowerCAmelCase							     =     BlenderbotConfig(
         vocab_size=self.vocab_size ,  d_model=2_4 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=3_2 ,  decoder_ffn_dim=3_2 ,  max_position_embeddings=4_8 ,  eos_token_id=2 ,  pad_token_id=1 ,  bos_token_id=0 ,  )
     return config, input_ids, batch_size






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self._get_config_and_data()
     __lowerCAmelCase							     =     FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_					)
     __lowerCAmelCase							     =     lm_model(input_ids=lowerCAmelCase_					)
     __lowerCAmelCase							     =     (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs['logits'].shape ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     BlenderbotConfig(
         vocab_size=self.vocab_size ,  d_model=1_4 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=8 ,  decoder_ffn_dim=8 ,  max_position_embeddings=4_8 ,  )
     __lowerCAmelCase							     =     FlaxBlenderbotForConditionalGeneration(lowerCAmelCase_					)
     __lowerCAmelCase							     =     np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] ,  dtype=np.intaa					)
     __lowerCAmelCase							     =     np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] ,  dtype=np.intaa					)
     __lowerCAmelCase							     =     lm_model(input_ids=lowerCAmelCase_ ,  decoder_input_ids=lowerCAmelCase_					)
     __lowerCAmelCase							     =     (*summary.shape, config.vocab_size)
     self.assertEqual(outputs['logits'].shape ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] ,  dtype=np.intaa					)
     __lowerCAmelCase							     =     shift_tokens_right(lowerCAmelCase_ ,  1 ,  2					)
     __lowerCAmelCase							     =     np.equal(lowerCAmelCase_ ,  1					).astype(np.floataa					).sum()
     __lowerCAmelCase							     =     np.equal(lowerCAmelCase_ ,  1					).astype(np.floataa					).sum()
     self.assertEqual(shifted.shape ,  input_ids.shape					)
     self.assertEqual(lowerCAmelCase_ ,  n_pad_before - 1					)
     self.assertTrue(np.equal(shifted[:, 0] ,  2					).all()					)





@require_flax
class     _UpperCAmelCase   (   _UpperCamelCase		,   unittest.TestCase		,   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			True
  a_      =			(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  a_      =			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     FlaxBlenderbotModelTester(self					)






  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Any					)							->						Tuple:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Optional[int]:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__					):
           __lowerCAmelCase							     =     self._prepare_for_class(lowerCAmelCase_ ,  lowerCAmelCase_					)
           __lowerCAmelCase							     =     model_class(lowerCAmelCase_					)

           @jax.jit
           def encode_jitted(lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		int=None ,  **lowerCAmelCase_			:		Union[str, Any]					):
              return model.encode(input_ids=lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_					)

           with self.subTest('JIT Enabled'					):
              __lowerCAmelCase							     =     encode_jitted(**lowerCAmelCase_					).to_tuple()

           with self.subTest('JIT Disabled'					):
              with jax.disable_jit():
                 __lowerCAmelCase							     =     encode_jitted(**lowerCAmelCase_					).to_tuple()

           self.assertEqual(len(lowerCAmelCase_					) ,  len(lowerCAmelCase_					)					)
           for jitted_output, output in zip(lowerCAmelCase_ ,  lowerCAmelCase_					):
              self.assertEqual(jitted_output.shape ,  output.shape					)






  def     lowercase		(    self			:		Dict					)							->						Dict:
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__					):
           __lowerCAmelCase							     =     model_class(lowerCAmelCase_					)
           __lowerCAmelCase							     =     model.encode(inputs_dict['input_ids'] ,  inputs_dict['attention_mask']					)

           __lowerCAmelCase							     =     {
               'decoder_input_ids': inputs_dict['decoder_input_ids'],
               'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
               'encoder_outputs': encoder_outputs,
           }

           @jax.jit
           def decode_jitted(lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Optional[int]					):
              return model.decode(
                  decoder_input_ids=lowerCAmelCase_ ,  decoder_attention_mask=lowerCAmelCase_ ,  encoder_outputs=lowerCAmelCase_ ,  )

           with self.subTest('JIT Enabled'					):
              __lowerCAmelCase							     =     decode_jitted(**lowerCAmelCase_					).to_tuple()

           with self.subTest('JIT Disabled'					):
              with jax.disable_jit():
                 __lowerCAmelCase							     =     decode_jitted(**lowerCAmelCase_					).to_tuple()

           self.assertEqual(len(lowerCAmelCase_					) ,  len(lowerCAmelCase_					)					)
           for jitted_output, output in zip(lowerCAmelCase_ ,  lowerCAmelCase_					):
              self.assertEqual(jitted_output.shape ,  output.shape					)






