from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertConfig,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
      )


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  13
        SCREAMING_SNAKE_CASE_          =  7
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  False
        SCREAMING_SNAKE_CASE_          =  False
        SCREAMING_SNAKE_CASE_          =  False
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  99
        SCREAMING_SNAKE_CASE_          =  0
        SCREAMING_SNAKE_CASE_          =  32
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  4
        SCREAMING_SNAKE_CASE_          =  0.1
        SCREAMING_SNAKE_CASE_          =  0.1
        SCREAMING_SNAKE_CASE_          =  512
        SCREAMING_SNAKE_CASE_          =  16
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  0.02
        SCREAMING_SNAKE_CASE_          =  3
        SCREAMING_SNAKE_CASE_          =  4
        SCREAMING_SNAKE_CASE_          =  "last"
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  0







  def    __A     (							self :       int   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size   )
        SCREAMING_SNAKE_CASE_          =  random_attention_mask([self.batch_size, self.seq_length]  ,     dtype=tf.floataa   )

        SCREAMING_SNAKE_CASE_          =  None
        if self.use_input_lengths:
              SCREAMING_SNAKE_CASE_          =  (
                  ids_tensor([self.batch_size]  ,     vocab_size=2   ) + self.seq_length - 2
              )  # small variation of seq_length

        SCREAMING_SNAKE_CASE_          =  None
        if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.n_langs   )

        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     self.type_sequence_label_size   )
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels   )
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     2  ,     dtype=tf.floataa   )
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     self.num_choices   )

        SCREAMING_SNAKE_CASE_          =  FlaubertConfig(
            vocab_size=self.vocab_size  ,     n_special=self.n_special  ,     emb_dim=self.hidden_size  ,     n_layers=self.num_hidden_layers  ,     n_heads=self.num_attention_heads  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     gelu_activation=self.gelu_activation  ,     sinusoidal_embeddings=self.sinusoidal_embeddings  ,     asm=self.asm  ,     causal=self.causal  ,     n_langs=self.n_langs  ,     max_position_embeddings=self.max_position_embeddings  ,     initializer_range=self.initializer_range  ,     summary_type=self.summary_type  ,     use_proj=self.use_proj  ,     bos_token_id=self.bos_token_id  ,     )

        return (
            config,
            input_ids,
            token_type_ids,
            input_lengths,
            sequence_labels,
            token_labels,
            is_impossible_labels,
            choice_labels,
            input_mask,
        )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       Any  ,     )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertModel(config=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [input_ids, input_mask]
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       str  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Tuple  ,     )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertWithLMHeadModel(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)   )







  def    __A     (							self :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       List[str]  ,     )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForQuestionAnsweringSimple(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths}

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        self.parent.assertEqual(result.start_logits.shape  ,     (self.batch_size, self.seq_length)   )
        self.parent.assertEqual(result.end_logits.shape  ,     (self.batch_size, self.seq_length)   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       str  ,     __magic_name__ :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       Tuple  ,     )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForSequenceClassification(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "lengths": input_lengths}

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)   )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]  ,     )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.num_labels
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForTokenClassification(config=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)   )







  def    __A     (							self :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Any  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.num_choices
        SCREAMING_SNAKE_CASE_          =  TFFlaubertForMultipleChoice(config=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  tf.tile(tf.expand_dims(__magic_name__  ,     1   )  ,     (1, self.num_choices, 1)   )
        SCREAMING_SNAKE_CASE_          =  tf.tile(tf.expand_dims(__magic_name__  ,     1   )  ,     (1, self.num_choices, 1)   )
        SCREAMING_SNAKE_CASE_          =  tf.tile(tf.expand_dims(__magic_name__  ,     1   )  ,     (1, self.num_choices, 1)   )
        SCREAMING_SNAKE_CASE_          =  {
            "input_ids": multiple_choice_inputs_ids,
            "attention_mask": multiple_choice_input_mask,
            "token_type_ids": multiple_choice_token_type_ids,
        }
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_choices)   )







  def    __A     (							self :       str   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , 
        )          =  config_and_inputs
        SCREAMING_SNAKE_CASE_          =  {
            "input_ids": input_ids,
            "token_type_ids": token_type_ids,
            "langs": token_type_ids,
            "lengths": input_lengths,
        }
        return config, inputs_dict



@require_tf
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						(
      (
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
          TFFlaubertForSequenceClassification,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForTokenClassification,
          TFFlaubertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  lowerCamelCase__     =						(
      (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  lowerCamelCase__     =						(
      {
          '''feature-extraction''': TFFlaubertModel,
          '''fill-mask''': TFFlaubertWithLMHeadModel,
          '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
          '''text-classification''': TFFlaubertForSequenceClassification,
          '''token-classification''': TFFlaubertForTokenClassification,
          '''zero-shot''': TFFlaubertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	List[str]:
        if (
            pipeline_test_casse_name == "QAPipelineTests"
            and tokenizer_name is not None
            and not tokenizer_name.endswith("Fast"   )
        ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True

        return False







  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     emb_dim=37   )







  def    __A     (							self :       str   )  ->	Optional[int]:
        self.config_tester.run_common_tests()







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*__magic_name__   )







  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*__magic_name__   )







  def    __A     (							self :       int   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*__magic_name__   )







  def    __A     (							self :       str   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_token_classification(*__magic_name__   )







  def    __A     (							self :       int   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_multiple_choice(*__magic_name__   )







  @slow
  def    __A     (							self :       int   )  ->	Dict:
        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_          =  TFFlaubertModel.from_pretrained(__magic_name__   )
              self.assertIsNotNone(__magic_name__   )



@require_tf
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Optional[Any]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"   )

        SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(
            [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]  ,     dtype=tf.intaa  ,     )  # "J'aime flaubert !"

