


"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case    					=					logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case    					=					[]
for i in range(6):
   # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
   rename_keys.append(
       (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
   )
   rename_keys.append(
       (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
   )
   rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
   rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
   rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
   rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
   rename_keys.append(
       (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
   )
   rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
   rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
   rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
   # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
   )
   rename_keys.append(
       (
           F"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
           F"decoder.layers.{i}.encoder_attn.out_proj.weight",
       )
   )
   rename_keys.append(
       (
           F"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
           F"decoder.layers.{i}.encoder_attn.out_proj.bias",
       )
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
   rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
   rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
   rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

   # q, k, v projections in self/cross-attention in decoder for conditional DETR
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
   )

   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
   )
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
   )
   # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
   )
   rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
   rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
   rename_keys.append(
       (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
   )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)


def    _lowerCamelCase   (     lowerCamelCase__	:							int       ,		lowerCamelCase__	:							Any       ,		lowerCamelCase__	:							Union[str, Any]  ):
 lowercase__       :       Optional[Any]				=						state_dict.pop(lowerCamelCase__  )
 lowercase__       :       str				=						val


def    _lowerCamelCase   (     lowerCamelCase__	:							Dict  ):
 lowercase__       :       Optional[Any]				=						OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   lowercase__       :       List[Any]				=						key.replace("""backbone.0.body"""       ,		"""backbone.conv_encoder.model"""  )
   lowercase__       :       Dict				=						value
  else:
   lowercase__       :       Optional[Any]				=						value

 return new_state_dict


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]       ,		lowerCamelCase__	:							Dict=False  ):
 lowercase__       :       Optional[int]				=						""""""
 if is_panoptic:
  lowercase__       :       Union[str, Any]				=						"""conditional_detr."""

 # first: transformer encoder
 for i in range(6  ):
  # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
  lowercase__       :       Union[str, Any]				=						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''  )
  lowercase__       :       Optional[int]				=						state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''  )
  # next, add query, keys and values (in that order) to the state dict
  lowercase__       :       Any				=						in_proj_weight[:2_56, :]
  lowercase__       :       str				=						in_proj_bias[:2_56]
  lowercase__       :       Union[str, Any]				=						in_proj_weight[2_56:5_12, :]
  lowercase__       :       str				=						in_proj_bias[2_56:5_12]
  lowercase__       :       Any				=						in_proj_weight[-2_56:, :]
  lowercase__       :       List[str]				=						in_proj_bias[-2_56:]


def    _lowerCamelCase   (     ):
 lowercase__       :       str				=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 lowercase__       :       Any				=						Image.open(requests.get(lowerCamelCase__       ,		stream=lowerCamelCase__  ).raw  )

 return im


@torch.no_grad()
def    _lowerCamelCase   (     lowerCamelCase__	:							Union[str, Any]       ,		lowerCamelCase__	:							str  ):
 lowercase__       :       Optional[int]				=						ConditionalDetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  lowercase__       :       Tuple				=						"""resnet101"""
 if "dc5" in model_name:
  lowercase__       :       Any				=						True
 lowercase__       :       Dict				=						"""panoptic""" in model_name
 if is_panoptic:
  lowercase__       :       str				=						2_50
 else:
  lowercase__       :       int				=						91
  lowercase__       :       Any				=						"""huggingface/label-files"""
  lowercase__       :       Dict				=						"""coco-detection-id2label.json"""
  lowercase__       :       int				=						json.load(open(hf_hub_download(lowerCamelCase__       ,		lowerCamelCase__       ,		repo_type="""dataset"""  )       ,		"""r"""  )  )
  lowercase__       :       Optional[Any]				=						{int(lowerCamelCase__  ): v for k, v in idalabel.items()}
  lowercase__       :       Union[str, Any]				=						idalabel
  lowercase__       :       Dict				=						{v: k for k, v in idalabel.items()}

 # load image processor
 lowercase__       :       Optional[Any]				=						"""coco_panoptic""" if is_panoptic else """coco_detection"""
 lowercase__       :       Tuple				=						ConditionalDetrImageProcessor(format=lowerCamelCase__  )

 # prepare image
 lowercase__       :       Tuple				=						prepare_img()
 lowercase__       :       Tuple				=						image_processor(images=lowerCamelCase__       ,		return_tensors="""pt"""  )
 lowercase__       :       Optional[int]				=						encoding["""pixel_values"""]

 logger.info(f'''Converting model {model_name}...'''  )

 # load original model from torch hub
 lowercase__       :       int				=						torch.hub.load("""DeppMeng/ConditionalDETR"""       ,		lowerCamelCase__       ,		pretrained=lowerCamelCase__  ).eval()
 lowercase__       :       Tuple				=						conditional_detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   lowercase__       :       str				=						"""conditional_detr.""" + src
  rename_key(lowerCamelCase__       ,		lowerCamelCase__       ,		lowerCamelCase__  )
 lowercase__       :       Optional[Any]				=						rename_backbone_keys(lowerCamelCase__  )
 # query, key and value matrices need special treatment
 read_in_q_k_v(lowerCamelCase__       ,		is_panoptic=lowerCamelCase__  )
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 lowercase__       :       Any				=						"""conditional_detr.model.""" if is_panoptic else """model."""
 for key in state_dict.copy().keys():
  if is_panoptic:
   if (
       key.startswith("""conditional_detr"""  )
       and not key.startswith("""class_labels_classifier"""  )
       and not key.startswith("""bbox_predictor"""  )
   ):
    lowercase__       :       int				=						state_dict.pop(lowerCamelCase__  )
    lowercase__       :       Optional[Any]				=						val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    lowercase__       :       Union[str, Any]				=						state_dict.pop(lowerCamelCase__  )
    lowercase__       :       Optional[int]				=						val
   elif key.startswith("""bbox_attention"""  ) or key.startswith("""mask_head"""  ):
    continue
   else:
    lowercase__       :       Any				=						state_dict.pop(lowerCamelCase__  )
    lowercase__       :       Optional[int]				=						val
  else:
   if not key.startswith("""class_labels_classifier"""  ) and not key.startswith("""bbox_predictor"""  ):
    lowercase__       :       Tuple				=						state_dict.pop(lowerCamelCase__  )
    lowercase__       :       Any				=						val
    # finally, create HuggingFace model and load state dict
 lowercase__       :       str				=						ConditionalDetrForSegmentation(lowerCamelCase__  ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase__  )
 model.load_state_dict(lowerCamelCase__  )
 model.eval()
 model.push_to_hub(repo_id=lowerCamelCase__       ,		organization="""DepuMeng"""       ,		commit_message="""Add model"""  )
 # verify our conversion
 lowercase__       :       Union[str, Any]				=						conditional_detr(lowerCamelCase__  )
 lowercase__       :       List[str]				=						model(lowerCamelCase__  )
 assert torch.allclose(outputs.logits       ,		original_outputs["""pred_logits"""]       ,		atol=1E-4  )
 assert torch.allclose(outputs.pred_boxes       ,		original_outputs["""pred_boxes"""]       ,		atol=1E-4  )
 if is_panoptic:
  assert torch.allclose(outputs.pred_masks       ,		original_outputs["""pred_masks"""]       ,		atol=1E-4  )

 # Save model and image processor
 logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''  )
 Path(lowerCamelCase__  ).mkdir(exist_ok=lowerCamelCase__  )
 model.save_pretrained(lowerCamelCase__  )
 image_processor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='conditional_detr_resnet50',
       type=str,
       help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   __snake_case    					=					parser.parse_args()
   convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""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')