


from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
     import torch

if is_tf_available():
     import tensorflow as tf







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ['image_processor']
 SCREAMING_SNAKE_CASE__      =       'SamImageProcessor'




 def __init__(		self					,  _lowerCamelCase    ):
       super().__init__(_lowerCamelCase    )
       a						:Dict 							=       self.image_processor
       a						:str 							=       -10
       a						:List[str] 							=       self.image_processor.size['''longest_edge''']




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       a						:List[Any] 							=       self.image_processor(
           _lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase					,  )

       # pop arguments that are not used in the foward but used nevertheless
       a						:Any 							=       encoding_image_processor['''original_sizes''']

       if hasattr(_lowerCamelCase					,  '''numpy'''    ):  # Checks if Torch or TF tensor
             a						:Union[str, Any] 							=       original_sizes.numpy()

       a							,				a							,				a						:Optional[Any] 							=       self._check_and_preprocess_points(
           input_points=_lowerCamelCase					,  input_labels=_lowerCamelCase					,  input_boxes=_lowerCamelCase					,  )

       a						:Optional[Any] 							=       self._normalize_and_convert(
           _lowerCamelCase					,  _lowerCamelCase					,  input_points=_lowerCamelCase					,  input_labels=_lowerCamelCase					,  input_boxes=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  )

       return encoding_image_processor




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase="pt"					,  ):
       if input_points is not None:
             if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
                   a						:Tuple 							=       [
                       self._normalize_coordinates(self.target_size					,  _lowerCamelCase					,  original_sizes[0]    ) for point in input_points
                   ]
             else:
                   a						:str 							=       [
                       self._normalize_coordinates(self.target_size					,  _lowerCamelCase					,  _lowerCamelCase    )
                       for point, original_size in zip(_lowerCamelCase					,  _lowerCamelCase    )
                   ]
             # check that all arrays have the same shape
             if not all(point.shape == input_points[0].shape for point in input_points    ):
                   if input_labels is not None:
                         a							,				a						:Tuple 							=       self._pad_points_and_labels(_lowerCamelCase					,  _lowerCamelCase    )

             a						:List[str] 							=       np.array(_lowerCamelCase    )

       if input_labels is not None:
             a						:Tuple 							=       np.array(_lowerCamelCase    )

       if input_boxes is not None:
             if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
                   a						:Dict 							=       [
                       self._normalize_coordinates(self.target_size					,  _lowerCamelCase					,  original_sizes[0]					,  is_bounding_box=_lowerCamelCase    )
                       for box in input_boxes
                   ]
             else:
                   a						:str 							=       [
                       self._normalize_coordinates(self.target_size					,  _lowerCamelCase					,  _lowerCamelCase					,  is_bounding_box=_lowerCamelCase    )
                       for box, original_size in zip(_lowerCamelCase					,  _lowerCamelCase    )
                   ]
             a						:Union[str, Any] 							=       np.array(_lowerCamelCase    )

       if input_boxes is not None:
             if return_tensors == "pt":
                   a						:Optional[Any] 							=       torch.from_numpy(_lowerCamelCase    )
                   # boxes batch size of 1 by default
                   a						:Optional[Any] 							=       input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
             elif return_tensors == "tf":
                   a						:Dict 							=       tf.convert_to_tensor(_lowerCamelCase    )
                   # boxes batch size of 1 by default
                   a						:Tuple 							=       tf.expand_dims(_lowerCamelCase					,  1    ) if len(input_boxes.shape    ) != 3 else input_boxes
             encoding_image_processor.update({'''input_boxes''': input_boxes}    )
       if input_points is not None:
             if return_tensors == "pt":
                   a						:List[Any] 							=       torch.from_numpy(_lowerCamelCase    )
                   # point batch size of 1 by default
                   a						:Any 							=       input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
             elif return_tensors == "tf":
                   a						:Tuple 							=       tf.convert_to_tensor(_lowerCamelCase    )
                   # point batch size of 1 by default
                   a						:int 							=       tf.expand_dims(_lowerCamelCase					,  1    ) if len(input_points.shape    ) != 4 else input_points
             encoding_image_processor.update({'''input_points''': input_points}    )
       if input_labels is not None:
             if return_tensors == "pt":
                   a						:Dict 							=       torch.from_numpy(_lowerCamelCase    )
                   # point batch size of 1 by default
                   a						:int 							=       input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
             elif return_tensors == "tf":
                   a						:Union[str, Any] 							=       tf.convert_to_tensor(_lowerCamelCase    )
                   # point batch size of 1 by default
                   a						:Dict 							=       tf.expand_dims(_lowerCamelCase					,  1    ) if len(input_labels.shape    ) != 3 else input_labels
             encoding_image_processor.update({'''input_labels''': input_labels}    )

