
"""simple docstring"""




from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :					Any							=   logging.get_logger(__name__)

A__   :					Optional[Any]							=   {
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''efficientformer'''
       def __init__(     self ,				A_ = [3, 2, 6, 4] ,				A_ = [48, 96, 224, 448] ,				A_ = [True, True, True, True] ,				A_ = 448 ,				A_ = 32 ,				A_ = 4 ,				A_ = 7 ,				A_ = 5 ,				A_ = 8 ,				A_ = 4 ,				A_ = 0.0 ,				A_ = 16 ,				A_ = 3 ,				A_ = 3 ,				A_ = 3 ,				A_ = 2 ,				A_ = 1 ,				A_ = 0.0 ,				A_ = 1 ,				A_ = True ,				A_ = True ,				A_ = 1E-5 ,				A_ = "gelu" ,				A_ = 0.02 ,				A_ = 1E-12 ,				A_ = 224 ,				A_ = 1E-05 ,				**A_ ,				)     ->	None:





         """simple docstring"""




         super().__init__(**A_       )

         _lowercase:			Optional[int]       =     hidden_act
         _lowercase:			int       =     hidden_dropout_prob
         _lowercase:			List[str]       =     hidden_sizes
         _lowercase:			str       =     num_hidden_layers
         _lowercase:			Tuple       =     num_attention_heads
         _lowercase:			List[Any]       =     initializer_range
         _lowercase:			List[str]       =     layer_norm_eps
         _lowercase:			Union[str, Any]       =     patch_size
         _lowercase:			Any       =     num_channels
         _lowercase:			List[str]       =     depths
         _lowercase:			Tuple       =     mlp_expansion_ratio
         _lowercase:			List[str]       =     downsamples
         _lowercase:			str       =     dim
         _lowercase:			Optional[Any]       =     key_dim
         _lowercase:			Any       =     attention_ratio
         _lowercase:			Tuple       =     resolution
         _lowercase:			Tuple       =     pool_size
         _lowercase:			Optional[int]       =     downsample_patch_size
         _lowercase:			Any       =     downsample_stride
         _lowercase:			int       =     downsample_pad
         _lowercase:			Dict       =     drop_path_rate
         _lowercase:			str       =     num_metaad_blocks
         _lowercase:			Any       =     distillation
         _lowercase:			List[Any]       =     use_layer_scale
         _lowercase:			Any       =     layer_scale_init_value
         _lowercase:			Optional[int]       =     image_size
         _lowercase:			Dict       =     batch_norm_eps







"""simple docstring"""




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
A__   :					Any							=   logging.getLogger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     '''\n'''.join(_UpperCamelCase						)
  Path(_UpperCamelCase						).open('''w'''						).writelines(_UpperCamelCase						)


A__   :					int							=   'patrickvonplaten/t5-tiny-random'
A__   :					Tuple							=   'sshleifer/bart-tiny-random'
A__   :					Optional[Any]							=   'sshleifer/tiny-mbart'

A__   :					Optional[int]							=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			str       =     Path(self.get_auto_remove_tmp_dir()       ) / '''utest_input.source'''
         _lowercase:			Tuple       =     input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()
         _lowercase:			List[str]       =     [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
         _dump_articles(A_ ,				A_       )

         _lowercase:			int       =     str(Path(self.get_auto_remove_tmp_dir()       ) / '''scores.json'''       )
         _lowercase:			Optional[Any]       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
         _lowercase:			str       =     f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

         with patch.object(A_ ,				'''argv''' ,				A_       ):
           run_generate()
           assert Path(A_       ).exists()
           # os.remove(Path(output_file_name))
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         self.run_eval_tester(A_       )
       @parameterized.expand([BART_TINY, MBART_TINY]       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




         self.run_eval_tester(A_       )



       @parameterized.expand([T5_TINY, MBART_TINY]       )
       @slow
       def 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     Path(self.get_auto_remove_tmp_dir()       ) / '''utest_input.source'''
         _lowercase:			Dict       =     input_file_name.parent / '''utest_output.txt'''
         assert not output_file_name.exists()

         _lowercase:			int       =     {
             '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
             '''de''': [
                 '''Maschinelles Lernen ist großartig, oder?''',
                 '''Ich esse gerne Bananen''',
                 '''Morgen ist wieder ein toller Tag!''',
             ],
         }

         _lowercase:			Tuple       =     Path(self.get_auto_remove_tmp_dir()       )
         _lowercase:			Tuple       =     str(tmp_dir / '''scores.json'''       )
         _lowercase:			List[str]       =     str(tmp_dir / '''val.target'''       )
         _dump_articles(A_ ,				text['''en''']       )
         _dump_articles(A_ ,				text['''de''']       )
         _lowercase:			Tuple       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
         _lowercase:			Tuple       =     f'''
            run_eval_search.py
            {model}
            {str(A_       )}
            {str(A_       )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
         testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0''']       )

         with patch.object(A_ ,				'''argv''' ,				A_       ):
           with CaptureStdout() as cs:
             run_search()
           _lowercase:			List[str]       =     [''' num_beams | length_penalty''', model, '''Best score args''']
           _lowercase:			Union[str, Any]       =     ['''Info''']
           if "translation" in task:
             expected_strings.append('''bleu'''       )
           else:
             expected_strings.extend(A_       )
           for w in expected_strings:
             assert w in cs.out
           for w in un_expected_strings:
             assert w not in cs.out
           assert Path(A_       ).exists()
           os.remove(Path(A_       )       )
