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from typing import List, Optional
import os
from DeepFakeAI.filesystem import is_file, is_directory
from DeepFakeAI.typing import Padding
def normalize_output_path(source_paths : List[str], target_path : str, output_path : str) -> Optional[str]:
if is_file(target_path) and is_directory(output_path):
target_name, target_extension = os.path.splitext(os.path.basename(target_path))
if source_paths and is_file(source_paths[0]):
source_name, _ = os.path.splitext(os.path.basename(source_paths[0]))
return os.path.join(output_path, source_name + '-' + target_name + target_extension)
return os.path.join(output_path, target_name + target_extension)
if is_file(target_path) and output_path:
_, target_extension = os.path.splitext(os.path.basename(target_path))
output_name, output_extension = os.path.splitext(os.path.basename(output_path))
output_directory_path = os.path.dirname(output_path)
if is_directory(output_directory_path) and output_extension:
return os.path.join(output_directory_path, output_name + target_extension)
return None
return output_path
def normalize_padding(padding : Optional[List[int]]) -> Optional[Padding]:
if padding and len(padding) == 1:
return tuple([ padding[0], padding[0], padding[0], padding[0] ]) # type: ignore[return-value]
if padding and len(padding) == 2:
return tuple([ padding[0], padding[1], padding[0], padding[1] ]) # type: ignore[return-value]
if padding and len(padding) == 3:
return tuple([ padding[0], padding[1], padding[2], padding[1] ]) # type: ignore[return-value]
if padding and len(padding) == 4:
return tuple(padding) # type: ignore[return-value]
return None
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