




"""simple docstring"""

import math

import flax.linen as nn
import jax.numpy as jnp






def 		_snake_case	(   _snake_case				:   jnp.ndarray							,    _snake_case				:   int							,    _snake_case				:   float = 1							,    _snake_case				:   float = 1							,    _snake_case				:   float = 1.0E4							,    _snake_case				:   bool = False							,    _snake_case				:   float = 1.0							,    )       ->   jnp.ndarray:






      '''simple docstring'''


      assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
      assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
      _A     =  float(embedding_dim // 2				)
      _A     =  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
      _A     =  min_timescale * jnp.exp(jnp.arange(_snake_case							,    dtype=jnp.floataa				) * -log_timescale_increment				)
      _A     =  jnp.expand_dims(_snake_case							,    1				) * jnp.expand_dims(_snake_case							,    0				)

      # scale embeddings
      _A     =  scale * emb

      if flip_sin_to_cos:
            _A     =  jnp.concatenate([jnp.cos(_snake_case				), jnp.sin(_snake_case				)]							,    axis=1				)
      else:
            _A     =  jnp.concatenate([jnp.sin(_snake_case				), jnp.cos(_snake_case				)]							,    axis=1				)
      _A     =  jnp.reshape(_snake_case							,    [jnp.shape(_snake_case				)[0], embedding_dim]				)
      return signal





class 							lowercase_			(	nn.Module				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	jnp.dtype     =      jnp.floataa

 @nn.compact
 def __call__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			str						):
       _A     =  nn.Dense(self.time_embed_dim       ,				dtype=self.dtype       ,				name='linear_1'						)(_UpperCAmelCase						)
       _A     =  nn.silu(_UpperCAmelCase						)
       _A     =  nn.Dense(self.time_embed_dim       ,				dtype=self.dtype       ,				name='linear_2'						)(_UpperCAmelCase						)
       return temb





class 							lowercase_			(	nn.Module				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	float     =      1

 @nn.compact
 def __call__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			str						):
       return get_sinusoidal_embeddings(
           _UpperCAmelCase       ,				embedding_dim=self.dim       ,				flip_sin_to_cos=self.flip_sin_to_cos       ,				freq_shift=self.freq_shift						)







"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

