



import logging
import os
import threading
import time


try:
    import warnings
except ImportError:
    __UpperCAmelCase          =       None

try:
    import msvcrt
except ImportError:
    __UpperCAmelCase          =       None

try:
    import fcntl
except ImportError:
    __UpperCAmelCase          =       None


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


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

__UpperCAmelCase          =       '''3.0.12'''


__UpperCAmelCase          =       None
def 		UpperCamelCase	(   )	->     Tuple:
   global _logger
   UpperCamelCase    :  Tuple							=	_logger or logging.getLogger(__name__	)
   return _logger



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		List[Any]:
          UpperCamelCase    :  Union[str, Any]							=	lock_file
          return None






       def __str__(			self       )     ->		int:
          UpperCamelCase    :  Any							=	F"""The file lock '{self.lock_file}' could not be acquired."""
          return temp






class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          UpperCamelCase    :  Tuple							=	lock
          return None
       def __enter__(			self       )     ->		List[str]:
          return self.lock






       def __exit__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          self.lock.release()
          return None






class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=-1,			SCREAMING_SNAKE_CASE_=None       )     ->		Any:
          UpperCamelCase    :  str							=	max_filename_length if max_filename_length is not None else 255
          # Hash the filename if it's too long
          UpperCamelCase    :  Any							=	self.hash_filename_if_too_long(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          # The path to the lock file.
          UpperCamelCase    :  List[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.
          UpperCamelCase    :  Tuple							=	None

          # The default timeout value.
          UpperCamelCase    :  Dict							=	timeout

          # We use this lock primarily for the lock counter.
          UpperCamelCase    :  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.
          UpperCamelCase    :  Dict							=	0
          return None
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self._lock_file
       @property
       def 					snake_case_       (			self       )     ->		str:
          return self._timeout
       @timeout.setter
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          UpperCamelCase    :  Union[str, Any]							=	float(SCREAMING_SNAKE_CASE_       )
          return None
       def 					snake_case_       (			self       )     ->		Tuple:
          raise NotImplementedError()
       def 					snake_case_       (			self       )     ->		List[str]:
          raise NotImplementedError()
       @property
       def 					snake_case_       (			self       )     ->		str:
          return self._lock_file_fd is not None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=0.05       )     ->		Optional[Any]:
          # Use the default timeout, if no timeout is provided.
          if timeout is None:
             UpperCamelCase    :  str							=	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

          UpperCamelCase    :  List[Any]							=	id(self       )
          UpperCamelCase    :  Optional[Any]							=	self._lock_file
          UpperCamelCase    :  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(SCREAMING_SNAKE_CASE_       )
          except:  # noqa
             # Something did go wrong, so decrement the counter.
             with self._thread_lock:
                UpperCamelCase    :  Optional[int]							=	max(0,			self._lock_counter - 1       )

             raise
          return _Acquire_ReturnProxy(lock=self       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=False       )     ->		Union[str, Any]:
          with self._thread_lock:
             if self.is_locked:
                self._lock_counter -= 1

                if self._lock_counter == 0 or force:
                   UpperCamelCase    :  Tuple							=	id(self       )
                   UpperCamelCase    :  str							=	self._lock_file

                   logger().debug(F"""Attempting to release lock {lock_id} on {lock_filename}"""       )
                   self._release()
                   UpperCamelCase    :  List[str]							=	0
                   logger().debug(F"""Lock {lock_id} released on {lock_filename}"""       )

          return None
       def __enter__(			self       )     ->		List[Any]:
          self.acquire()
          return self
       def __exit__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Dict:
          self.release()
          return None
       def __del__(			self       )     ->		List[Any]:
          self.release(force=SCREAMING_SNAKE_CASE_       )
          return None






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		str:
          UpperCamelCase    :  Optional[Any]							=	os.path.basename(SCREAMING_SNAKE_CASE_       )
          if len(SCREAMING_SNAKE_CASE_       ) > max_length and max_length > 0:
             UpperCamelCase    :  List[Any]							=	os.path.dirname(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Union[str, Any]							=	str(hash(SCREAMING_SNAKE_CASE_       )       )
             UpperCamelCase    :  Dict							=	filename[: max_length - len(SCREAMING_SNAKE_CASE_       ) - 8] + '...' + hashed_filename + '.lock'
             return os.path.join(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          else:
             return path






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=-1,			SCREAMING_SNAKE_CASE_=None       )     ->		Dict:
          from .file_utils import relative_to_absolute_path

          super().__init__(SCREAMING_SNAKE_CASE_,			timeout=SCREAMING_SNAKE_CASE_,			max_filename_length=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Tuple							=	'\\\\?\\' + relative_to_absolute_path(self.lock_file       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Optional[Any]							=	os.O_RDWR | os.O_CREAT | os.O_TRUNC

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






       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Tuple							=	self._lock_file_fd
          UpperCamelCase    :  Dict							=	None
          msvcrt.locking(SCREAMING_SNAKE_CASE_,			msvcrt.LK_UNLCK,			1       )
          os.close(SCREAMING_SNAKE_CASE_       )

