"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch


lowercase__    =     random.Random()







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=1.0		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[Any]:
  if rng is None:
    a__:					Optional[Any]      							=     global_rng

  a__:					Any      							=     []
  for batch_idx in range(shape[0]       ):
    values.append([]       )
    for _ in range(shape[1]       ):
      values[-1].append(rng.random() * scale       )

  return values




@require_torch
class        __snake_case	(					unittest.TestCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase=7   ,      lowercase=4_00   ,      lowercase=20_00   ,      lowercase=1   ,      lowercase=0.0   ,      lowercase=1_60_00   ,      lowercase=True   ,      lowercase=80   ,      lowercase=16   ,      lowercase=64   ,      lowercase="hann_window"   ,      lowercase=80   ,      lowercase=76_00   ,      lowercase=1e-10   ,      lowercase=True   ,      )     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[int]      							=     parent
        a__:					int      							=     batch_size
        a__:					Tuple      							=     min_seq_length
        a__:					int      							=     max_seq_length
        a__:					Optional[int]      							=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__:					Union[str, Any]      							=     feature_size
        a__:					Tuple      							=     padding_value
        a__:					str      							=     sampling_rate
        a__:					Dict      							=     do_normalize
        a__:					int      							=     num_mel_bins
        a__:					Dict      							=     hop_length
        a__:					Dict      							=     win_length
        a__:					Dict      							=     win_function
        a__:					int      							=     fmin
        a__:					str      							=     fmax
        a__:					List[str]      							=     mel_floor
        a__:					List[str]      							=     return_attention_mask



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "do_normalize": self.do_normalize,
            "num_mel_bins": self.num_mel_bins,
            "hop_length": self.hop_length,
            "win_length": self.win_length,
            "win_function": self.win_function,
            "fmin": self.fmin,
            "fmax": self.fmax,
            "mel_floor": self.mel_floor,
            "return_attention_mask": self.return_attention_mask,
        }



      def   lowerCamelCase_       (						self   ,      lowercase=False   ,      lowercase=False)     ->      List[Any]:





        '''simple docstring'''





        def _flatten(lowercase):
          return list(itertools.chain(*__A))

        if equal_length:
          a__:					int      							=     floats_list((self.batch_size, self.max_seq_length))
        else:
          # make sure that inputs increase in size
          a__:					str      							=     [
              _flatten(floats_list((x, self.feature_size)))
              for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff)
          ]

        if numpify:
          a__:					int      							=     [np.asarray(__A) for x in speech_inputs]

        return speech_inputs



      def   lowerCamelCase_       (						self   ,      lowercase=False   ,      lowercase=False)     ->      Optional[int]:





        '''simple docstring'''





        if equal_length:
          a__:					Dict      							=     [floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)]
        else:
          # make sure that inputs increase in size
          a__:					Dict      							=     [
              floats_list((x, self.num_mel_bins))
              for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff)
          ]

        if numpify:
          a__:					int      							=     [np.asarray(__A) for x in speech_inputs]

        return speech_inputs







@require_torch
class        __snake_case	(					lowerCamelCase__	,				unittest.TestCase    ):
      a__				=		SpeechTaFeatureExtractor



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					str      							=     SpeechTaFeatureExtractionTester(self)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        self.assertTrue(np.all(np.mean(__A   ,      axis=0) < 1e-3))
        self.assertTrue(np.all(np.abs(np.var(__A   ,      axis=0) - 1) < 1e-3))



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					Dict      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:					int      							=     [floats_list((1, x))[0] for x in range(8_00   ,      14_00   ,      2_00)]
        a__:					Optional[Any]      							=     [np.asarray(__A) for speech_input in speech_inputs]

        # Test not batched input
        a__:					Tuple      							=     feat_extract(speech_inputs[0]   ,      return_tensors='np').input_values
        a__:					Union[str, Any]      							=     feat_extract(np_speech_inputs[0]   ,      return_tensors='np').input_values
        self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1e-3))

        # Test batched
        a__:					Optional[Any]      							=     feat_extract(__A   ,      return_tensors='np').input_values
        a__:					int      							=     feat_extract(__A   ,      return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(__A   ,      __A):
          self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1e-3))



