


import warnings
from typing import List

import numpy as np

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




class  UpperCAmelCase       (							_SCREAMING_SNAKE_CASE     ):
			lowercase						       =			["""image_processor""", """tokenizer"""]
			lowercase						       =			"""OwlViTImageProcessor"""
			lowercase						       =			("""CLIPTokenizer""", """CLIPTokenizerFast""")




			def __init__(    self				:		Tuple							,							__magic_name__				:		List[Any]=None							,							__magic_name__				:		int=None							,							**__magic_name__				:		Any    ):







						"""simple docstring"""







						UpperCamelCase								=       None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
									    """ instead."""							,							__snake_case							,							)
									UpperCamelCase								=       kwargs.pop("""feature_extractor"""    )

						UpperCamelCase								=       image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("""You need to specify an `image_processor`."""    )
						if tokenizer is None:
									raise ValueError("""You need to specify a `tokenizer`."""    )

						super().__init__(__snake_case							,							__snake_case    )




			def __call__(    self				:		Union[str, Any]							,							__magic_name__				:		Tuple=None							,							__magic_name__				:		Optional[int]=None							,							__magic_name__				:		str=None							,							__magic_name__				:		Optional[int]="max_length"							,							__magic_name__				:		Tuple="np"							,							**__magic_name__				:		str    ):







						"""simple docstring"""







						if text is None and query_images is None and images is None:
									raise ValueError(
									    """You have to specify at least one text or query image or image. All three cannot be none."""    )

						if text is not None:
									if isinstance(__snake_case							,							__snake_case    ) or (isinstance(__snake_case							,							__snake_case    ) and not isinstance(text[0]							,							__snake_case    )):
												UpperCamelCase								=       [self.tokenizer(__snake_case							,							padding=__snake_case							,							return_tensors=__snake_case							,							**__snake_case    )]

									elif isinstance(__snake_case							,							__snake_case    ) and isinstance(text[0]							,							__snake_case    ):
												UpperCamelCase								=       []

												# Maximum number of queries across batch
												UpperCamelCase								=       max([len(__snake_case    ) for t in text]    )

												# Pad all batch samples to max number of text queries
												for t in text:
															if len(__snake_case    ) != max_num_queries:
																		UpperCamelCase								=       t + [""" """] * (max_num_queries - len(__snake_case    ))

															UpperCamelCase								=       self.tokenizer(__snake_case							,							padding=__snake_case							,							return_tensors=__snake_case							,							**__snake_case    )
															encodings.append(__snake_case    )
									else:
												raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""    )

									if return_tensors == "np":
												UpperCamelCase								=       np.concatenate([encoding["""input_ids"""] for encoding in encodings]							,							axis=0    )
												UpperCamelCase								=       np.concatenate([encoding["""attention_mask"""] for encoding in encodings]							,							axis=0    )

									elif return_tensors == "jax" and is_flax_available():
												import jax.numpy as jnp

												UpperCamelCase								=       jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]							,							axis=0    )
												UpperCamelCase								=       jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]							,							axis=0    )

									elif return_tensors == "pt" and is_torch_available():
												import torch

												UpperCamelCase								=       torch.cat([encoding["""input_ids"""] for encoding in encodings]							,							dim=0    )
												UpperCamelCase								=       torch.cat([encoding["""attention_mask"""] for encoding in encodings]							,							dim=0    )

									elif return_tensors == "tf" and is_tf_available():
												import tensorflow as tf

												UpperCamelCase								=       tf.stack([encoding["""input_ids"""] for encoding in encodings]							,							axis=0    )
												UpperCamelCase								=       tf.stack([encoding["""attention_mask"""] for encoding in encodings]							,							axis=0    )

									else:
												raise ValueError("""Target return tensor type could not be returned"""    )

									UpperCamelCase								=       BatchEncoding()
									UpperCamelCase								=       input_ids
									UpperCamelCase								=       attention_mask

						if query_images is not None:
									UpperCamelCase								=       BatchEncoding()
									UpperCamelCase								=       self.image_processor(
									    __snake_case							,							return_tensors=__snake_case							,							**__snake_case    ).pixel_values
									UpperCamelCase								=       query_pixel_values

						if images is not None:
									UpperCamelCase								=       self.image_processor(__snake_case							,							return_tensors=__snake_case							,							**__snake_case    )

						if text is not None and images is not None:
									UpperCamelCase								=       image_features.pixel_values
									return encoding
						elif query_images is not None and images is not None:
									UpperCamelCase								=       image_features.pixel_values
									return encoding
						elif text is not None or query_images is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**__snake_case    )							,							tensor_type=__snake_case    )




			def 	lowerCamelCase_       (    self				:		int							,							*__magic_name__				:		Tuple							,							**__magic_name__				:		List[str]    ):







						"""simple docstring"""







						return self.image_processor.post_process(*__snake_case							,							**__snake_case    )




			def 	lowerCamelCase_       (    self				:		Dict							,							*__magic_name__				:		Dict							,							**__magic_name__				:		Any    ):







						"""simple docstring"""







						return self.image_processor.post_process_object_detection(*__snake_case							,							**__snake_case    )




			def 	lowerCamelCase_       (    self				:		int							,							*__magic_name__				:		Optional[int]							,							**__magic_name__				:		Union[str, Any]    ):







						"""simple docstring"""







						return self.image_processor.post_process_image_guided_detection(*__snake_case							,							**__snake_case    )




			def 	lowerCamelCase_       (    self				:		Any							,							*__magic_name__				:		Union[str, Any]							,							**__magic_name__				:		int    ):







						"""simple docstring"""







						return self.tokenizer.batch_decode(*__snake_case							,							**__snake_case    )




			def 	lowerCamelCase_       (    self				:		int							,							*__magic_name__				:		str							,							**__magic_name__				:		List[str]    ):







						"""simple docstring"""







						return self.tokenizer.decode(*__snake_case							,							**__snake_case    )




			@property
			def 	lowerCamelCase_       (    self				:		Tuple    ):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""							,							__snake_case							,							)
						return self.image_processor_class





			@property
			def 	lowerCamelCase_       (    self				:		List[Any]    ):







						"""simple docstring"""







						warnings.warn(
						    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""							,							__snake_case							,							)
						return self.image_processor











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')))