       return encoding_image_processor




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:List[Any] 							=       max([point.shape[0] for point in input_points]    )
       a						:Union[str, Any] 							=       []
       for i, point in enumerate(_lowerCamelCase    ):
             if point.shape[0] != expected_nb_points:
                   a						:int 							=       np.concatenate(
                       [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]					,  axis=0    )
                   a						:Union[str, Any] 							=       np.append(input_labels[i]					,  [self.point_pad_value]    )
             processed_input_points.append(_lowerCamelCase    )
       a						:Any 							=       processed_input_points
       return input_points, input_labels




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase=False    ):
       a							,				a						:str 							=       original_size
       a							,				a						:Optional[Any] 							=       self.image_processor._get_preprocess_shape(_lowerCamelCase					,  longest_edge=_lowerCamelCase    )
       a						:List[Any] 							=       deepcopy(_lowerCamelCase    ).astype(_lowerCamelCase    )

       if is_bounding_box:
             a						:Dict 							=       coords.reshape(-1					,  2					,  2    )

       a						:Tuple 							=       coords[..., 0] * (new_w / old_w)
       a						:Dict 							=       coords[..., 1] * (new_h / old_h)

       if is_bounding_box:
             a						:Any 							=       coords.reshape(-1					,  4    )

       return coords




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  _lowerCamelCase=None					,  ):
       if input_points is not None:
             if hasattr(_lowerCamelCase					,  '''numpy'''    ):  # Checks for TF or Torch tensor
                   a						:int 							=       input_points.numpy().tolist()

             if not isinstance(_lowerCamelCase					,  _lowerCamelCase    ) or not isinstance(input_points[0]					,  _lowerCamelCase    ):
                   raise ValueError('''Input points must be a list of list of floating points.'''    )
             a						:Dict 							=       [np.array(_lowerCamelCase    ) for input_point in input_points]
       else:
             a						:List[str] 							=       None

       if input_labels is not None:
             if hasattr(_lowerCamelCase					,  '''numpy'''    ):
                   a						:Optional[int] 							=       input_labels.numpy().tolist()

             if not isinstance(_lowerCamelCase					,  _lowerCamelCase    ) or not isinstance(input_labels[0]					,  _lowerCamelCase    ):
                   raise ValueError('''Input labels must be a list of list integers.'''    )
             a						:List[Any] 							=       [np.array(_lowerCamelCase    ) for label in input_labels]
       else:
             a						:Optional[Any] 							=       None

       if input_boxes is not None:
             if hasattr(_lowerCamelCase					,  '''numpy'''    ):
                   a						:int 							=       input_boxes.numpy().tolist()

             if (
                 not isinstance(_lowerCamelCase					,  _lowerCamelCase    )
                 or not isinstance(input_boxes[0]					,  _lowerCamelCase    )
                 or not isinstance(input_boxes[0][0]					,  _lowerCamelCase    )
             ):
                   raise ValueError('''Input boxes must be a list of list of list of floating points.'''    )
             a						:Optional[int] 							=       [np.array(_lowerCamelCase    ).astype(np.floataa    ) for box in input_boxes]
       else:
             a						:Optional[int] 							=       None

       return input_points, input_labels, input_boxes




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.image_processor.model_input_names
       return list(dict.fromkeys(_lowerCamelCase    )    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.image_processor.post_process_masks(*_lowerCamelCase					,  **_lowerCamelCase    )









from __future__ import annotations

import collections
import pprint
from pathlib import Path
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return "".join(sorted(UpperCAmelCase_						)						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return word_by_signature[signature(UpperCAmelCase_						)]


snake_case				:    str       				=     Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
snake_case				:    Optional[int]       				=     sorted({word.strip().lower() for word in data.splitlines()})

snake_case				:    str       				=     collections.defaultdict(list)
for word in word_list:
     word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
     snake_case				:    Optional[int]       				=     {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

     with open('''anagrams.txt''', '''w''') as file:
          file.write('''all_anagrams = \n ''')
          file.write(pprint.pformat(all_anagrams))





