





'''simple docstring'''






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer




class  _A (					tf.keras.layers.Layer		):

		def __init__(   self					:   Dict  ,      __magic_name__					:   Dict[str, int]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   int = None  ,      __magic_name__					:   int = None			)					->    Optional[Any]:


					"""simple docstring"""

					super().__init__()
					__snake_case					:      str							      =     pad_token_id
					__snake_case					:      Tuple							      =     max_length
					__snake_case					:      Dict							      =     vocab
					__snake_case					:      Optional[Any]							      =     merges
					__snake_case					:      Optional[Any]							      =     BytePairTokenizer(__magic_name__  ,      __magic_name__  ,      sequence_length=__magic_name__			)

		@classmethod
		def 	lowercase__     (   cls					:   int  ,      __magic_name__					:   GPTaTokenizer  ,      *__magic_name__					:   int  ,      **__magic_name__					:   int			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [""" """.join(__magic_name__			) for m in tokenizer.bpe_ranks.keys()]
					__snake_case					:      List[str]							      =     tokenizer.get_vocab()
					return cls(__magic_name__  ,      __magic_name__  ,      *__magic_name__  ,      **__magic_name__			)

		@classmethod
		def 	lowercase__     (   cls					:   int  ,      __magic_name__					:   Union[str, os.PathLike]  ,      *__magic_name__					:   Dict  ,      **__magic_name__					:   str			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     GPTaTokenizer.from_pretrained(__magic_name__  ,      *__magic_name__  ,      **__magic_name__			)
					return cls.from_tokenizer(__magic_name__  ,      *__magic_name__  ,      **__magic_name__			)

		@classmethod
		def 	lowercase__     (   cls					:   Any  ,      __magic_name__					:   Dict			)					->    List[Any]:


					"""simple docstring"""

					return cls(**__magic_name__			)

		def 	lowercase__     (   self					:   Optional[Any]			)					->    str:


					"""simple docstring"""

					return {
					    "vocab": self.vocab,
					    "merges": self.merges,
					    "max_length": self.max_length,
					    "pad_token_id": self.pad_token_id,
					}

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   int = None			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tf_tokenizer(__magic_name__			)
					__snake_case					:      Any							      =     tf.ones_like(__magic_name__			)

					if self.pad_token_id is not None:
								# pad the tokens up to max length
								__snake_case					:      Dict							      =     max_length if max_length is not None else self.max_length

								if max_length is not None:
											__snake_case      ,     __snake_case					:      Any							      =     pad_model_inputs(
											    __magic_name__  ,      max_seq_length=__magic_name__  ,      pad_value=self.pad_token_id			)

					return {"attention_mask": attention_mask, "input_ids": input_ids}









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')))



