






"""simple docstring"""







import logging
import os
import threading
import time


try:
	import warnings
except ImportError:
	__a       :	List[str]             = None

try:
	import msvcrt
except ImportError:
	__a       :	Optional[int]             = None

try:
	import fcntl
except ImportError:
	__a       :	List[Any]             = None


# Backward compatibility
# ------------------------------------------------
try:
	TimeoutError
except NameError:
	__a       :	Tuple             = OSError


# Data
# ------------------------------------------------
__a       :	int             = [
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

__a       :	Any             = '3.0.12'


__a       :	List[str]             = None





def        SCREAMING_SNAKE_CASE       (      ):
					global _logger
					a__          =  _logger or logging.getLogger(__name__)
					return _logger







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def __init__(   self:     Union[str, Any]				,		__A:     int    ):

												'''simple docstring'''

												a__          =  lock_file
												return None





							def __str__(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  F'The file lock \'{self.lock_file}\' could not be acquired.'
												return temp







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Optional[int]				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  lock
												return None




							def __enter__(   self:     Optional[int]    ):

												'''simple docstring'''

												return self.lock





							def __exit__(   self:     Optional[int]				,		__A:     List[Any]				,		__A:     Optional[Any]				,		__A:     int    ):

												'''simple docstring'''

												self.lock.release()
												return None







class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Tuple				,		__A:     Union[str, Any]				,		__A:     List[Any]=-1				,		__A:     List[Any]=None    ):

												'''simple docstring'''

												a__          =  max_filename_length if max_filename_length is not None else 255
												# Hash the filename if it's too long
												a__          =  self.hash_filename_if_too_long(__A				,		__A    )
												# The path to the lock file.
												a__          =  lock_file

												# The file descriptor for the *_lock_file* as it is returned by the
												# os.open() function.
												# This file lock is only NOT None, if the object currently holds the
												# lock.
												a__          =  None

												# The default timeout value.
												a__          =  timeout

												# We use this lock primarily for the lock counter.
												a__          =  threading.Lock()

												# The lock counter is used for implementing the nested locking
												# mechanism. Whenever the lock is acquired, the counter is increased and
												# the lock is only released, when this value is 0 again.
												a__          =  0
												return None




							@property
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												return self._lock_file




							@property
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												return self._timeout




							@timeout.setter
							def  lowercase		(   self:     Dict				,		__A:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  float(__A    )
												return None




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												raise NotImplementedError()




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												raise NotImplementedError()




							@property
							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												return self._lock_file_fd is not None




							def  lowercase		(   self:     Union[str, Any]				,		__A:     Any=None				,		__A:     int=0.0_5    ):

												'''simple docstring'''

												if timeout is None:
																	a__          =  self.timeout

												# Increment the number right at the beginning.
												# We can still undo it, if something fails.
												with self._thread_lock:
																	self._lock_counter += 1

												a__          =  id(self    )
												a__          =  self._lock_file
												a__          =  time.time()
												try:
																	while True:
																						with self._thread_lock:
																											if not self.is_locked:
																																logger().debug(F'Attempting to acquire lock {lock_id} on {lock_filename}'    )
																																self._acquire()

																						if self.is_locked:
																											logger().debug(F'Lock {lock_id} acquired on {lock_filename}'    )
																											break
																						elif timeout >= 0 and time.time() - start_time > timeout:
																											logger().debug(F'Timeout on acquiring lock {lock_id} on {lock_filename}'    )
																											raise Timeout(self._lock_file    )
																						else:
																											logger().debug(
																											    F'Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...'    )
																											time.sleep(__A    )
												except:  # noqa
																	# Something did go wrong, so decrement the counter.
																	with self._thread_lock:
																						a__          =  max(0				,		self._lock_counter - 1    )

																	raise
												return _Acquire_ReturnProxy(lock=self    )




							def  lowercase		(   self:     List[Any]				,		__A:     Optional[int]=False    ):

												'''simple docstring'''

												with self._thread_lock:
																	if self.is_locked:
																						self._lock_counter -= 1

																						if self._lock_counter == 0 or force:
																											a__          =  id(self    )
																											a__          =  self._lock_file