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					Dict      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:					Dict      							=     [floats_list((1, x))[0] for x in range(8_00   ,      14_00   ,      2_00)]

        a__:					Optional[int]      							=     ['''longest''', '''max_length''', '''do_not_pad''']
        a__:					str      							=     [None, 16_00, None]
        for max_length, padding in zip(__A   ,      __A):
          a__:					Tuple      							=     feat_extract(__A   ,      padding=__A   ,      max_length=__A   ,      return_tensors='np')
          a__:					Union[str, Any]      							=     processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					str      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:					List[str]      							=     range(8_00   ,      14_00   ,      2_00)
        a__:					str      							=     [floats_list((1, x))[0] for x in lengths]

        a__:					List[Any]      							=     ['''longest''', '''max_length''', '''do_not_pad''']
        a__:					str      							=     [None, 16_00, None]

        for max_length, padding in zip(__A   ,      __A):
          a__:					List[Any]      							=     feat_extract(__A   ,      max_length=__A   ,      padding=__A)
          a__:					int      							=     processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        a__:					List[str]      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:					str      							=     [floats_list((1, x))[0] for x in range(8_00   ,      14_00   ,      2_00)]
        a__:					Union[str, Any]      							=     feat_extract(
            __A   ,      truncation=__A   ,      max_length=10_00   ,      padding='max_length'   ,      return_tensors='np')
        a__:					Dict      							=     processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Any      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:					Any      							=     [floats_list((1, x))[0] for x in range(8_00   ,      14_00   ,      2_00)]
        a__:					Tuple      							=     feat_extract(
            __A   ,      truncation=__A   ,      max_length=10_00   ,      padding='longest'   ,      return_tensors='np')
        a__:					Optional[int]      							=     processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 10_00))

        a__:					Any      							=     [floats_list((1, x))[0] for x in range(8_00   ,      14_00   ,      2_00)]
        a__:					str      							=     feat_extract(
            __A   ,      truncation=__A   ,      max_length=20_00   ,      padding='longest'   ,      return_tensors='np')
        a__:					str      							=     processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 12_00))



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:					Any      							=     np.random.rand(1_00).astype(np.floataa)
        a__:					Optional[int]      							=     np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
          a__:					List[Any]      							=     feature_extractor.pad([{'input_values': inputs}]   ,      return_tensors='np')
          self.assertTrue(np_processed.input_values.dtype == np.floataa)
          a__:					List[str]      							=     feature_extractor.pad([{'input_values': inputs}]   ,      return_tensors='pt')
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:					Optional[int]      							=     [floats_list((1, x))[0] for x in range(8_00   ,      14_00   ,      2_00)]
        a__:					List[str]      							=     [np.asarray(__A) for speech_input in speech_inputs]

        # Test feature size
        a__:					Tuple      							=     feature_extractor(audio_target=__A   ,      padding=__A   ,      return_tensors='np').input_values
        self.assertTrue(input_values.ndim == 3)
        self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins)

        # Test not batched input
        a__:					Dict      							=     feature_extractor(speech_inputs[0]   ,      return_tensors='np').input_values
        a__:					List[Any]      							=     feature_extractor(np_speech_inputs[0]   ,      return_tensors='np').input_values
        self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1e-3))

        # Test batched
        a__:					Optional[Any]      							=     feature_extractor(__A   ,      return_tensors='np').input_values
        a__:					Dict      							=     feature_extractor(__A   ,      return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(__A   ,      __A):
          self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1e-3))

        # Test 2-D numpy arrays are batched.
        a__:					Optional[Any]      							=     [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
        a__:					Any      							=     np.asarray(__A)
        a__:					Dict      							=     feature_extractor(__A   ,      return_tensors='np').input_values
        a__:					Tuple      							=     feature_extractor(__A   ,      return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(__A   ,      __A):
          self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1e-3))



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					List[Any]      							=     self.feat_extract_tester.prepare_inputs_for_target()
        a__:					Any      							=     self.feature_extraction_class(**self.feat_extract_dict)
        a__:					Optional[int]      							=     feat_extract.model_input_names[0]

        a__:					Dict      							=     BatchFeature({input_name: speech_inputs})

        self.assertTrue(all(len(__A) == len(__A) for x, y in zip(__A   ,      processed_features[input_name])))

        a__:					Any      							=     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__A)
        a__:					Union[str, Any]      							=     BatchFeature({input_name: speech_inputs}   ,      tensor_type='np')

        a__:					Dict      							=     processed_features[input_name]

        if len(batch_features_input.shape) < 3:
          a__:					Optional[int]      							=     batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))



      @require_torch
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Tuple      							=     self.feat_extract_tester.prepare_inputs_for_target(equal_length=__A)
        a__:					Dict      							=     self.feature_extraction_class(**self.feat_extract_dict)
        a__:					List[str]      							=     feat_extract.model_input_names[0]

        a__:					Dict      							=     BatchFeature({input_name: speech_inputs}   ,      tensor_type='pt')

        a__:					Optional[Any]      							=     processed_features[input_name]

        if len(batch_features_input.shape) < 3:
          a__:					Optional[int]      							=     batch_features_input[:, :, None]

        self.assertTrue(
            batch_features_input.shape
            == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))



