






"""simple docstring"""


import numpy as np



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A     ,       __A		     =					a.shape
  __A		     =					min(__UpperCamelCase						,			__UpperCamelCase					)
  __A		     =					np.eye(__UpperCamelCase					)
  __A		     =					a.copy()

  for k in range(t - 1					):
    # select a column of modified matrix A':
    __A		     =					r[k:, [k]]
    # construct first basis vector
    __A		     =					np.zeros_like(__UpperCamelCase					)
    __A		     =					1.0
    # determine scaling factor
    __A		     =					np.linalg.norm(__UpperCamelCase					)
    # construct vector v for Householder reflection
    __A		     =					x + np.sign(x[0]					) * alpha * ea
    v /= np.linalg.norm(__UpperCamelCase					)

    # construct the Householder matrix
    __A		     =					np.eye(m - k					) - 2.0 * v @ v.T
    # pad with ones and zeros as necessary
    __A		     =					np.block([[np.eye(__UpperCamelCase					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

    __A		     =					q @ q_k.T
    __A		     =					q_k @ r

  return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






'''simple docstring'''





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''vit-hybrid'''




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=7_68,     SCREAMING_SNAKE_CASE_						:					List[str]=12,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=12,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=30_72,     SCREAMING_SNAKE_CASE_						:					List[Any]="gelu",     SCREAMING_SNAKE_CASE_						:					Any=0.0,     SCREAMING_SNAKE_CASE_						:					Dict=0.0,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.02,     SCREAMING_SNAKE_CASE_						:					List[Any]=1e-12,     SCREAMING_SNAKE_CASE_						:					int=2_24,     SCREAMING_SNAKE_CASE_						:					int=1,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=3,     SCREAMING_SNAKE_CASE_						:					Any=[1, 10_24, 24, 24],     SCREAMING_SNAKE_CASE_						:					List[str]=True,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										if backbone_config is None:
																logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''   )
																snake_case							:  int			=     {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																if "model_type" in backbone_config:
																						snake_case							:  Any			=     CONFIG_MAPPING[backbone_config['''model_type''']]
																else:
																						logger.info(
																						    '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''   )
																						snake_case							:  Any			=     BitConfig
																snake_case							:  Optional[Any]			=     backbone_config_class(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  str			=     backbone_featmap_shape
										snake_case							:  Union[str, Any]			=     backbone_config
										snake_case							:  List[str]			=     hidden_size
										snake_case							:  List[Any]			=     num_hidden_layers
										snake_case							:  Dict			=     num_attention_heads
										snake_case							:  List[str]			=     intermediate_size
										snake_case							:  Optional[int]			=     hidden_act
										snake_case							:  List[str]			=     hidden_dropout_prob
										snake_case							:  Tuple			=     attention_probs_dropout_prob
										snake_case							:  Optional[int]			=     initializer_range
										snake_case							:  Optional[int]			=     layer_norm_eps
										snake_case							:  List[Any]			=     image_size
										snake_case							:  Any			=     patch_size
										snake_case							:  str			=     num_channels
										snake_case							:  List[str]			=     qkv_bias




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  Tuple			=     copy.deepcopy(self.__dict__   )
										snake_case							:  List[Any]			=     self.backbone_config.to_dict()
										snake_case							:  Optional[Any]			=     self.__class__.model_type
										return output

