

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase     : Dict       =						logging.get_logger(__name__)







def       lowercase					(	__A :					Any		)				->			List[str]:





    '''simple docstring'''




    snake_case							:		int        =		MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        snake_case							:		Tuple        =		[144, 192, 240]
        snake_case							:		Dict        =		[16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        snake_case							:		int        =		[96, 120, 144]
        snake_case							:		Union[str, Any]        =		[16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        snake_case							:		str        =		[64, 80, 96]
        snake_case							:		str        =		[16, 16, 24, 48, 64, 80, 320]
        snake_case							:		Union[str, Any]        =		0.05
        snake_case							:		Union[str, Any]        =		2.0

    if mobilevit_name.startswith("""deeplabv3_"""		):
        snake_case							:		Union[str, Any]        =		512
        snake_case							:		Optional[int]        =		16
        snake_case							:		Optional[Any]        =		21
        snake_case							:		int        =		"""pascal-voc-id2label.json"""
    else:
        snake_case							:		Optional[int]        =		1000
        snake_case							:		Optional[Any]        =		"""imagenet-1k-id2label.json"""

    snake_case							:		List[str]        =		"""huggingface/label-files"""
    snake_case							:		Optional[Any]        =		json.load(open(hf_hub_download(lowerCamelCase__ ,    lowerCamelCase__ ,    repo_type="""dataset"""		) ,    """r"""		)		)
    snake_case							:		int        =		{int(lowerCamelCase__		): v for k, v in idalabel.items()}
    snake_case							:		Optional[int]        =		idalabel
    snake_case							:		Any        =		{v: k for k, v in idalabel.items()}

    return config







def       lowercase					(	__A :					Union[str, Any] ,    __A :					int=False		)				->			Union[str, Any]:





    '''simple docstring'''




    for i in range(1 ,    6		):
        if f"""layer_{i}.""" in name:
            snake_case							:		Union[str, Any]        =		name.replace(f"""layer_{i}.""" ,    f"""encoder.layer.{i - 1}."""		)

    if "conv_1." in name:
        snake_case							:		List[Any]        =		name.replace("""conv_1.""" ,    """conv_stem."""		)
    if ".block." in name:
        snake_case							:		Dict        =		name.replace(""".block.""" ,    """."""		)

    if "exp_1x1" in name:
        snake_case							:		int        =		name.replace("""exp_1x1""" ,    """expand_1x1"""		)
    if "red_1x1" in name:
        snake_case							:		Tuple        =		name.replace("""red_1x1""" ,    """reduce_1x1"""		)
    if ".local_rep.conv_3x3." in name:
        snake_case							:		Dict        =		name.replace(""".local_rep.conv_3x3.""" ,    """.conv_kxk."""		)
    if ".local_rep.conv_1x1." in name:
        snake_case							:		Union[str, Any]        =		name.replace(""".local_rep.conv_1x1.""" ,    """.conv_1x1."""		)
    if ".norm." in name:
        snake_case							:		Any        =		name.replace(""".norm.""" ,    """.normalization."""		)
    if ".conv." in name:
        snake_case							:		List[str]        =		name.replace(""".conv.""" ,    """.convolution."""		)
    if ".conv_proj." in name:
        snake_case							:		int        =		name.replace(""".conv_proj.""" ,    """.conv_projection."""		)

    for i in range(0 ,    2		):
        for j in range(0 ,    4		):
            if f""".{i}.{j}.""" in name:
                snake_case							:		Union[str, Any]        =		name.replace(f""".{i}.{j}.""" ,    f""".{i}.layer.{j}."""		)

    for i in range(2 ,    6		):
        for j in range(0 ,    4		):
            if f""".{i}.{j}.""" in name:
                snake_case							:		str        =		name.replace(f""".{i}.{j}.""" ,    f""".{i}."""		)
                if "expand_1x1" in name:
                    snake_case							:		Any        =		name.replace("""expand_1x1""" ,    """downsampling_layer.expand_1x1"""		)
                if "conv_3x3" in name:
                    snake_case							:		str        =		name.replace("""conv_3x3""" ,    """downsampling_layer.conv_3x3"""		)
                if "reduce_1x1" in name:
                    snake_case							:		List[str]        =		name.replace("""reduce_1x1""" ,    """downsampling_layer.reduce_1x1"""		)

