



"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''umt5'''
							_UpperCAmelCase      				=					['''past_key_values''']






							def __init__(  self	,       snake_case=250112	,       snake_case=512	,       snake_case=64	,       snake_case=1024	,       snake_case=8	,       snake_case=None	,       snake_case=6	,       snake_case=32	,       snake_case=128	,       snake_case=0.1	,       snake_case=1E-6	,       snake_case=1.0	,       snake_case="gated-gelu"	,       snake_case=True	,       snake_case=True	,       snake_case="T5Tokenizer"	,       snake_case=True	,       snake_case=0	,       snake_case=1	,       snake_case=0	,       **snake_case	,       )			->							List[Any]:
									super().__init__(
									    is_encoder_decoder=snake_case	,       tokenizer_class=snake_case	,       tie_word_embeddings=snake_case	,       pad_token_id=snake_case	,       eos_token_id=snake_case	,       decoder_start_token_id=snake_case	,       **snake_case	,       )
									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		d_model
									_UpperCAmelCase							 =		d_kv
									_UpperCAmelCase							 =		d_ff
									_UpperCAmelCase							 =		num_layers
									_UpperCAmelCase							 =		(
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		relative_attention_num_buckets
									_UpperCAmelCase							 =		relative_attention_max_distance
									_UpperCAmelCase							 =		dropout_rate
									_UpperCAmelCase							 =		layer_norm_epsilon
									_UpperCAmelCase							 =		initializer_factor
									_UpperCAmelCase							 =		feed_forward_proj
									_UpperCAmelCase							 =		use_cache

									_UpperCAmelCase							 =		self.feed_forward_proj.split('-'						)
									_UpperCAmelCase							 =		act_info[-1]
									_UpperCAmelCase							 =		act_info[0] == 'gated'

									if len(snake_case						) > 1 and act_info[0] != "gated" or len(snake_case						) > 2:
											raise ValueError(
											    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''						)

									if feed_forward_proj == "gated-gelu":
											_UpperCAmelCase							 =		'gelu_new'






							@property
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									return self.d_model






							@property
							def        lowerCamelCase_   (  self						)			->							Tuple:
									return self.num_heads






							@property
							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									return self.num_layers







class 			lowercase__			(      A    ):
							'''simple docstring'''







							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									_UpperCAmelCase							 =		{
									    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
									    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
									}
									if self.use_past:
											_UpperCAmelCase							 =		'past_encoder_sequence + sequence'
											_UpperCAmelCase							 =		{0: 'batch'}
											_UpperCAmelCase							 =		{0: 'batch', 1: 'past_decoder_sequence + sequence'}
									else:
											_UpperCAmelCase							 =		{0: 'batch', 1: 'decoder_sequence'}
											_UpperCAmelCase							 =		{0: 'batch', 1: 'decoder_sequence'}

									if self.use_past:
											self.fill_with_past_key_values_(snake_case	,       direction='inputs'						)

									return common_inputs






							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
							def        lowerCamelCase_   (  self						)			->							int:
									return 13






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 5E-4







"""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)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

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






class 			lowercase__			(      A    ):
							'''simple docstring'''







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

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		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(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([T5_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							Dict:
									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						) / 'utest_input.source'
									_UpperCAmelCase							 =		input_file_name.parent / 'utest_output.txt'
									assert not output_file_name.exists()

									_UpperCAmelCase							 =		{
									    '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							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(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(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(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(snake_case						).exists()
											os.remove(Path(snake_case						)						)


