import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property


@unittest.skipUnless(os.path.exists(a     )				,       """Tatoeba directory does not exist."""     )
class 						lowercase    (     unittest.TestCase     ):




							@cached_property
							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_UpperCamelCase      )




							@slow
							def 	__snake_case(						self   :					Tuple      )					->				List[Any]:



												'''simple docstring'''

												self.resolver.convert_models(["heb-eng"]      )





							@slow
							def 	__snake_case(						self   :					Any      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.resolver.write_model_card("opus-mt-he-en"	,							dry_run=_UpperCamelCase      )
												assert mmeta["long_pair"] == "heb-eng"



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_lowerCamelCase					:   Tuple							=		{
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}


class 						lowercase    (     a     ):
							lowercase__						:       Optional[Any]								=							"""ernie_m"""
							lowercase__						:       Dict[str, str]								=							{"dropout": "classifier_dropout", "num_classes": "num_labels"}




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					int = 250_002	,							_UpperCamelCase   :					int = 768	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 12	,							_UpperCamelCase   :					int = 3_072	,							_UpperCamelCase   :					str = "gelu"	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					float = 0.1	,							_UpperCamelCase   :					int = 514	,							_UpperCamelCase   :					float = 0.0_2	,							_UpperCamelCase   :					int = 1	,							_UpperCamelCase   :					float = 1e-05	,							_UpperCamelCase   :					int=None	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					int=0.0	,							**_UpperCamelCase   :					Union[str, Any]	,							)					->				Optional[Any]:



												'''simple docstring'''

												super().__init__(pad_token_id=_UpperCamelCase	,							**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       vocab_size
												SCREAMING_SNAKE_CASE    			=       hidden_size
												SCREAMING_SNAKE_CASE    			=       num_hidden_layers
												SCREAMING_SNAKE_CASE    			=       num_attention_heads
												SCREAMING_SNAKE_CASE    			=       intermediate_size
												SCREAMING_SNAKE_CASE    			=       hidden_act
												SCREAMING_SNAKE_CASE    			=       hidden_dropout_prob
												SCREAMING_SNAKE_CASE    			=       attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE    			=       max_position_embeddings
												SCREAMING_SNAKE_CASE    			=       initializer_range
												SCREAMING_SNAKE_CASE    			=       layer_norm_eps
												SCREAMING_SNAKE_CASE    			=       classifier_dropout
												SCREAMING_SNAKE_CASE    			=       is_decoder
												SCREAMING_SNAKE_CASE    			=       act_dropout




