


"""simple docstring"""






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


UpperCamelCase__				:List[str]			   =	logging.get_logger(__name__)

UpperCamelCase__				:str			   =	{
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"transfo-xl"
		A    =							["mems"]
		A    =							{
		    "n_token": "vocab_size",
		    "hidden_size": "d_model",
		    "num_attention_heads": "n_head",
		    "num_hidden_layers": "n_layer",
		}
		def __init__( self					,				SCREAMING_SNAKE_CASE__=26_77_35					,				SCREAMING_SNAKE_CASE__=[2_00_00, 4_00_00, 20_00_00]					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=64					,				SCREAMING_SNAKE_CASE__=40_96					,				SCREAMING_SNAKE_CASE__=4					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=18					,				SCREAMING_SNAKE_CASE__=16_00					,				SCREAMING_SNAKE_CASE__=10_00					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=-1					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__="normal"					,				SCREAMING_SNAKE_CASE__=0.0_1					,				SCREAMING_SNAKE_CASE__=0.0_1					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=1E-5					,				SCREAMING_SNAKE_CASE__=0					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	vocab_size
					_UpperCamelCase					:Optional[int]					     =	[]
					self.cutoffs.extend(SCREAMING_SNAKE_CASE__   )
					if proj_share_all_but_first:
								_UpperCamelCase					:Tuple					     =	[False] + [True] * len(self.cutoffs   )
					else:
								_UpperCamelCase					:Optional[Any]					     =	[False] + [False] * len(self.cutoffs   )
					_UpperCamelCase					:Tuple					     =	d_model
					_UpperCamelCase					:int					     =	d_embed
					_UpperCamelCase					:List[str]					     =	d_head
					_UpperCamelCase					:Dict					     =	d_inner
					_UpperCamelCase					:List[str]					     =	div_val
					_UpperCamelCase					:Dict					     =	pre_lnorm
					_UpperCamelCase					:Tuple					     =	n_layer
					_UpperCamelCase					:List[str]					     =	n_head
					_UpperCamelCase					:Any					     =	mem_len
					_UpperCamelCase					:Optional[int]					     =	same_length
					_UpperCamelCase					:Optional[Any]					     =	attn_type
					_UpperCamelCase					:List[Any]					     =	clamp_len
					_UpperCamelCase					:Dict					     =	sample_softmax
					_UpperCamelCase					:Any					     =	adaptive
					_UpperCamelCase					:Tuple					     =	dropout
					_UpperCamelCase					:Optional[int]					     =	dropatt
					_UpperCamelCase					:Union[str, Any]					     =	untie_r
					_UpperCamelCase					:int					     =	init
					_UpperCamelCase					:Dict					     =	init_range
					_UpperCamelCase					:Dict					     =	proj_init_std
					_UpperCamelCase					:List[str]					     =	init_std
					_UpperCamelCase					:Dict					     =	layer_norm_epsilon
					super().__init__(eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."   )
					return -1





		@max_position_embeddings.setter
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					raise NotImplementedError(
					    f"The model {self.model_type} is one of the few models that has no sequence length limit."   )





"""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)
UpperCamelCase__				:str			   =	logging.getLogger()





def 				A_       ( snake_case__       ,					snake_case__		)					->  Optional[Any]:
			_UpperCamelCase					:Optional[int]					     =	'''\n'''.join(snake_case__		)
			Path(snake_case__		).open('''w'''		).writelines(snake_case__		)


UpperCamelCase__				:Dict			   =	"""patrickvonplaten/t5-tiny-random"""
UpperCamelCase__				:List[Any]			   =	"""sshleifer/bart-tiny-random"""
UpperCamelCase__				:str			   =	"""sshleifer/tiny-mbart"""

UpperCamelCase__				:Any			   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



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

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

					with patch.object(SCREAMING_SNAKE_CASE__					,				'''argv'''					,				SCREAMING_SNAKE_CASE__   ):
								run_generate()
								assert Path(SCREAMING_SNAKE_CASE__   ).exists()
								# os.remove(Path(output_file_name))
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )
		@parameterized.expand([BART_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					self.run_eval_tester(SCREAMING_SNAKE_CASE__   )





		@parameterized.expand([T5_TINY, MBART_TINY]   )
		@slow
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



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

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''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!''',
					    ],
					}

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

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

