



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


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)

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






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	"""transfo-xl"""
	lowerCAmelCase	:   int				=	["""mems"""]
	lowerCAmelCase	:   int				=	{
	    """n_token""": """vocab_size""",
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """n_head""",
	    """num_hidden_layers""": """n_layer""",
	}



	def __init__(       self			,	UpperCAmelCase__=267_735			,	UpperCAmelCase__=[20_000, 40_000, 200_000]			,	UpperCAmelCase__=1_024			,	UpperCAmelCase__=1_024			,	UpperCAmelCase__=16			,	UpperCAmelCase__=64			,	UpperCAmelCase__=4_096			,	UpperCAmelCase__=4			,	UpperCAmelCase__=False			,	UpperCAmelCase__=18			,	UpperCAmelCase__=1_600			,	UpperCAmelCase__=1_000			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0			,	UpperCAmelCase__=-1			,	UpperCAmelCase__=True			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=True			,	UpperCAmelCase__="normal"			,	UpperCAmelCase__=0.01			,	UpperCAmelCase__=0.01			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-5			,	UpperCAmelCase__=0			,	**UpperCAmelCase__			,	):
							A__							=       vocab_size
							A__							=       []
							self.cutoffs.extend(UpperCAmelCase__		)
							if proj_share_all_but_first:
													A__							=       [False] + [True] * len(self.cutoffs		)
							else:
													A__							=       [False] + [False] * len(self.cutoffs		)
							A__							=       d_model
							A__							=       d_embed
							A__							=       d_head
							A__							=       d_inner
							A__							=       div_val
							A__							=       pre_lnorm
							A__							=       n_layer
							A__							=       n_head
							A__							=       mem_len
							A__							=       same_length
							A__							=       attn_type
							A__							=       clamp_len
							A__							=       sample_softmax
							A__							=       adaptive
							A__							=       dropout
							A__							=       dropatt
							A__							=       untie_r
							A__							=       init
							A__							=       init_range
							A__							=       proj_init_std
							A__							=       init_std
							A__							=       layer_norm_epsilon
							super().__init__(eos_token_id=UpperCAmelCase__			,	**UpperCAmelCase__		)



	@property
	def 			__A							(       self		):
							# Message copied from Transformer-XL documentation
							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 			__A							(       self			,	UpperCAmelCase__		):
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""		)










import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout
def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





						"""simple docstring"""





						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       FileLock(str(tmpdir / "foo.lock"    )    )
						A__							=       0.01
						with locka.acquire():
												with pytest.raises(_A    ):
																		A__							=       time.time()
																		locka.acquire(_A    )
																		assert time.time() - _start > timeout
def  UpperCamelCase						(       _A			:				str    )->       List[Any]:





						"""simple docstring"""





						A__							=       "a" * 1000 + ".lock"
						A__							=       FileLock(str(tmpdir / filename    )    )
						assert locka._lock_file.endswith(".lock"    )
						assert not locka._lock_file.endswith(_A    )
						assert len(os.path.basename(locka._lock_file    )    ) <= 255
						A__							=       FileLock(tmpdir / filename    )
						with locka.acquire():
												with pytest.raises(_A    ):
																		locka.acquire(0    )