    for i in range(2 ,    5		):
        if f""".global_rep.{i}.weight""" in name:
            snake_case							:		Tuple        =		name.replace(f""".global_rep.{i}.weight""" ,    """.layernorm.weight"""		)
        if f""".global_rep.{i}.bias""" in name:
            snake_case							:		str        =		name.replace(f""".global_rep.{i}.bias""" ,    """.layernorm.bias"""		)

    if ".global_rep." in name:
        snake_case							:		Any        =		name.replace(""".global_rep.""" ,    """.transformer."""		)
    if ".pre_norm_mha.0." in name:
        snake_case							:		List[Any]        =		name.replace(""".pre_norm_mha.0.""" ,    """.layernorm_before."""		)
    if ".pre_norm_mha.1.out_proj." in name:
        snake_case							:		Dict        =		name.replace(""".pre_norm_mha.1.out_proj.""" ,    """.attention.output.dense."""		)
    if ".pre_norm_ffn.0." in name:
        snake_case							:		List[Any]        =		name.replace(""".pre_norm_ffn.0.""" ,    """.layernorm_after."""		)
    if ".pre_norm_ffn.1." in name:
        snake_case							:		Dict        =		name.replace(""".pre_norm_ffn.1.""" ,    """.intermediate.dense."""		)
    if ".pre_norm_ffn.4." in name:
        snake_case							:		List[Any]        =		name.replace(""".pre_norm_ffn.4.""" ,    """.output.dense."""		)
    if ".transformer." in name:
        snake_case							:		Any        =		name.replace(""".transformer.""" ,    """.transformer.layer."""		)

    if ".aspp_layer." in name:
        snake_case							:		int        =		name.replace(""".aspp_layer.""" ,    """."""		)
    if ".aspp_pool." in name:
        snake_case							:		List[Any]        =		name.replace(""".aspp_pool.""" ,    """."""		)
    if "seg_head." in name:
        snake_case							:		List[Any]        =		name.replace("""seg_head.""" ,    """segmentation_head."""		)
    if "segmentation_head.classifier.classifier." in name:
        snake_case							:		str        =		name.replace("""segmentation_head.classifier.classifier.""" ,    """segmentation_head.classifier."""		)

    if "classifier.fc." in name:
        snake_case							:		Any        =		name.replace("""classifier.fc.""" ,    """classifier."""		)
    elif (not base_model) and ("segmentation_head." not in name):
        snake_case							:		Any        =		"""mobilevit.""" + name

    return name







def       lowercase					(	__A :					Dict ,    __A :					Tuple ,    __A :					Optional[int]=False		)				->			int:





    '''simple docstring'''




    if base_model:
        snake_case							:		Union[str, Any]        =		""""""
    else:
        snake_case							:		Optional[int]        =		"""mobilevit."""

    for key in orig_state_dict.copy().keys():
        snake_case							:		Optional[Any]        =		orig_state_dict.pop(lowerCamelCase__		)

        if key[:8] == "encoder.":
            snake_case							:		int        =		key[8:]

        if "qkv" in key:
            snake_case							:		Any        =		key.split("""."""		)
            snake_case							:		Dict        =		int(key_split[0][6:]		) - 1
            snake_case							:		Any        =		int(key_split[3]		)
            snake_case							:		List[Any]        =		model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}"""		)
            snake_case							:		str        =		layer.transformer.layer[transformer_num].attention.attention.all_head_size
            snake_case							:		List[str]        =		(
                f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
            )
            if "weight" in key:
                snake_case							:		Tuple        =		val[:dim, :]
                snake_case							:		int        =		val[dim : dim * 2, :]
                snake_case							:		Dict        =		val[-dim:, :]
            else:
                snake_case							:		List[str]        =		val[:dim]
                snake_case							:		Union[str, Any]        =		val[dim : dim * 2]
                snake_case							:		Any        =		val[-dim:]
        else:
            snake_case							:		Any        =		val

    return orig_state_dict







def       lowercase					(	)				->			int:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case							:		Any        =		Image.open(requests.get(lowerCamelCase__ ,    stream=lowerCamelCase__		).raw		)
    return im







@torch.no_grad()
def       lowercase					(	__A :					Dict ,    __A :					Tuple ,    __A :					Optional[int] ,    __A :					Dict=False		)				->			List[Any]:





    '''simple docstring'''




    snake_case							:		List[str]        =		get_mobilevit_config(lowerCamelCase__		)

    # load original state_dict
    snake_case							:		Optional[Any]        =		torch.load(lowerCamelCase__ ,    map_location="""cpu"""		)