      @require_torch
      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					List[str]      							=     self.feature_extraction_class(**self.feat_extract_dict)
        a__:					str      							=     self.feat_extract_tester.prepare_inputs_for_target()
        a__:					Any      							=     feat_extract.model_input_names[0]

        a__:					List[Any]      							=     BatchFeature({input_name: speech_inputs})

        a__:					Union[str, Any]      							=     feat_extract.num_mel_bins  # hack!

        a__:					List[str]      							=     feat_extract.pad(__A   ,      padding='longest'   ,      return_tensors='np')[input_name]
        a__:					Dict      							=     feat_extract.pad(__A   ,      padding='longest'   ,      return_tensors='pt')[input_name]

        self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     self.feat_extract_dict
        a__:					Any      							=     True
        a__:					Dict      							=     self.feature_extraction_class(**__A)
        a__:					Tuple      							=     self.feat_extract_tester.prepare_inputs_for_target()
        a__:					Any      							=     [len(__A) for x in speech_inputs]
        a__:					Tuple      							=     feat_extract.model_input_names[0]

        a__:					Optional[int]      							=     BatchFeature({input_name: speech_inputs})

        a__:					List[Any]      							=     feat_extract.num_mel_bins  # hack!

        a__:					str      							=     feat_extract.pad(__A   ,      padding='longest'   ,      return_tensors='np')
        self.assertIn('attention_mask'   ,      __A)
        self.assertListEqual(list(processed.attention_mask.shape)   ,      list(processed[input_name].shape[:2]))
        self.assertListEqual(processed.attention_mask.sum(-1).tolist()   ,      __A)



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Dict      							=     self.feat_extract_dict
        a__:					Optional[int]      							=     True
        a__:					Optional[int]      							=     self.feature_extraction_class(**__A)
        a__:					Any      							=     self.feat_extract_tester.prepare_inputs_for_target()
        a__:					Union[str, Any]      							=     [len(__A) for x in speech_inputs]
        a__:					str      							=     feat_extract.model_input_names[0]

        a__:					Tuple      							=     BatchFeature({input_name: speech_inputs})
        a__:					Optional[Any]      							=     min(__A)

        a__:					List[Any]      							=     feat_extract.num_mel_bins  # hack!

        a__:					str      							=     feat_extract.pad(
            __A   ,      padding='max_length'   ,      max_length=__A   ,      truncation=__A   ,      return_tensors='np')
        self.assertIn('attention_mask'   ,      __A)
        self.assertListEqual(
            list(processed_pad.attention_mask.shape)   ,      [processed_pad[input_name].shape[0], max_length])
        self.assertListEqual(
            processed_pad.attention_mask[:, :max_length].sum(-1).tolist()   ,      [max_length for x in speech_inputs])



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        from datasets import load_dataset

        a__:					List[Any]      							=     load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,      'clean'   ,      split='validation')
        # automatic decoding with librispeech
        a__:					Optional[Any]      							=     ds.sort('id').select(range(__A))[:num_samples]['''audio''']

        return [x["array"] for x in speech_samples]



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Dict      							=     torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03])
        # fmt: on

        a__:					Optional[int]      							=     self._load_datasamples(1)
        a__:					Optional[Any]      							=     SpeechTaFeatureExtractor()
        a__:					str      							=     feature_extractor(__A   ,      return_tensors='pt').input_values
        self.assertEquals(input_values.shape   ,      (1, 9_36_80))
        self.assertTrue(torch.allclose(input_values[0, :30]   ,      __A   ,      atol=1e-6))



      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     torch.tensor(
            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998])
        # fmt: on

        a__:					int      							=     self._load_datasamples(1)
        a__:					Tuple      							=     SpeechTaFeatureExtractor()
        a__:					List[str]      							=     feature_extractor(audio_target=__A   ,      return_tensors='pt').input_values
        self.assertEquals(input_values.shape   ,      (1, 3_66, 80))
        self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      __A   ,      atol=1e-4))



"""simple docstring"""
import logging
import os
import threading
import time


try:
 import warnings
except ImportError:
 lowercase__ 	=     None

try:
 import msvcrt
except ImportError:
 lowercase__ 	=     None

try:
 import fcntl
except ImportError:
 lowercase__ 	=     None


# Backward compatibility
# ------------------------------------------------
try:
 TimeoutError
except NameError:
 lowercase__ 	=     OSError


