





'''simple docstring'''






import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
snake_case_     =	logging.getLogger()

snake_case_     =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class   _lowercase						(							a				):


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       os.makedirs(_UpperCAmelCase    ,   exist_ok=_UpperCAmelCase					)
       A       :       str							=						{'''source''': '''What is love ?''', '''target''': '''life'''}
       A       :       Dict							=						{'''train''': 12, '''val''': 2, '''test''': 2}
       for split in ["train", "test", "val"]:
           for field in ["source", "target"]:
               A       :       int							=						'''\n'''.join([contents[field]] * n_lines[split]					)
               with open(os.path.join(_UpperCAmelCase    ,   f'''{split}.{field}'''					)    ,   '''w'''					) as f:
                   f.write(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = "pytorch"					):
       A       :       str							=						self.get_auto_remove_tmp_dir()
       A       :       Optional[int]							=						os.path.join(_UpperCAmelCase    ,   '''output'''					)
       A       :       Dict							=						os.path.join(_UpperCAmelCase    ,   '''data'''					)
       self._create_dummy_data(data_dir=_UpperCAmelCase					)

       A       :       List[Any]							=						f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

       if gpus > 0:
           testargs.append(f'''--gpus={gpus}'''					)
           if is_apex_available():
               testargs.append('''--fp16'''					)
       else:
           testargs.append('''--gpus=0'''					)
           testargs.append('''--distributed_backend=ddp_cpu'''					)
           testargs.append('''--num_processes=2'''					)

       A       :       Dict							=						[sys.executable, str(Path(finetune_rag.__file__					).resolve()					)] + testargs
       execute_subprocess_async(_UpperCAmelCase    ,   env=self.get_env()					)

       A       :       Tuple							=						os.path.join(_UpperCAmelCase    ,   '''metrics.json'''					)
       with open(_UpperCAmelCase					) as f:
           A       :       Any							=						json.load(_UpperCAmelCase					)
       return result


   @require_torch_gpu
   def 					snake_case				( self					):
       A       :       Any							=						self._run_finetune(gpus=1					)
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']    ,   0.2					)


   @require_torch_multi_gpu
   def 					snake_case				( self					):
       A       :       str							=						self._run_finetune(gpus=2					)
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']    ,   0.2					)


   @require_torch_gpu
   @require_ray
   def 					snake_case				( self					):
       A       :       Optional[int]							=						self._run_finetune(gpus=1    ,   distributed_retriever='''ray'''					)
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']    ,   0.2					)


   @require_torch_multi_gpu
   @require_ray
   def 					snake_case				( self					):
       A       :       int							=						self._run_finetune(gpus=1    ,   distributed_retriever='''ray'''					)
       self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']    ,   0.2					)







'''simple docstring'''






import requests

snake_case_     =	"""YOUR API KEY"""
def 							_lowerCamelCase(  UpperCamelCase__    : str    ,       UpperCamelCase__    : str = giphy_api_key		)		->  list:
    A       :       Optional[Any]							=						'''+'''.join(query.split()		)
    A       :       List[Any]							=						F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
    A       :       int							=						requests.get(UpperCamelCase__		).json()['''data''']
    return [gif["url"] for gif in gifs]


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