
import copy

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


_A       =					logging.get_logger(__name__)

_A       =					{}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				int 	=						"fsmt_decoder"





     def __init__(self :       List[str]							,       _A :       Tuple=0							,       _A :       Optional[int]=0  )		->		str:
         super().__init__()
         snake_case						=   vocab_size
         snake_case						=   bos_token_id







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				Optional[Any] 	=						"fsmt"
     UpperCAmelCase__							:				Optional[int] 	=						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(self :       List[Any]							,       _A :       Dict=["en", "de"]							,       _A :       Optional[int]=4_2_0_2_4							,       _A :       Any=4_2_0_2_4							,       _A :       Union[str, Any]="relu"							,       _A :       Optional[Any]=1_0_2_4							,       _A :       str=2_0_0							,       _A :       Tuple=1_0_2_4							,       _A :       Union[str, Any]=4_0_9_6							,       _A :       Optional[Any]=1_2							,       _A :       int=1_6							,       _A :       List[str]=0.0							,       _A :       Tuple=4_0_9_6							,       _A :       Optional[int]=1_2							,       _A :       List[str]=1_6							,       _A :       Tuple=0.0							,       _A :       Union[str, Any]=0.0							,       _A :       Any=0.1							,       _A :       Union[str, Any]=0.0							,       _A :       List[str]=0.02							,       _A :       List[Any]=2							,       _A :       Optional[int]=True							,       _A :       Optional[int]=True							,       _A :       int=False							,       _A :       Any=5							,       _A :       Dict=1.0							,       _A :       str=False							,       _A :       Tuple=True							,       _A :       List[Any]=1							,       _A :       Tuple=0							,       _A :       Dict=2							,       _A :       int=2							,       **_A :       Dict							,       )		->		Any:
         snake_case						=   langs
         snake_case						=   src_vocab_size
         snake_case						=   tgt_vocab_size
         snake_case						=   d_model  # encoder_embed_dim and decoder_embed_dim

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

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

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

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

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





     def 						UpperCAmelCase(self :       int  )		->		List[Any]:
         snake_case						=   copy.deepcopy(self.__dict__  )
         snake_case						=   self.decoder.to_dict()
         snake_case						=   self.__class__.model_type
         return output



import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_A       =					[[12, 10], [3, 9]]
_A       =					[[3, 4], [7, 4]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_A       =					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_A       =					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_A       =					[1]
_A       =					[2]

_A       =					logging.getLogger()
_A       =					logging.DEBUG
_A       =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Dict:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} returned integer'						)
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_addition.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) + np.array(A__						)).tolist()
        snake_case						=   matop.add(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_addition.__name__} with different matrix dims'						)
            matop.add(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				List[Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} returned integer'						)
            matop.subtract(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'						)
        snake_case						=   (np.array(A__						) - np.array(A__						)).tolist()
        snake_case						=   matop.subtract(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")					,						[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]						)
def 		lowercase_     (  A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2) or (np.array(A__						)).shape < (2, 2):
        logger.info(F'\n\t{test_multiplication.__name__} returned integer'						)
        with pytest.raises(A__						):
            matop.add(A__					,						A__						)
    elif (np.array(A__						)).shape == (np.array(A__						)).shape:
        logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'						)
        snake_case						=   (np.matmul(A__					,						A__						)).tolist()
        snake_case						=   matop.multiply(A__					,						A__						)
        assert theo == act
    else:
        with pytest.raises(A__						):
            logger.info(
                F'\n\t{test_multiplication.__name__} does not meet dim requirements'						)
            assert matop.subtract(A__					,						A__						)



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   (3.5 * np.array(A__						)).tolist()
    snake_case						=   matop.scalar_multiply(A__					,						3.5						)
    assert theo == act



@pytest.mark.mat_ops
def 		lowercase_     (  )				->				Any:
    """simple docstring"""

    snake_case						=   (np.identity(5						)).tolist()
    snake_case						=   matop.identity(5						)
    assert theo == act





@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"					,						[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]						)
def 		lowercase_     (  A__						)				->				List[str]:
    """simple docstring"""

    if (np.array(A__						)).shape < (2, 2):
        with pytest.raises(A__						):
            logger.info(F'\n\t{test_transpose.__name__} returned integer'						)
            matop.transpose(A__						)
    else:
        snake_case						=   (np.transpose(A__						)).tolist()
        snake_case						=   matop.transpose(A__					,						return_map=A__						)
        assert theo == act

