





'''simple docstring'''

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


UpperCAmelCase					:		Any		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[str]		=							{
    '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 lowerCamelCase						(a__					):
  _lowercase  :	Union[str, Any]       					=				"""speech_to_text"""
  _lowercase  :	str       					=				["""past_key_values"""]
  _lowercase  :	List[str]       					=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







  def __init__(		self     ,     lowercase__=10_000     ,     lowercase__=12     ,     lowercase__=2_048     ,     lowercase__=4     ,     lowercase__=6     ,     lowercase__=2_048     ,     lowercase__=4     ,     lowercase__=0.0     ,     lowercase__=0.0     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__="relu"     ,     lowercase__=256     ,     lowercase__=0.1     ,     lowercase__=0.0     ,     lowercase__=0.0     ,     lowercase__=0.02     ,     lowercase__=2     ,     lowercase__=True     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=2     ,     lowercase__=6_000     ,     lowercase__=1_024     ,     lowercase__=2     ,     lowercase__=(5, 5)     ,     lowercase__=1_024     ,     lowercase__=80     ,     lowercase__=1     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        _snake_case		:	int					     =     vocab_size
        _snake_case		:	str					     =     d_model
        _snake_case		:	Optional[Any]					     =     encoder_ffn_dim
        _snake_case		:	List[str]					     =     encoder_layers
        _snake_case		:	List[Any]					     =     encoder_attention_heads
        _snake_case		:	Union[str, Any]					     =     decoder_ffn_dim
        _snake_case		:	List[str]					     =     decoder_layers
        _snake_case		:	Any					     =     decoder_attention_heads
        _snake_case		:	Any					     =     dropout
        _snake_case		:	List[str]					     =     attention_dropout
        _snake_case		:	Dict					     =     activation_dropout
        _snake_case		:	List[str]					     =     activation_function
        _snake_case		:	Optional[int]					     =     init_std
        _snake_case		:	List[str]					     =     encoder_layerdrop
        _snake_case		:	Any					     =     decoder_layerdrop
        _snake_case		:	Any					     =     use_cache
        _snake_case		:	Optional[Any]					     =     encoder_layers
        _snake_case		:	Optional[int]					     =     scale_embedding  # scale factor will be sqrt(d_model) if True
        _snake_case		:	int					     =     max_source_positions
        _snake_case		:	Optional[int]					     =     max_target_positions
        _snake_case		:	int					     =     num_conv_layers
        _snake_case		:	Optional[int]					     =     list(lowercase__		)
        _snake_case		:	List[str]					     =     conv_channels
        _snake_case		:	Tuple					     =     input_feat_per_channel
        _snake_case		:	int					     =     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=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__     ,     is_encoder_decoder=lowercase__     ,     decoder_start_token_id=lowercase__     ,     **lowercase__     ,     )









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


