
import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_UpperCamelCase:					int     =[[12, 10], [3, 9]]
_UpperCamelCase:					int     =[[3, 4], [7, 4]]
_UpperCamelCase:					List[str]     =[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_UpperCamelCase:					Union[str, Any]     =[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_UpperCamelCase:					Dict     =[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_UpperCamelCase:					Optional[Any]     =[1]
_UpperCamelCase:					Tuple     =[2]

_UpperCamelCase:					Optional[Any]     =logging.getLogger()
_UpperCamelCase:					Union[str, Any]     =logging.DEBUG
_UpperCamelCase:					int     =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 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  Tuple    ):






  """simple docstring"""




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



@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 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




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



@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 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




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



@pytest.mark.mat_ops
def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		(3.5 * np.array(__SCREAMING_SNAKE_CASE    )).tolist()
  _lowerCAmelCase						=		matop.scalar_multiply(__SCREAMING_SNAKE_CASE		,				3.5    )
  assert theo == act



@pytest.mark.mat_ops
def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		(np.identity(5    )).tolist()
  _lowerCAmelCase						=		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 	_a  (					__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




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


import copy
from typing import Dict, Optional

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


_UpperCamelCase:					str     =logging.get_logger(__name__)

_UpperCamelCase:					Optional[Any]     ={
    '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 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







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



       def __init__(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			Optional[Dict] = None  ,      _lowerCAmelCase					:			int = 255  ,      _lowerCAmelCase					:			int = 150  ,      _lowerCAmelCase					:			int = 0.1  ,      _lowerCAmelCase					:			float = 2.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 5.0  ,      _lowerCAmelCase					:			float = 0.5  ,      _lowerCAmelCase					:			float = 0.07  ,      _lowerCAmelCase					:			int = 1_2544  ,      _lowerCAmelCase					:			float = 3.0  ,      _lowerCAmelCase					:			float = 0.75  ,      _lowerCAmelCase					:			float = 0.02  ,      _lowerCAmelCase					:			float = 1.0  ,      _lowerCAmelCase					:			float = 1e-05  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			Optional[list] = [4, 8, 16, 32]  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 77  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 4_9408  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 16  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 256  ,      _lowerCAmelCase					:			int = 1024  ,      _lowerCAmelCase					:			str = "GN"  ,      _lowerCAmelCase					:			int = 6  ,      _lowerCAmelCase					:			int = 10  ,      _lowerCAmelCase					:			bool = True  ,      _lowerCAmelCase					:			int = 8  ,      _lowerCAmelCase					:			float = 0.1  ,      _lowerCAmelCase					:			int = 2048  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			bool = False  ,      _lowerCAmelCase					:			int = 2  ,      _lowerCAmelCase					:			int = 4  ,      **_lowerCAmelCase					:			Any  ,      )					->   Tuple:
         if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
           _lowerCAmelCase						=		CONFIG_MAPPING['swin'](
               image_size=224  ,      in_channels=3  ,      patch_size=4  ,      embed_dim=96  ,      depths=[2, 2, 6, 2]  ,      num_heads=[3, 6, 12, 24]  ,      window_size=7  ,      drop_path_rate=0.3  ,      use_absolute_embeddings=_lowerCAmelCase  ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,      )
         elif isinstance(_lowerCAmelCase  ,      _lowerCAmelCase       ):
           _lowerCAmelCase						=		backbone_config.get('model_type'       )
           _lowerCAmelCase						=		CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						=		config_class.from_dict(_lowerCAmelCase       )

         _lowerCAmelCase						=		backbone_config

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

         super().__init__(**_lowerCAmelCase       )






       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Dict[str, any]:
         _lowerCAmelCase						=		copy.deepcopy(self.__dict__       )
         _lowerCAmelCase						=		self.backbone_config.to_dict()
         _lowerCAmelCase						=		self.__class__.model_type
         return output
