




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     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		['''image_processor''']
 UpperCamelCase		  =		'''SamImageProcessor'''

 def __init__(							self   :    Optional[int]		,				A_   :    int    )  ->      Optional[Any]:

       """simple docstring"""






       super().__init__(A_    )
       lowerCamelCase_							=       self.image_processor
       lowerCamelCase_							=       -10
       lowerCamelCase_							=       self.image_processor.size['longest_edge']

 def __call__(							self   :    Any		,				A_   :    List[str]=None		,				A_   :    List[Any]=None		,				A_   :    int=None		,				A_   :    List[Any]=None		,				A_   :    Optional[Union[str, TensorType]] = None		,				**A_   :    Union[str, Any]		,				)  ->      BatchEncoding:

       """simple docstring"""






       lowerCamelCase_							=       self.image_processor(
           A_		,				return_tensors=A_		,				**A_		,				)

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

       if hasattr(A_		,				'numpy'    ):  # Checks if Torch or TF tensor
             lowerCamelCase_							=       original_sizes.numpy()

       lowerCamelCase_	,							lowerCamelCase_	,							lowerCamelCase_							=       self._check_and_preprocess_points(
           input_points=A_		,				input_labels=A_		,				input_boxes=A_		,				)

       lowerCamelCase_							=       self._normalize_and_convert(
           A_		,				A_		,				input_points=A_		,				input_labels=A_		,				input_boxes=A_		,				return_tensors=A_		,				)

       return encoding_image_processor

 def 							a__					(							self   :    Dict		,				A_   :    int		,				A_   :    Any		,				A_   :    str=None		,				A_   :    Union[str, Any]=None		,				A_   :    List[Any]=None		,				A_   :    Union[str, Any]="pt"		,				)  ->      Any:

       """simple docstring"""






       if input_points is not None:
             if len(A_    ) != len(A_    ):
                   lowerCamelCase_							=       [
                       self._normalize_coordinates(self.target_size		,				A_		,				original_sizes[0]    ) for point in input_points
                   ]
             else:
                   lowerCamelCase_							=       [
                       self._normalize_coordinates(self.target_size		,				A_		,				A_    )
                       for point, original_size in zip(A_		,				A_    )
                   ]
             # 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:
                         lowerCamelCase_	,							lowerCamelCase_							=       self._pad_points_and_labels(A_		,				A_    )

             lowerCamelCase_							=       np.array(A_    )

       if input_labels is not None:
             lowerCamelCase_							=       np.array(A_    )

       if input_boxes is not None:
             if len(A_    ) != len(A_    ):
                   lowerCamelCase_							=       [
                       self._normalize_coordinates(self.target_size		,				A_		,				original_sizes[0]		,				is_bounding_box=A_    )
                       for box in input_boxes
                   ]
             else:
                   lowerCamelCase_							=       [
                       self._normalize_coordinates(self.target_size		,				A_		,				A_		,				is_bounding_box=A_    )
                       for box, original_size in zip(A_		,				A_    )
                   ]
             lowerCamelCase_							=       np.array(A_    )

       if input_boxes is not None:
             if return_tensors == "pt":
                   lowerCamelCase_							=       torch.from_numpy(A_    )
                   # boxes batch size of 1 by default
                   lowerCamelCase_							=       input_boxes.unsqueeze(1    ) if len(input_boxes.shape    ) != 3 else input_boxes
             elif return_tensors == "tf":
                   lowerCamelCase_							=       tf.convert_to_tensor(A_    )
                   # boxes batch size of 1 by default
                   lowerCamelCase_							=       tf.expand_dims(A_		,				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":
                   lowerCamelCase_							=       torch.from_numpy(A_    )
                   # point batch size of 1 by default
                   lowerCamelCase_							=       input_points.unsqueeze(1    ) if len(input_points.shape    ) != 4 else input_points
             elif return_tensors == "tf":
                   lowerCamelCase_							=       tf.convert_to_tensor(A_    )
                   # point batch size of 1 by default
                   lowerCamelCase_							=       tf.expand_dims(A_		,				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":
                   lowerCamelCase_							=       torch.from_numpy(A_    )
                   # point batch size of 1 by default
                   lowerCamelCase_							=       input_labels.unsqueeze(1    ) if len(input_labels.shape    ) != 3 else input_labels
             elif return_tensors == "tf":
                   lowerCamelCase_							=       tf.convert_to_tensor(A_    )
                   # point batch size of 1 by default
                   lowerCamelCase_							=       tf.expand_dims(A_		,				1    ) if len(input_labels.shape    ) != 3 else input_labels
             encoding_image_processor.update({'input_labels': input_labels}    )

       return encoding_image_processor

 def 							a__					(							self   :    Tuple		,				A_   :    int		,				A_   :    str    )  ->      Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=       max([point.shape[0] for point in input_points]    )
       lowerCamelCase_							=       []
       for i, point in enumerate(A_    ):
             if point.shape[0] != expected_nb_points:
                   lowerCamelCase_							=       np.concatenate(
                       [point, np.zeros((expected_nb_points - point.shape[0], 2)    ) + self.point_pad_value]		,				axis=0    )
                   lowerCamelCase_							=       np.append(input_labels[i]		,				[self.point_pad_value]    )
             processed_input_points.append(A_    )
       lowerCamelCase_							=       processed_input_points
       return input_points, input_labels

 def 							a__					(							self   :    Optional[Any]		,				A_   :    int		,				A_   :    np.ndarray		,				A_   :    Union[str, Any]		,				A_   :    Any=False    )  ->      np.ndarray:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       original_size
       lowerCamelCase_	,							lowerCamelCase_							=       self.image_processor._get_preprocess_shape(A_		,				longest_edge=A_    )
       lowerCamelCase_							=       deepcopy(A_    ).astype(A_    )

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

       lowerCamelCase_							=       coords[..., 0] * (new_w / old_w)
       lowerCamelCase_							=       coords[..., 1] * (new_h / old_h)

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

       return coords

 def 							a__					(							self   :    int		,				A_   :    str=None		,				A_   :    Optional[Any]=None		,				A_   :    str=None		,				)  ->      List[str]:

       """simple docstring"""






       if input_points is not None:
             if hasattr(A_		,				'numpy'    ):  # Checks for TF or Torch tensor
                   lowerCamelCase_							=       input_points.numpy().tolist()

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

       if input_labels is not None:
             if hasattr(A_		,				'numpy'    ):
                   lowerCamelCase_							=       input_labels.numpy().tolist()

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

       if input_boxes is not None:
             if hasattr(A_		,				'numpy'    ):
                   lowerCamelCase_							=       input_boxes.numpy().tolist()

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

       return input_points, input_labels, input_boxes

 @property
 def 							a__					(							self   :    List[str]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.image_processor.model_input_names
       return list(dict.fromkeys(A_    )    )





 def 							a__					(							self   :    List[Any]		,				*A_   :    Optional[Any]		,				**A_   :    Optional[Any]    )  ->      Optional[int]:

       """simple docstring"""






       return self.image_processor.post_process_masks(*A_		,				**A_    )










import requests

a__      :     Any         =		'YOUR API KEY'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str = giphy_api_key				)	->				list:
     '''simple docstring'''


     A_          =       '''+'''.join(query.split()				)
     A_          =       f'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
     A_          =       requests.get(_UpperCAmelCase				).json()['''data''']
     return [gif["url"] for gif in gifs]


if __name__ == "__main__":
      print('\n'.join(get_gifs('space ship')))



