"""simple docstring"""
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowercase__ 	=     {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=True       )     ->Tuple:
  if model_type not in MODEL_CLASSES:
    raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()       )}.'       )

  a__							,   a__							,   a__							,   a__:					List[str]      							=     MODEL_CLASSES[model_type]

  # Initialise TF model
  if config_file in aws_config_map:
    a__:					Optional[int]      							=     cached_file(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
  a__:					Any      							=     config_class.from_json_file(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[int]      							=     True
  a__:					str      							=     True
  print(F'Building TensorFlow model from configuration: {config}'       )
  a__:					Optional[int]      							=     model_class(_SCREAMING_SNAKE_CASE       )

  # Load weights from tf checkpoint
  if pytorch_checkpoint_path in aws_config_map.keys():
    a__:					Union[str, Any]      							=     cached_file(
        _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
  # Load PyTorch checkpoint in tf2 model:
  a__:					Tuple      							=     load_pytorch_checkpoint_in_tfa_model(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  if compare_with_pt_model:
    a__:					Tuple      							=     tf_model(tf_model.dummy_inputs		,					training=_SCREAMING_SNAKE_CASE       )  # build the network

    a__:					Tuple      							=     torch.load(_SCREAMING_SNAKE_CASE		,					map_location='cpu'       )
    a__:					str      							=     pt_model_class.from_pretrained(
        pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE		,					config=_SCREAMING_SNAKE_CASE		,					state_dict=_SCREAMING_SNAKE_CASE       )

    with torch.no_grad():
      a__:					str      							=     pt_model(**pt_model.dummy_inputs       )

    a__:					List[str]      							=     pto[0].numpy()
    a__:					int      							=     tfo[0].numpy()
    a__:					List[Any]      							=     np.amax(np.abs(np_pt - np_tf       )       )
    print(F'Max absolute difference between models outputs {diff}'       )
    assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'

  # Save pytorch-model
  print(F'Save TensorFlow model to {tf_dump_path}'       )
  tf_model.save_weights(_SCREAMING_SNAKE_CASE		,					save_format='h5'       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False		,					)     ->Union[str, Any]:
  if args_model_type is None:
    a__:					List[str]      							=     list(MODEL_CLASSES.keys()       )
  else:
    a__:					Optional[Any]      							=     [args_model_type]

  for j, model_type in enumerate(_SCREAMING_SNAKE_CASE		,					start=1       ):
    print('=' * 100       )
    print(F' Converting model type {j}/{len(_SCREAMING_SNAKE_CASE       )}: {model_type}'       )
    print('=' * 100       )
    if model_type not in MODEL_CLASSES:
      raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()       )}.'       )

    a__							,   a__							,   a__							,   a__							,   a__:					int      							=     MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      a__:					Optional[Any]      							=     list(aws_model_maps.keys()       )
    if config_shortcut_names_or_path is None:
      a__:					Optional[Any]      							=     model_shortcut_names_or_path

    for i, (model_shortcut_name, config_shortcut_name) in enumerate(
        zip(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					start=1       ):
      print('-' * 100       )
      if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
        if not only_convert_finetuned_models:
          print(F'    Skipping finetuned checkpoint {model_shortcut_name}'       )
          continue
        a__:					Union[str, Any]      							=     model_shortcut_name
      elif only_convert_finetuned_models:
        print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'       )
        continue
      print(
          F'    Converting checkpoint {i}/{len(_SCREAMING_SNAKE_CASE       )}: {model_shortcut_name} - model_type {model_type}'       )
      print('-' * 100       )

      if config_shortcut_name in aws_config_map:
        a__:					int      							=     cached_file(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
      else:
        a__:					List[Any]      							=     config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        a__:					int      							=     cached_file(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
      else:
        a__:					List[Any]      							=     model_shortcut_name

      if os.path.isfile(_SCREAMING_SNAKE_CASE       ):
        a__:					Union[str, Any]      							=     'converted_model'

      convert_pt_checkpoint_to_tf(
          model_type=_SCREAMING_SNAKE_CASE		,					pytorch_checkpoint_path=_SCREAMING_SNAKE_CASE		,					config_file=_SCREAMING_SNAKE_CASE		,					tf_dump_path=os.path.join(_SCREAMING_SNAKE_CASE		,					model_shortcut_name + '-tf_model.h5'       )		,					compare_with_pt_model=_SCREAMING_SNAKE_CASE		,					)
      if remove_cached_files:
        os.remove(_SCREAMING_SNAKE_CASE       )
        os.remove(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
 )
 parser.add_argument(
     '--model_type',
     default=None,
     type=str,
     help=(
         f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
         'convert all the models from AWS.'
     ),
 )
 parser.add_argument(
     '--pytorch_checkpoint_path',
     default=None,
     type=str,
     help=(
         'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
         'If not given, will download and convert all the checkpoints from AWS.'
     ),
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     help=(
         'The config json file corresponding to the pre-trained model. \n'
         'This specifies the model architecture. If not given and '
         '--pytorch_checkpoint_path is not given or is a shortcut name '
         'use the configuration associated to the shortcut name on the AWS'
     ),
 )
 parser.add_argument(
     '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
 )
 parser.add_argument(
     '--use_cached_models',
     action='store_true',
     help='Use cached models if possible instead of updating to latest checkpoint versions.',
 )
 parser.add_argument(
     '--remove_cached_files',
     action='store_true',
     help='Remove pytorch models after conversion (save memory when converting in batches).',
 )
 parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
 lowercase__ 	=     parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )



"""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')