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )[0]
        SCREAMING_SNAKE_CASE_          =  tf.TensorShape((1, 8, 512)   )
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice.
        SCREAMING_SNAKE_CASE_          =  tf.convert_to_tensor(
            [
                [
                    [-1.876_8773, -1.56_6555, 0.2707_2418],
                    [-1.692_0038, -0.587_3505, 1.932_9599],
                    [-2.956_3985, -1.699_3835, 1.797_2052],
                ]
            ]  ,     dtype=tf.floataa  ,     )

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,     expected_slice.numpy()  ,     atol=1e-4   )   )



import logging
import os
import threading
import time


try:
      import warnings
except ImportError:
      A     :  Optional[int]					     =       None

try:
      import msvcrt
except ImportError:
      A     :  List[str]					     =       None

try:
      import fcntl
except ImportError:
      A     :  Union[str, Any]					     =       None


# Backward compatibility
# ------------------------------------------------
try:
      TimeoutError
except NameError:
      A     :  int					     =       OSError


# Data
# ------------------------------------------------
A     :  Any					     =       [
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

A     :  Tuple					     =       "3.0.12"


A     :  str					     =       None


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


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  lock_file
        return None







  def __str__(							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  F'''The file lock \'{self.lock_file}\' could not be acquired.'''
        return temp



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  lock
        return None







  def __enter__(							self :       int   )  ->	int:
        return self.lock







  def __exit__(							self :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	Optional[int]:
        self.lock.release()
        return None



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]=-1  ,     __magic_name__ :       Optional[int]=None   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  max_filename_length if max_filename_length is not None else 255
        # Hash the filename if it's too long
        SCREAMING_SNAKE_CASE_          =  self.hash_filename_if_too_long(__magic_name__  ,     __magic_name__   )
        # The path to the lock file.
        SCREAMING_SNAKE_CASE_          =  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.
        SCREAMING_SNAKE_CASE_          =  None

        # The default timeout value.
        SCREAMING_SNAKE_CASE_          =  timeout

        # We use this lock primarily for the lock counter.
        SCREAMING_SNAKE_CASE_          =  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.
        SCREAMING_SNAKE_CASE_          =  0
        return None







  @property
  def    __A     (							self :       int   )  ->	Optional[Any]:
        return self._lock_file







  @property
  def    __A     (							self :       List[str]   )  ->	Union[str, Any]:
        return self._timeout







  @timeout.setter
  def    __A     (							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  float(__magic_name__   )
        return None







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        raise NotImplementedError()







  def    __A     (							self :       Optional[Any]   )  ->	int:
        raise NotImplementedError()







  @property
  def    __A     (							self :       Dict   )  ->	List[str]:
        return self._lock_file_fd is not None







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Optional[int]=0.05   )  ->	str:
        # Use the default timeout, if no timeout is provided.
        if timeout is None:
              SCREAMING_SNAKE_CASE_          =  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

        SCREAMING_SNAKE_CASE_          =  id(self   )
        SCREAMING_SNAKE_CASE_          =  self._lock_file
        SCREAMING_SNAKE_CASE_          =  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(__magic_name__   )
        except:  # noqa
              # Something did go wrong, so decrement the counter.
              with self._thread_lock:
                    SCREAMING_SNAKE_CASE_          =  max(0  ,     self._lock_counter - 1   )

              raise
        return _Acquire_ReturnProxy(lock=self   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Tuple=False   )  ->	Optional[int]:
        with self._thread_lock:
              if self.is_locked:
                    self._lock_counter -= 1

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

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

        return None







  def __enter__(							self :       Optional[Any]   )  ->	Tuple:
        self.acquire()
        return self







  def __exit__(							self :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       Union[str, Any]   )  ->	Union[str, Any]:
        self.release()
        return None







  def __del__(							self :       Optional[int]   )  ->	Optional[Any]:
        self.release(force=__magic_name__   )
        return None







  def    __A     (							self :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.path.basename(__magic_name__   )
        if len(__magic_name__   ) > max_length and max_length > 0:
              SCREAMING_SNAKE_CASE_          =  os.path.dirname(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  str(hash(__magic_name__   )   )
              SCREAMING_SNAKE_CASE_          =  filename[: max_length - len(__magic_name__   ) - 8] + "..." + hashed_filename + ".lock"
              return os.path.join(__magic_name__  ,     __magic_name__   )
        else:
              return path



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Optional[int]=None   )  ->	Dict:
        from .file_utils import relative_to_absolute_path

        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  "\\\\?\\" + relative_to_absolute_path(self.lock_file   )







  def    __A     (							self :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC

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







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        msvcrt.locking(__magic_name__  ,     msvcrt.LK_UNLCK  ,     1   )
        os.close(__magic_name__   )

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



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Dict  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Dict=None   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  os.statvfs(os.path.dirname(__magic_name__   )   ).f_namemax
        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )







  def    __A     (							self :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC
        SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )

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







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        # Do not remove the lockfile:
        #
        #   https://github.com/benediktschmitt/py-filelock/issues/31
        #   https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        fcntl.flock(__magic_name__  ,     fcntl.LOCK_UN   )
        os.close(__magic_name__   )
        return None



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
              SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )
        except OSError:
              pass
        else:
              SCREAMING_SNAKE_CASE_          =  fd
        return None







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        os.close(self._lock_file_fd   )
        SCREAMING_SNAKE_CASE_          =  None

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



A     :  List[str]					     =       None

if msvcrt:
      A     :  Tuple					     =       WindowsFileLock
elif fcntl:
      A     :  Union[str, Any]					     =       UnixFileLock
else:
      A     :  List[Any]					     =       SoftFileLock

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