    # load 🤗 model
    if mobilevit_name.startswith("""deeplabv3_"""		):
        snake_case							:		Tuple        =		MobileViTForSemanticSegmentation(lowerCamelCase__		).eval()
    else:
        snake_case							:		Union[str, Any]        =		MobileViTForImageClassification(lowerCamelCase__		).eval()

    snake_case							:		Optional[int]        =		convert_state_dict(lowerCamelCase__ ,    lowerCamelCase__		)
    model.load_state_dict(lowerCamelCase__		)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    snake_case							:		int        =		MobileViTImageProcessor(crop_size=config.image_size ,    size=config.image_size + 32		)
    snake_case							:		Tuple        =		image_processor(images=prepare_img() ,    return_tensors="""pt"""		)
    snake_case							:		Tuple        =		model(**lowerCamelCase__		)
    snake_case							:		Union[str, Any]        =		outputs.logits

    if mobilevit_name.startswith("""deeplabv3_"""		):
        assert logits.shape == (1, 21, 32, 32)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            snake_case							:		Optional[int]        =		torch.tensor(
                [
                    [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
                    [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
                    [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
                ]		)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            snake_case							:		Any        =		torch.tensor(
                [
                    [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
                    [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
                    [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
                ]		)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            snake_case							:		Union[str, Any]        =		torch.tensor(
                [
                    [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
                    [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
                    [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
                ]		)
        else:
            raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""		)

        assert torch.allclose(logits[0, :3, :3, :3] ,    lowerCamelCase__ ,    atol=1E-4		)
    else:
        assert logits.shape == (1, 1000)

        if mobilevit_name == "mobilevit_s":
            snake_case							:		Any        =		torch.tensor([-0.9_866, 0.2_392, -1.1_241]		)
        elif mobilevit_name == "mobilevit_xs":
            snake_case							:		List[Any]        =		torch.tensor([-2.4_761, -0.9_399, -1.9_587]		)
        elif mobilevit_name == "mobilevit_xxs":
            snake_case							:		List[str]        =		torch.tensor([-1.9_364, -1.2_327, -0.4_653]		)
        else:
            raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""		)

        assert torch.allclose(logits[0, :3] ,    lowerCamelCase__ ,    atol=1E-4		)

    Path(lowerCamelCase__		).mkdir(exist_ok=lowerCamelCase__		)
    print(f"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""		)
    model.save_pretrained(lowerCamelCase__		)
    print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
    image_processor.save_pretrained(lowerCamelCase__		)

    if push_to_hub:
        snake_case							:		Any        =		{
            """mobilevit_s""": """mobilevit-small""",
            """mobilevit_xs""": """mobilevit-x-small""",
            """mobilevit_xxs""": """mobilevit-xx-small""",
            """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
            """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
            """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
        }

        print("""Pushing to the hub..."""		)
        snake_case							:		Any        =		model_mapping[mobilevit_name]
        image_processor.push_to_hub(lowerCamelCase__ ,    organization="""apple"""		)
        model.push_to_hub(lowerCamelCase__ ,    organization="""apple"""		)


if __name__ == "__main__":
     __lowercase     : Optional[Any]       =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--mobilevit_name''',
         default='''mobilevit_s''',
         type=str,
         help=(
             '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
             ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
         ),
     )
     parser.add_argument(
         '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
     )

     __lowercase     : Union[str, Any]       =						parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )







"""simple docstring"""


import logging
import os
import threading
import time


try:
   import warnings
except ImportError:
   __snake_case    					=					None

try:
   import msvcrt
except ImportError:
   __snake_case    					=					None

try:
   import fcntl
except ImportError:
   __snake_case    					=					None


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


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

__snake_case    					=					'3.0.12'


__snake_case    					=					None


def    _lowerCamelCase   (     ):
 global _logger
 lowercase__       :       Tuple				=						_logger or logging.getLogger(__name__  )
 return _logger

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__						)      ->							Optional[int]:
    lowercase__       :       Union[str, Any]				=						lock_file
    return None







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

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       str				=						lock
    return None


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







   def __exit__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Any:
    self.lock.release()
    return None

class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



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

    # The default timeout value.
    lowercase__       :       Optional[Any]				=						timeout

    # We use this lock primarily for the lock counter.
    lowercase__       :       Optional[int]				=						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.
    lowercase__       :       Union[str, Any]				=						0
    return None


   @property
   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    return self._lock_file


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


   @timeout.setter
   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							Optional[Any]:
    lowercase__       :       Union[str, Any]				=						float(lowerCamelCase__						)
    return None


