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


__lowerCamelCase	:					Any			  =					logging.get_logger(__name__)

__lowerCamelCase	:					Tuple			  =					{
    "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  A__       (	lowercase_      ):
      _UpperCAmelCase     :Any						   =       "speech_to_text"
      _UpperCAmelCase     :List[str]						   =       ["past_key_values"]
      _UpperCAmelCase     :List[str]						   =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





      def __init__(			self	,							A_=1_0000	,							A_=12	,							A_=2048	,							A_=4	,							A_=6	,							A_=2048	,							A_=4	,							A_=0.0	,							A_=0.0	,							A_=True	,							A_=True	,							A_="relu"	,							A_=256	,							A_=0.1	,							A_=0.0	,							A_=0.0	,							A_=0.02	,							A_=2	,							A_=True	,							A_=1	,							A_=0	,							A_=2	,							A_=6000	,							A_=1024	,							A_=2	,							A_=(5, 5)	,							A_=1024	,							A_=80	,							A_=1	,							**A_	,							):
       '''simple docstring'''




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







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       )
