


"""simple docstring"""







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(_lowerCamelCase				)   ,    "Tatoeba directory does not exist."				)
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       @cached_property
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							tempfile.mkdtemp()
         return TatoebaConverter(save_dir=lowerCAmelCase_						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





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







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCAmelCase_						)
         assert mmeta["long_pair"] == "heb-eng"







'''simple docstring'''







import warnings

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


lowercase    :							int      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """xlnet"""
							__lowercase      		=       ["""mems"""]
							__lowercase      		=       {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(       self    ,   lowerCAmelCase_=3_20_00    ,   lowerCAmelCase_=10_24    ,   lowerCAmelCase_=24    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=40_96    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="bi"    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=1E-12    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5_12    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=-1    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_="last"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="tanh"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=2    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		vocab_size
								_snake_case   						=		d_model
								_snake_case   						=		n_layer
								_snake_case   						=		n_head
								if d_model % n_head != 0:
									raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'  )
								if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'  )
								_snake_case   						=		d_model // n_head
								_snake_case   						=		ff_activation
								_snake_case   						=		d_inner
								_snake_case   						=		untie_r
								_snake_case   						=		attn_type

								_snake_case   						=		initializer_range
								_snake_case   						=		layer_norm_eps

								_snake_case   						=		dropout
								_snake_case   						=		mem_len
								_snake_case   						=		reuse_len
								_snake_case   						=		bi_data
								_snake_case   						=		clamp_len
								_snake_case   						=		same_length

								_snake_case   						=		summary_type
								_snake_case   						=		summary_use_proj
								_snake_case   						=		summary_activation
								_snake_case   						=		summary_last_dropout
								_snake_case   						=		start_n_top
								_snake_case   						=		end_n_top

								_snake_case   						=		bos_token_id
								_snake_case   						=		pad_token_id
								_snake_case   						=		eos_token_id

								if "use_cache" in kwargs:
									warnings.warn(
									    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
									    ' instead.'    ,   lowerCAmelCase_    ,   )
									_snake_case   						=		kwargs['use_cache']

								_snake_case   						=		use_mems_eval
								_snake_case   						=		use_mems_train
								super().__init__(pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_  )


							@property
							def  lowerCamelCase				(       self  ):





								"""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  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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

