

import copy

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


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

A_						:     str			  =				{}
class    A_				( UpperCAmelCase_  ):


    '''simple docstring'''






    a__       							=    "fsmt_decoder"


    def __init__(self       , lowercase__=0       , lowercase__=0					)       -> str:
        super().__init__()
        __UpperCAmelCase			   =     vocab_size
        __UpperCAmelCase			   =     bos_token_id






class    A_				( UpperCAmelCase_  ):


    '''simple docstring'''






    a__       							=    "fsmt"
    a__       							=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


    def __init__(self       , lowercase__=["en", "de"]       , lowercase__=42_024       , lowercase__=42_024       , lowercase__="relu"       , lowercase__=1_024       , lowercase__=200       , lowercase__=1_024       , lowercase__=4_096       , lowercase__=12       , lowercase__=16       , lowercase__=0.0       , lowercase__=4_096       , lowercase__=12       , lowercase__=16       , lowercase__=0.0       , lowercase__=0.0       , lowercase__=0.1       , lowercase__=0.0       , lowercase__=0.02       , lowercase__=2       , lowercase__=True       , lowercase__=True       , lowercase__=False       , lowercase__=5       , lowercase__=1.0       , lowercase__=False       , lowercase__=True       , lowercase__=1       , lowercase__=0       , lowercase__=2       , lowercase__=2       , **lowercase__       , )       -> Dict:
        __UpperCAmelCase			   =     langs
        __UpperCAmelCase			   =     src_vocab_size
        __UpperCAmelCase			   =     tgt_vocab_size
        __UpperCAmelCase			   =     d_model  # encoder_embed_dim and decoder_embed_dim

        __UpperCAmelCase			   =     encoder_ffn_dim
        __UpperCAmelCase			   =     __UpperCAmelCase			   =     encoder_layers
        __UpperCAmelCase			   =     encoder_attention_heads
        __UpperCAmelCase			   =     encoder_layerdrop
        __UpperCAmelCase			   =     decoder_layerdrop
        __UpperCAmelCase			   =     decoder_ffn_dim
        __UpperCAmelCase			   =     decoder_layers
        __UpperCAmelCase			   =     decoder_attention_heads
        __UpperCAmelCase			   =     max_position_embeddings
        __UpperCAmelCase			   =     init_std  # Normal(0, this parameter)
        __UpperCAmelCase			   =     activation_function

        __UpperCAmelCase			   =     DecoderConfig(vocab_size=__UpperCamelCase       , bos_token_id=__UpperCamelCase					)
        if "decoder" in common_kwargs:
            del common_kwargs["decoder"]

        __UpperCAmelCase			   =     scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        __UpperCAmelCase			   =     attention_dropout
        __UpperCAmelCase			   =     activation_dropout
        __UpperCAmelCase			   =     dropout

        __UpperCAmelCase			   =     use_cache
        super().__init__(
            pad_token_id=__UpperCamelCase       , bos_token_id=__UpperCamelCase       , eos_token_id=__UpperCamelCase       , decoder_start_token_id=__UpperCamelCase       , is_encoder_decoder=__UpperCamelCase       , tie_word_embeddings=__UpperCamelCase       , forced_eos_token_id=__UpperCamelCase       , max_length=__UpperCamelCase       , num_beams=__UpperCamelCase       , length_penalty=__UpperCamelCase       , early_stopping=__UpperCamelCase       , **__UpperCamelCase       , )






    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase			   =     copy.deepcopy(self.__dict__					)
        __UpperCAmelCase			   =     self.decoder.to_dict()
        __UpperCAmelCase			   =     self.__class__.model_type
        return output






def  __a    (				SCREAMING_SNAKE_CASE ) ->       float:






    '''simple docstring'''
    return -num if num < 0 else num


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_min() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE ) < abs_val(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE ) > abs(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max_sort() arg is an empty sequence''' )
    return sorted(SCREAMING_SNAKE_CASE    ,				key=SCREAMING_SNAKE_CASE )[-1]


def  __a    (				) ->       Union[str, Any]:






    '''simple docstring'''
    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    __UpperCAmelCase			   =     [-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE ) == -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       test_abs_val()
       print(abs_val(-34))  # --> 34