# Data
# ------------------------------------------------
lowercase__ 	=     [
    'Timeout',
    'BaseFileLock',
    'WindowsFileLock',
    'UnixFileLock',
    'SoftFileLock',
    'FileLock',
]

lowercase__ 	=     '3.0.12'


lowercase__ 	=     None







def 						__a       (     )     ->List[Any]:
  global _logger
  a__:					str      							=     _logger or logging.getLogger(__name__       )
  return _logger




class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      lowercase)     ->      Any:





        '''simple docstring'''





        a__:					List[Any]      							=     lock_file
        return None



      def __str__(						self)     ->      List[str]:





        '''simple docstring'''





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







class        __snake_case	:



      def __init__(						self   ,      lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Any      							=     lock
        return None



      def __enter__(						self)     ->      List[Any]:





        '''simple docstring'''





        return self.lock



      def __exit__(						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      int:





        '''simple docstring'''





        self.lock.release()
        return None







class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=-1   ,      lowercase=None)     ->      Dict:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     max_filename_length if max_filename_length is not None else 2_55
        # Hash the filename if it's too long
        a__:					Tuple      							=     self.hash_filename_if_too_long(lowercase   ,      lowercase)
        # The path to the lock file.
        a__:					Any      							=     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__:					Dict      							=     None

        # The default timeout value.
        a__:					Union[str, Any]      							=     timeout

        # We use this lock primarily for the lock counter.
        a__:					Any      							=     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__:					Tuple      							=     0
        return None



      @property
      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        return self._lock_file



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        return self._timeout



      @timeout.setter
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[int]      							=     float(lowercase)
        return None



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        raise NotImplementedError()



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        raise NotImplementedError()



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return self._lock_file_fd is not None



      def   lowerCamelCase_       (						self   ,      lowercase=None   ,      lowercase=0.05)     ->      int:





        '''simple docstring'''





        if timeout is None:
          a__:					int      							=     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__:					Optional[int]      							=     id(self)
        a__:					Union[str, Any]      							=     self._lock_file
        a__:					Optional[int]      							=     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(lowercase)
        except:  # noqa
          # Something did go wrong, so decrement the counter.
          with self._thread_lock:
            a__:					Optional[int]      							=     max(0   ,      self._lock_counter - 1)

          raise
        return _Acquire_ReturnProxy(lock=self)



      def   lowerCamelCase_       (						self   ,      lowercase=False)     ->      Tuple:





        '''simple docstring'''





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

            if self._lock_counter == 0 or force:
              a__:					List[str]      							=     id(self)
              a__:					List[Any]      							=     self._lock_file

              logger().debug(f'Attempting to release lock {lock_id} on {lock_filename}')
              self._release()
              a__:					List[str]      							=     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   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        self.release()
        return None



      def __del__(						self)     ->      Optional[int]:





        '''simple docstring'''





        self.release(force=lowercase)
        return None



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





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







class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase=-1   ,      lowercase=None)     ->      Dict:





        '''simple docstring'''





        from .file_utils import relative_to_absolute_path

        super().__init__(lowercase   ,      timeout=lowercase   ,      max_filename_length=lowercase)
        a__:					List[Any]      							=     '\\\\?\\' + relative_to_absolute_path(self.lock_file)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





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

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



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					int      							=     self._lock_file_fd
        a__:					Union[str, Any]      							=     None
        msvcrt.locking(lowercase   ,      msvcrt.LK_UNLCK   ,      1)
        os.close(lowercase)

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







class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      lowercase   ,      lowercase=-1   ,      lowercase=None)     ->      Dict:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     os.statvfs(os.path.dirname(lowercase)).f_namemax
        super().__init__(lowercase   ,      timeout=lowercase   ,      max_filename_length=lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Optional[int]      							=     os.O_RDWR | os.O_CREAT | os.O_TRUNC
        a__:					int      							=     os.open(self._lock_file   ,      lowercase)

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



      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        a__:					str      							=     self._lock_file_fd
        a__:					Tuple      							=     None
        fcntl.flock(lowercase   ,      fcntl.LOCK_UN)
        os.close(lowercase)
        return None







class        __snake_case	(					__lowerCAmelCase    ):



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					int      							=     os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
          a__:					Optional[Any]      							=     os.open(self._lock_file   ,      lowercase)
        except OSError:
          pass
        else:
          a__:					Union[str, Any]      							=     fd
        return None



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        os.close(self._lock_file_fd)
        a__:					int      							=     None

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







lowercase__ 	=     None

if msvcrt:
 lowercase__ 	=     WindowsFileLock
elif fcntl:
 lowercase__ 	=     UnixFileLock
else:
 lowercase__ 	=     SoftFileLock

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


