

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    str:
												_a	:    str         =		tempfile.mkdtemp()

												_a	:    Optional[int]         =		BlipImageProcessor()
												_a	:    Tuple         =		GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''       )

												_a	:    Optional[int]         =		BlipaProcessor(_a       ,							_a       )

												processor.save_pretrained(self.tmpdirname       )


							def 				__lowercase				(			self       ,							**_a       )    ->    Dict:
												return AutoProcessor.from_pretrained(self.tmpdirname       ,							**_a       ).tokenizer


							def 				__lowercase				(			self       ,							**_a       )    ->    Optional[int]:
												return AutoProcessor.from_pretrained(self.tmpdirname       ,							**_a       ).image_processor


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												shutil.rmtree(self.tmpdirname       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		[np.random.randint(2_5_5       ,							size=(3, 3_0, 4_0_0)       ,							dtype=np.uinta       )]

												_a	:    List[str]         =		[Image.fromarray(np.moveaxis(_a       ,							0       ,							-1       )       ) for x in image_inputs]

												return image_inputs


							def 				__lowercase				(			self       )    ->    List[str]:
												_a	:    int         =		BlipaProcessor(tokenizer=self.get_tokenizer()       ,							image_processor=self.get_image_processor()       )
												processor.save_pretrained(self.tmpdirname       )

												_a	:    Any         =		self.get_tokenizer(bos_token='''(BOS)'''       ,							eos_token='''(EOS)'''       )
												_a	:    List[str]         =		self.get_image_processor(do_normalize=_a       ,							padding_value=1.0       )

												_a	:    Optional[Any]         =		BlipaProcessor.from_pretrained(
												    self.tmpdirname       ,							bos_token='''(BOS)'''       ,							eos_token='''(EOS)'''       ,							do_normalize=_a       ,							padding_value=1.0       )

												self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer_add_kwargs.get_vocab()       )
												self.assertIsInstance(processor.tokenizer       ,							_a       )

												self.assertEqual(processor.image_processor.to_json_string()       ,							image_processor_add_kwargs.to_json_string()       )
												self.assertIsInstance(processor.image_processor       ,							_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Union[str, Any]         =		self.get_image_processor()
												_a	:    Optional[int]         =		self.get_tokenizer()

												_a	:    Tuple         =		BlipaProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    List[str]         =		self.prepare_image_inputs()

												_a	:    Tuple         =		image_processor(_a       ,							return_tensors='''np'''       )
												_a	:    Optional[Any]         =		processor(images=_a       ,							return_tensors='''np'''       )

												for key in input_feat_extract.keys():
																	self.assertAlmostEqual(input_feat_extract[key].sum()       ,							input_processor[key].sum()       ,							delta=1e-2       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Optional[Any]         =		self.get_image_processor()
												_a	:    Union[str, Any]         =		self.get_tokenizer()

												_a	:    Any         =		BlipaProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Optional[Any]         =		'''lower newer'''

												_a	:    int         =		processor(text=_a       )

												_a	:    List[str]         =		tokenizer(_a       ,							return_token_type_ids=_a       )

												for key in encoded_tok.keys():
																	self.assertListEqual(encoded_tok[key]       ,							encoded_processor[key]       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Optional[int]         =		self.get_image_processor()
												_a	:    List[Any]         =		self.get_tokenizer()

												_a	:    Union[str, Any]         =		BlipaProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Any         =		'''lower newer'''
												_a	:    Any         =		self.prepare_image_inputs()

												_a	:    Optional[int]         =		processor(text=_a       ,							images=_a       )

												self.assertListEqual(list(inputs.keys()       )       ,							['''pixel_values''', '''input_ids''', '''attention_mask''']       )

												# test if it raises when no input is passed
												with pytest.raises(_a       ):
																	processor()


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[Any]         =		self.get_image_processor()
												_a	:    Union[str, Any]         =		self.get_tokenizer()

												_a	:    Any         =		BlipaProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Any         =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												_a	:    Union[str, Any]         =		processor.batch_decode(_a       )
												_a	:    List[str]         =		tokenizer.batch_decode(_a       )

												self.assertListEqual(_a       ,							_a       )




							def 				__lowercase				(			self       )    ->    int:
												_a	:    Optional[Any]         =		self.get_image_processor()
												_a	:    Dict         =		self.get_tokenizer()

												_a	:    Optional[Any]         =		BlipaProcessor(tokenizer=_a       ,							image_processor=_a       )

												_a	:    Tuple         =		'''lower newer'''
												_a	:    Any         =		self.prepare_image_inputs()

												_a	:    int         =		processor(text=_a       ,							images=_a       )

												# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
												self.assertListEqual(list(inputs.keys()       )       ,							['''pixel_values''', '''input_ids''', '''attention_mask''']       )






import requests

a__				=		'''YOUR API KEY'''



def    __UpperCAmelCase (	__a      :			str		,__a      :			str = giphy_api_key	)			->     list:

					"""simple docstring"""

					_a	:    Optional[Any]         =		'''+'''.join(query.split()	)
					_a	:    Union[str, Any]         =		F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
					_a	:    List[Any]         =		requests.get(__a	).json()['''data''']
					return [gif["url"] for gif in gifs]


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