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    raise NotImplementedError()


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    raise NotImplementedError()


   @property
   def 					UpperCAmelCase__(						self						)      ->							str:
    return self._lock_file_fd is not None


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__=None ,     lowerCamelCase__=0.05						)      ->							List[str]:
    # Use the default timeout, if no timeout is provided.
    if timeout is None:
     lowercase__       :       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

    lowercase__       :       Tuple				=						id(self						)
    lowercase__       :       Any				=						self._lock_file
    lowercase__       :       Union[str, Any]				=						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(lowerCamelCase__						)
    except:  # noqa
     # Something did go wrong, so decrement the counter.
     with self._thread_lock:
      lowercase__       :       Any				=						max(0 ,     self._lock_counter - 1						)

     raise
    return _Acquire_ReturnProxy(lock=self						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__=False						)      ->							int:
    with self._thread_lock:
     if self.is_locked:
      self._lock_counter -= 1

      if self._lock_counter == 0 or force:
       lowercase__       :       Tuple				=						id(self						)
       lowercase__       :       int				=						self._lock_file

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

    return None


   def __enter__(						self						)      ->							Dict:
    self.acquire()
    return self


   def __exit__(						self ,     lowerCamelCase__ ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							Dict:
    self.release()
    return None


   def __del__(						self						)      ->							int:
    self.release(force=lowerCamelCase__						)
    return None







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ ,     lowerCamelCase__						)      ->							str:
    lowercase__       :       Optional[int]				=						os.path.basename(lowerCamelCase__						)
    if len(lowerCamelCase__						) > max_length and max_length > 0:
     lowercase__       :       Union[str, Any]				=						os.path.dirname(lowerCamelCase__						)
     lowercase__       :       List[Any]				=						str(hash(lowerCamelCase__						)						)
     lowercase__       :       Optional[int]				=						filename[: max_length - len(lowerCamelCase__						) - 8] + """...""" + hashed_filename + """.lock"""
     return os.path.join(lowerCamelCase__ ,     lowerCamelCase__						)
    else:
     return path

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=-1 ,     lowerCamelCase__=None						)      ->							Tuple:
    from .file_utils import relative_to_absolute_path

    super().__init__(lowerCamelCase__ ,     timeout=lowerCamelCase__ ,     max_filename_length=lowerCamelCase__						)
    lowercase__       :       List[Any]				=						"""\\\\?\\""" + relative_to_absolute_path(self.lock_file						)


   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    lowercase__       :       Union[str, Any]				=						os.O_RDWR | os.O_CREAT | os.O_TRUNC

    try:
     lowercase__       :       Dict				=						os.open(self._lock_file ,     lowerCamelCase__						)
    except OSError:
     pass
    else:
     try:
      msvcrt.locking(lowerCamelCase__ ,     msvcrt.LK_NBLCK ,     1						)
     except OSError:
      os.close(lowerCamelCase__						)
     else:
      lowercase__       :       Optional[Any]				=						fd
    return None







   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       int				=						self._lock_file_fd
    lowercase__       :       Any				=						None
    msvcrt.locking(lowerCamelCase__ ,     msvcrt.LK_UNLCK ,     1						)
    os.close(lowerCamelCase__						)

    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				(							__UpperCAmelCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=-1 ,     lowerCamelCase__=None						)      ->							List[str]:
    lowercase__       :       Optional[Any]				=						os.statvfs(os.path.dirname(lowerCamelCase__						)						).f_namemax
    super().__init__(lowerCamelCase__ ,     timeout=lowerCamelCase__ ,     max_filename_length=lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       List[Any]				=						os.O_RDWR | os.O_CREAT | os.O_TRUNC
    lowercase__       :       List[Any]				=						os.open(self._lock_file ,     lowerCamelCase__						)

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







   def 					UpperCAmelCase__(						self						)      ->							str:
    # 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
    lowercase__       :       Optional[int]				=						self._lock_file_fd
    lowercase__       :       Optional[Any]				=						None
    fcntl.flock(lowerCamelCase__ ,     fcntl.LOCK_UN						)
    os.close(lowerCamelCase__						)
    return None

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       Tuple				=						os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
    try:
     lowercase__       :       Any				=						os.open(self._lock_file ,     lowerCamelCase__						)
    except OSError:
     pass
    else:
     lowercase__       :       Union[str, Any]				=						fd
    return None







   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    os.close(self._lock_file_fd						)
    lowercase__       :       Optional[Any]				=						None

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

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

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