  @slow
  def     lowercase		(    self			:		Dict					)							->						List[Any]:
     for model_class_name in self.all_model_classes:
        __lowerCAmelCase							     =     model_class_name.from_pretrained('facebook/blenderbot-400M-distill'					)
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        __lowerCAmelCase							     =     np.ones((1, 1)					) * model.config.eos_token_id
        __lowerCAmelCase							     =     model(lowerCAmelCase_					)
        self.assertIsNotNone(lowerCAmelCase_					)






  @unittest.skipUnless(jax_device != 'cpu' ,  '3B test too slow on CPU.'					)
  @slow
  def     lowercase		(    self			:		List[str]					)							->						str:
     __lowerCAmelCase							     =     {'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
     __lowerCAmelCase							     =     {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

     __lowerCAmelCase							     =     FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' ,  from_pt=lowerCAmelCase_					)
     __lowerCAmelCase							     =     BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'					)

     __lowerCAmelCase							     =     ['Sam']
     __lowerCAmelCase							     =     tokenizer(lowerCAmelCase_ ,  return_tensors='jax'					)

     __lowerCAmelCase							     =     model.generate(**lowerCAmelCase_ ,  **lowerCAmelCase_					)
     __lowerCAmelCase							     =     'Sam is a great name. It means "sun" in Gaelic.'

     __lowerCAmelCase							     =     tokenizer.batch_decode(lowerCAmelCase_ ,  **lowerCAmelCase_					)
     assert generated_txt[0].strip() == tgt_text








import math
from typing import Optional

import numpy as np

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


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

_snake_case  :   Dict						   =  {
    '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   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""encodec"""






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0] ,  lowerCAmelCase_			:		Optional[Any]=2_4_0_0_0 ,  lowerCAmelCase_			:		int=1 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[Any]=1_2_8 ,  lowerCAmelCase_			:		List[Any]=3_2 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		Optional[int]=[8, 5, 4, 2] ,  lowerCAmelCase_			:		Optional[Any]="weight_norm" ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		Optional[int]=7 ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[Any]=2 ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Dict="reflect" ,  lowerCAmelCase_			:		List[Any]=2 ,  lowerCAmelCase_			:		int=2 ,  lowerCAmelCase_			:		Dict=1.0 ,  lowerCAmelCase_			:		str=1_0_2_4 ,  lowerCAmelCase_			:		Dict=None ,  lowerCAmelCase_			:		List[Any]=True ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						Union[str, Any]:
     __lowerCAmelCase							     =     target_bandwidths
     __lowerCAmelCase							     =     sampling_rate
     __lowerCAmelCase							     =     audio_channels
     __lowerCAmelCase							     =     normalize
     __lowerCAmelCase							     =     chunk_length_s
     __lowerCAmelCase							     =     overlap
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_filters
     __lowerCAmelCase							     =     num_residual_layers
     __lowerCAmelCase							     =     upsampling_ratios
     __lowerCAmelCase							     =     norm_type
     __lowerCAmelCase							     =     kernel_size
     __lowerCAmelCase							     =     last_kernel_size
     __lowerCAmelCase							     =     residual_kernel_size
     __lowerCAmelCase							     =     dilation_growth_rate
     __lowerCAmelCase							     =     use_causal_conv
     __lowerCAmelCase							     =     pad_mode
     __lowerCAmelCase							     =     compress
     __lowerCAmelCase							     =     num_lstm_layers
     __lowerCAmelCase							     =     trim_right_ratio
     __lowerCAmelCase							     =     codebook_size
     __lowerCAmelCase							     =     codebook_dim if codebook_dim is not None else hidden_size
     __lowerCAmelCase							     =     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     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     if self.chunk_length_s is None:
        return None
     else:
        return int(self.chunk_length_s * self.sampling_rate					)






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     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     lowercase		(    self			:		Any					)							->						int:
     __lowerCAmelCase							     =     np.prod(self.upsampling_ratios					)
     return math.ceil(self.sampling_rate / hop_length					)






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)					)

