
"""simple docstring"""






# Function to print upper half of diamond (pyramid)




def       A_  (				__lowercase	):
 for i in range(0						,	__lowercase	):
  for _ in range(0						,	n - i - 1	):  # printing spaces
   print(' '						,	end=''	)
  for _ in range(0						,	i + 1	):  # printing stars
   print('* '						,	end=''	)
  print()




def       A_  (				__lowercase	):
 for i in range(__lowercase						,	0						,	-1	):
  for _ in range(__lowercase						,	0						,	-1	):  # printing stars
   print('* '						,	end=''	)
  print()
  for _ in range(n - i + 1						,	0						,	-1	):  # printing spaces
   print(' '						,	end=''	)




def       A_  (				__lowercase	):
 if n <= 0:
  print('       ...       ....        nothing printing :('	)
  return
 floyd(__lowercase	)  # upper half
 reverse_floyd(__lowercase	)  # lower half


if __name__ == "__main__":
 print(r'| /\ | |- |  |-  |--| |\  /| |-')
 print(r'|/  \| |- |_ |_  |__| | \/ | |_')
 __SCREAMING_SNAKE_CASE							=     1
 while K:
  __SCREAMING_SNAKE_CASE							=     int(input('enter the number and , and see the magic : '))
  print()
  pretty_print(user_number)
  __SCREAMING_SNAKE_CASE							=     int(input('press 0 to exit... and 1 to continue...'))

 print('Good Bye...')


"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE							=     {
    'configuration_mctct': ['MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MCTCTConfig'],
    'feature_extraction_mctct': ['MCTCTFeatureExtractor'],
    'processing_mctct': ['MCTCTProcessor'],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MCTCTForCTC',
     'MCTCTModel',
     'MCTCTPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

 __SCREAMING_SNAKE_CASE							=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
