






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  :							Dict			  =    logging.getLogger()

def      lowerCAmelCase__	(    _a       :			Path  ,      _a       :			list    ):
		snake_case_				:							Union[str, Any]						 =       "\n".join(_a    )
		Path(_a    ).open("w"    ).writelines(_a    )


lowercase  :							int			  =    '''patrickvonplaten/t5-tiny-random'''
lowercase  :							List[str]			  =    '''sshleifer/bart-tiny-random'''
lowercase  :							Optional[Any]			  =    '''sshleifer/tiny-mbart'''

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




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								snake_case_				:							int						 =       Path(self.get_auto_remove_tmp_dir()				) / "utest_input.source"
								snake_case_				:							Optional[Any]						 =       input_file_name.parent / "utest_output.txt"
								assert not output_file_name.exists()
								snake_case_				:							Tuple						 =       [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
								_dump_articles(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							List[str]						 =       str(Path(self.get_auto_remove_tmp_dir()				) / "scores.json"				)
								snake_case_				:							Optional[int]						 =       "translation_en_to_de" if model == T5_TINY else "summarization"
								snake_case_				:							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(_SCREAMING_SNAKE_CASE		,				"argv"		,				_SCREAMING_SNAKE_CASE				):
										run_generate()
										assert Path(_SCREAMING_SNAKE_CASE				).exists()
										# os.remove(Path(output_file_name))






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								self.run_eval_tester(_SCREAMING_SNAKE_CASE				)






						@parameterized.expand([BART_TINY, MBART_TINY]				)
						@slow
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								self.run_eval_tester(_SCREAMING_SNAKE_CASE				)






						@parameterized.expand([T5_TINY, MBART_TINY]				)
						@slow
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[str]:
								snake_case_				:							int						 =       Path(self.get_auto_remove_tmp_dir()				) / "utest_input.source"
								snake_case_				:							Optional[int]						 =       input_file_name.parent / "utest_output.txt"
								assert not output_file_name.exists()

								snake_case_				:							Dict						 =       {
								    "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!",
								    ],
								}

								snake_case_				:							Dict						 =       Path(self.get_auto_remove_tmp_dir()				)
								snake_case_				:							List[Any]						 =       str(tmp_dir / "scores.json"				)
								snake_case_				:							Optional[Any]						 =       str(tmp_dir / "val.target"				)
								_dump_articles(_SCREAMING_SNAKE_CASE		,				text["en"]				)
								_dump_articles(_SCREAMING_SNAKE_CASE		,				text["de"]				)
								snake_case_				:							Union[str, Any]						 =       "translation_en_to_de" if model == T5_TINY else "summarization"
								snake_case_				:							Tuple						 =       f'''
            run_eval_search.py
            {model}
            {str(_SCREAMING_SNAKE_CASE				)}
            {str(_SCREAMING_SNAKE_CASE				)}
            --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(_SCREAMING_SNAKE_CASE		,				"argv"		,				_SCREAMING_SNAKE_CASE				):
										with CaptureStdout() as cs:
												run_search()
										snake_case_				:							Optional[Any]						 =       [" num_beams | length_penalty", model, "Best score args"]
										snake_case_				:							List[str]						 =       ["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				)				)








import functools
import operator

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


lowercase  :							List[Any]			  =    logging.get_logger(__name__)

lowercase  :							Any			  =    {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Optional[int]						=					'sew-d'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=32		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=("p2c", "c2p")		,				_SCREAMING_SNAKE_CASE="layer_norm"		,				_SCREAMING_SNAKE_CASE="gelu_python"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-7		,				_SCREAMING_SNAKE_CASE=1e-5		,				_SCREAMING_SNAKE_CASE="group"		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,				_SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=128		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.05		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=10		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE="mean"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=256		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[Any]:
								super().__init__(**_SCREAMING_SNAKE_CASE		,				pad_token_id=_SCREAMING_SNAKE_CASE		,				bos_token_id=_SCREAMING_SNAKE_CASE		,				eos_token_id=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[Any]						 =       hidden_size
								snake_case_				:							Any						 =       feat_extract_norm
								snake_case_				:							Union[str, Any]						 =       feat_extract_activation
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Any						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       conv_bias
								snake_case_				:							Union[str, Any]						 =       num_conv_pos_embeddings
								snake_case_				:							Tuple						 =       num_conv_pos_embedding_groups
								snake_case_				:							Optional[int]						 =       len(self.conv_dim				)
								snake_case_				:							str						 =       num_hidden_layers
								snake_case_				:							List[Any]						 =       intermediate_size
								snake_case_				:							List[Any]						 =       squeeze_factor
								snake_case_				:							Tuple						 =       max_position_embeddings
								snake_case_				:							Optional[int]						 =       position_buckets
								snake_case_				:							Union[str, Any]						 =       share_att_key
								snake_case_				:							Optional[int]						 =       relative_attention
								snake_case_				:							List[str]						 =       norm_rel_ebd
								snake_case_				:							Tuple						 =       list(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       hidden_act
								snake_case_				:							Any						 =       num_attention_heads
								snake_case_				:							Dict						 =       hidden_dropout
								snake_case_				:							int						 =       attention_dropout
								snake_case_				:							Any						 =       activation_dropout
								snake_case_				:							Optional[Any]						 =       feat_proj_dropout
								snake_case_				:							Tuple						 =       final_dropout
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							Tuple						 =       feature_layer_norm_eps
								snake_case_				:							Any						 =       initializer_range
								snake_case_				:							Optional[int]						 =       vocab_size

								if (
								    (len(self.conv_stride				) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
								    or (len(self.conv_dim				) != self.num_feat_extract_layers)
								):
										raise ValueError(
										    "Configuration for convolutional layers is incorrect."
										    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
										    f'''but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)'''
										    f'''= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'''				)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								snake_case_				:							List[str]						 =       apply_spec_augment
								snake_case_				:							List[Any]						 =       mask_time_prob
								snake_case_				:							str						 =       mask_time_length
								snake_case_				:							Optional[Any]						 =       mask_time_min_masks
								snake_case_				:							Optional[Any]						 =       mask_feature_prob
								snake_case_				:							str						 =       mask_feature_length
								snake_case_				:							Optional[int]						 =       mask_feature_min_masks

								# ctc loss
								snake_case_				:							Any						 =       ctc_loss_reduction
								snake_case_				:							Tuple						 =       ctc_zero_infinity

								# sequence classification
								snake_case_				:							int						 =       use_weighted_layer_sum
								snake_case_				:							List[Any]						 =       classifier_proj_size






						@property
						def       _lowerCAmelCase       (	self				) ->				Dict:
								return functools.reduce(operator.mul		,				self.conv_stride		,				1				)