																											logger().debug(F'Attempting to release lock {lock_id} on {lock_filename}'    )
																											self._release()
																											a__          =  0
																											logger().debug(F'Lock {lock_id} released on {lock_filename}'    )

												return None




							def __enter__(   self:     Dict    ):

												'''simple docstring'''

												self.acquire()
												return self




							def __exit__(   self:     Tuple				,		__A:     Tuple				,		__A:     Optional[Any]				,		__A:     int    ):

												'''simple docstring'''

												self.release()
												return None




							def __del__(   self:     List[str]    ):

												'''simple docstring'''

												self.release(force=__A    )
												return None





							def  lowercase		(   self:     str				,		__A:     str				,		__A:     int    ):

												'''simple docstring'''

												a__          =  os.path.basename(__A    )
												if len(__A    ) > max_length and max_length > 0:
																	a__          =  os.path.dirname(__A    )
																	a__          =  str(hash(__A    )    )
																	a__          =  filename[: max_length - len(__A    ) - 8] + '''...''' + hashed_filename + '''.lock'''
																	return os.path.join(__A				,		__A    )
												else:
																	return path







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Union[str, Any]				,		__A:     Union[str, Any]=-1				,		__A:     Tuple=None    ):

												'''simple docstring'''

												from .file_utils import relative_to_absolute_path

												super().__init__(__A				,		timeout=__A				,		max_filename_length=__A    )
												a__          =  '''\\\\?\\''' + relative_to_absolute_path(self.lock_file    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC

												try:
																	a__          =  os.open(self._lock_file				,		__A    )
												except OSError:
																	pass
												else:
																	try:
																						msvcrt.locking(__A				,		msvcrt.LK_NBLCK				,		1    )
																	except OSError:
																						os.close(__A    )
																	else:
																						a__          =  fd
												return None





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self._lock_file_fd
												a__          =  None
												msvcrt.locking(__A				,		msvcrt.LK_UNLCK				,		1    )
												os.close(__A    )

												try:
																	os.remove(self._lock_file    )
												# Probably another instance of the application
												# that acquired the file lock.
												except OSError:
																	pass
												return None







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def __init__(   self:     Optional[int]				,		__A:     List[Any]				,		__A:     Dict=-1				,		__A:     Union[str, Any]=None    ):

												'''simple docstring'''

												a__          =  os.statvfs(os.path.dirname(__A    )    ).f_namemax
												super().__init__(__A				,		timeout=__A				,		max_filename_length=__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC
												a__          =  os.open(self._lock_file				,		__A    )

												try:
																	fcntl.flock(__A				,		fcntl.LOCK_EX | fcntl.LOCK_NB    )
												except OSError:
																	os.close(__A    )
												else:
																	a__          =  fd
												return None





							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  self._lock_file_fd
												a__          =  None
												fcntl.flock(__A				,		fcntl.LOCK_UN    )
												os.close(__A    )
												return None







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
												try:
																	a__          =  os.open(self._lock_file				,		__A    )
												except OSError:
																	pass
												else:
																	a__          =  fd
												return None





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												os.close(self._lock_file_fd    )
												a__          =  None

												try:
																	os.remove(self._lock_file    )
												# The file is already deleted and that's what we want.
												except OSError:
																	pass
												return None

__a       :	Optional[int]             = None

if msvcrt:
	__a       :	Union[str, Any]             = WindowsFileLock
elif fcntl:
	__a       :	Optional[int]             = UnixFileLock
else:
	__a       :	Any             = SoftFileLock

	if warnings is not None:
		warnings.warn('only soft file lock is available')










"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_attention_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  num_choices




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  None
												if self.use_attention_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

												a__          =  None
												if self.use_token_type_ids:
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size    )

												a__          =  RobertaPreLayerNormConfig(
												    vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)

												return config, input_ids, token_type_ids, attention_mask




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

							_SCREAMING_SNAKE_CASE    =(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	a__          =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )
																	a__          =  model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__A    )







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


