

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				'oneformer'
    lowerCamelCase						      =				{'hidden_size': 'hidden_dim'}


    def __init__( self					:			List[Any],lowercase_					:			Tuple = None,lowercase_					:			Dict = 2_5_5,lowercase_					:			str = 1_5_0,lowercase_					:			Optional[Any] = 0.1,lowercase_					:			int = 2.0,lowercase_					:			Tuple = 5.0,lowercase_					:			List[Any] = 5.0,lowercase_					:			str = 0.5,lowercase_					:			List[Any] = 0.07,lowercase_					:			str = 1_2_5_4_4,lowercase_					:			Optional[Any] = 3.0,lowercase_					:			Optional[Any] = 0.75,lowercase_					:			str = 0.02,lowercase_					:			Optional[Any] = 1.0,lowercase_					:			Any = 1E-05,lowercase_					:			Optional[int] = False,lowercase_					:			Dict = True,lowercase_					:			int = True,lowercase_					:			List[Any] = [4, 8, 1_6, 3_2],lowercase_					:			int = 7_7,lowercase_					:			Optional[int] = 2_5_6,lowercase_					:			Any = 7_7,lowercase_					:			Tuple = 6,lowercase_					:			str = 4_9_4_0_8,lowercase_					:			Any = 2,lowercase_					:			Optional[int] = 1_6,lowercase_					:			Optional[Any] = 2_5_6,lowercase_					:			List[Any] = 2_5_6,lowercase_					:			Dict = 2_5_6,lowercase_					:			Optional[int] = 1_0_2_4,lowercase_					:			Union[str, Any] = "GN",lowercase_					:			Optional[Any] = 6,lowercase_					:			Tuple = 1_0,lowercase_					:			Optional[int] = True,lowercase_					:			Tuple = 8,lowercase_					:			Tuple = 0.1,lowercase_					:			List[Any] = 2_0_4_8,lowercase_					:			List[str] = False,lowercase_					:			Optional[Any] = False,lowercase_					:			List[str] = 2,lowercase_					:			List[Any] = 4,**lowercase_					:			List[Any],)->       Union[str, Any]:





        '''simple docstring'''







        if backbone_config is None:
            logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'						)
            A__        =  CONFIG_MAPPING['''swin'''](
                image_size=2_2_4,in_channels=3,patch_size=4,embed_dim=9_6,depths=[2, 2, 6, 2],num_heads=[3, 6, 1_2, 2_4],window_size=7,drop_path_rate=0.3,use_absolute_embeddings=_UpperCAmelCase,out_features=['stage1', 'stage2', 'stage3', 'stage4'],)
        elif isinstance(_UpperCAmelCase,_UpperCAmelCase						):
            A__        =  backbone_config.get('model_type'						)
            A__        =  CONFIG_MAPPING[backbone_model_type]
            A__        =  config_class.from_dict(_UpperCAmelCase						)

        A__        =  backbone_config

        A__        =  ignore_value
        A__        =  num_queries
        A__        =  no_object_weight
        A__        =  class_weight
        A__        =  mask_weight
        A__        =  dice_weight
        A__        =  contrastive_weight
        A__        =  contrastive_temperature
        A__        =  train_num_points
        A__        =  oversample_ratio
        A__        =  importance_sample_ratio
        A__        =  init_std
        A__        =  init_xavier_std
        A__        =  layer_norm_eps
        A__        =  is_training
        A__        =  use_auxiliary_loss
        A__        =  output_auxiliary_logits
        A__        =  strides
        A__        =  task_seq_len
        A__        =  text_encoder_width
        A__        =  text_encoder_context_length
        A__        =  text_encoder_num_layers
        A__        =  text_encoder_vocab_size
        A__        =  text_encoder_proj_layers
        A__        =  text_encoder_n_ctx
        A__        =  conv_dim
        A__        =  mask_dim
        A__        =  hidden_dim
        A__        =  encoder_feedforward_dim
        A__        =  norm
        A__        =  encoder_layers
        A__        =  decoder_layers
        A__        =  use_task_norm
        A__        =  num_attention_heads
        A__        =  dropout
        A__        =  dim_feedforward
        A__        =  pre_norm
        A__        =  enforce_input_proj
        A__        =  query_dec_layers
        A__        =  common_stride
        A__        =  decoder_layers

        super().__init__(**_UpperCAmelCase						)


    def 		snake_case__	( self					:			Optional[Any]						)->       Dict:





        '''simple docstring'''







        A__        =  copy.deepcopy(self.__dict__						)
        A__        =  self.backbone_config.to_dict()
        A__        =  self.__class__.model_type
        return output








'''simple docstring'''






import timeit


class 	_lowercase    :


   def __init__(			self     , _UpperCAmelCase       ):
       if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ):  # Initializes a matrix identical to the one provided.
           A							:							List[Any]				=    arg
           A							:							Dict				=    len(_UpperCAmelCase       )
       else:  # Initializes a square matrix of the given size and set values to zero.
           A							:							str				=    arg
           A							:							Union[str, Any]				=    [[0 for _ in range(self.n       )] for _ in range(self.n       )]


   def __mul__(			self     , _UpperCAmelCase       ):
       A							:							List[str]				=    Matrix(self.n       )
       for i in range(self.n       ):
           for j in range(self.n       ):
               for k in range(self.n       ):
                   matrix.t[i][j] += self.t[i][k] * b.t[k][j]
       return matrix







def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			int		)		->	Union[str, Any]:
    A							:							Tuple				=    Matrix([[1, 0], [0, 1]]		)
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 				_lowerCamelCase(	UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			Any		)		->	Any:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa
    A							:							Optional[Any]				=    Matrix([[1, 1], [1, 0]]		)
    A							:							int				=    modular_exponentiation(UpperCamelCase__     ,							n - 2		)
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			Any		)		->	List[Any]:
    # Trivial Cases
    if n == 1:
        return fa
    elif n == 2:
        return fa

    A							:							str				=    fa
    A							:							List[Any]				=    fa
    n -= 2

    while n > 0:
        A,						A							:							List[str]				=    fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 				_lowerCamelCase(	)		->	Union[str, Any]:
    A							:							Any				=    '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
    A							:							Optional[int]				=    '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
    A							:							List[Any]				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print('''With matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	List[Any]:
    A							:							str				=    '''
from random import randint
from __main__ import simple_fibonacci
'''
    A							:							Any				=    '''simple_fibonacci(randint(1,70000), 1, 1)'''
    A							:							str				=    timeit.timeit(setup=UpperCamelCase__     ,							stmt=UpperCamelCase__     ,							number=100		)
    print(
        '''Without matrix exponentiation the average execution time is '''     ,							exec_time / 100		)
    return exec_time
def 				_lowerCamelCase(	)		->	Optional[Any]:
    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()