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






class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=-1,			SCREAMING_SNAKE_CASE_=None       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	os.statvfs(os.path.dirname(SCREAMING_SNAKE_CASE_       )       ).f_namemax
          super().__init__(SCREAMING_SNAKE_CASE_,			timeout=SCREAMING_SNAKE_CASE_,			max_filename_length=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  List[Any]							=	os.O_RDWR | os.O_CREAT | os.O_TRUNC
          UpperCamelCase    :  Any							=	os.open(self._lock_file,			SCREAMING_SNAKE_CASE_       )

          try:
             fcntl.flock(SCREAMING_SNAKE_CASE_,			fcntl.LOCK_EX | fcntl.LOCK_NB       )
          except OSError:
             os.close(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  Optional[int]							=	fd
          return None






       def 					snake_case_       (			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
          UpperCamelCase    :  List[Any]							=	self._lock_file_fd
          UpperCamelCase    :  int							=	None
          fcntl.flock(SCREAMING_SNAKE_CASE_,			fcntl.LOCK_UN       )
          os.close(SCREAMING_SNAKE_CASE_       )
          return None






class 				lowerCAmelCase_		(  a__							):
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
          try:
             UpperCamelCase    :  List[str]							=	os.open(self._lock_file,			SCREAMING_SNAKE_CASE_       )
          except OSError:
             pass
          else:
             UpperCamelCase    :  Optional[Any]							=	fd
          return None






       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          os.close(self._lock_file_fd       )
          UpperCamelCase    :  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






__UpperCAmelCase          =       None

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

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








import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ImageGPTImageProcessor



class 				lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=7,			SCREAMING_SNAKE_CASE_=3,			SCREAMING_SNAKE_CASE_=18,			SCREAMING_SNAKE_CASE_=30,			SCREAMING_SNAKE_CASE_=400,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=True,			)     ->		Union[str, Any]:
          UpperCamelCase    :  str							=	size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase    :  int							=	parent
          UpperCamelCase    :  List[Any]							=	batch_size
          UpperCamelCase    :  int							=	num_channels
          UpperCamelCase    :  Any							=	image_size
          UpperCamelCase    :  Optional[int]							=	min_resolution
          UpperCamelCase    :  Optional[Any]							=	max_resolution
          UpperCamelCase    :  Union[str, Any]							=	do_resize
          UpperCamelCase    :  List[Any]							=	size
          UpperCamelCase    :  int							=	do_normalize






       def 					snake_case_       (			self       )     ->		Tuple:
          return {
              # here we create 2 clusters for the sake of simplicity
              "clusters": np.asarray(
                  [
                      [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                      [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                  ]       ),
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
          }






@require_torch
@require_vision
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					Any        =  ImageGPTImageProcessor if is_vision_available() else None
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  str							=	ImageGPTImageProcessingTester(self       )
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  int							=	self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'clusters'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_resize'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'size'       )       )
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,			'do_normalize'       )       )
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  Tuple							=	self.image_processing_class.from_dict(self.image_processor_dict       )
          self.assertEqual(image_processor.size,			{'height': 18, 'width': 18}       )

          UpperCamelCase    :  Dict							=	self.image_processing_class.from_dict(self.image_processor_dict,			size=42       )
          self.assertEqual(image_processor.size,			{'height': 42, 'width': 42}       )
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  str							=	self.image_processing_class(**self.image_processor_dict       )
          UpperCamelCase    :  int							=	json.loads(image_processor.to_json_string()       )
          for key, value in self.image_processor_dict.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			obj[key]       )       )
             else:
                self.assertEqual(obj[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Dict:
          UpperCamelCase    :  Tuple							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCamelCase    :  List[str]							=	os.path.join(SCREAMING_SNAKE_CASE_,			'image_processor.json'       )
             image_processor_first.to_json_file(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Tuple							=	self.image_processing_class.from_json_file(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  List[Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          UpperCamelCase    :  Optional[Any]							=	self.image_processing_class(**self.image_processor_dict       )

          with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  str							=	self.image_processing_class.from_pretrained(SCREAMING_SNAKE_CASE_       ).to_dict()

          UpperCamelCase    :  Union[str, Any]							=	image_processor_first.to_dict()
          for key, value in image_processor_first.items():
             if key == "clusters":
                self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE_,			image_processor_second[key]       )       )
             else:
                self.assertEqual(image_processor_first[key],			SCREAMING_SNAKE_CASE_       )






       @unittest.skip('ImageGPT requires clusters at initialization'       )
       def 					snake_case_       (			self       )     ->		str:
          pass






def 		UpperCamelCase	(   )	->     int:
   UpperCamelCase    :  Optional[int]							=	load_dataset('hf-internal-testing/fixtures_image_utils'       ,     split='test'	)

   UpperCamelCase    :  int							=	Image.open(dataset[4]['file']	)
   UpperCamelCase    :  Optional[Any]							=	Image.open(dataset[5]['file']	)

   UpperCamelCase    :  str							=	[imagea, imagea]

   return images



@require_vision
@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def 					snake_case_       (			self       )     ->		str:
          UpperCamelCase    :  List[str]							=	ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'       )

          UpperCamelCase    :  List[str]							=	prepare_images()

          # test non-batched
          UpperCamelCase    :  int							=	image_processing(images[0],			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(1, 1024)       )

          UpperCamelCase    :  Union[str, Any]							=	[306, 191, 191]
          self.assertEqual(encoding.input_ids[0, :3].tolist(),			SCREAMING_SNAKE_CASE_       )

          # test batched
          UpperCamelCase    :  Tuple							=	image_processing(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )

          self.assertIsInstance(encoding.input_ids,			torch.LongTensor       )
          self.assertEqual(encoding.input_ids.shape,			(2, 1024)       )

          UpperCamelCase    :  Optional[Any]							=	[303, 13, 13]
          self.assertEqual(encoding.input_ids[1, -3:].tolist(),			SCREAMING_SNAKE_CASE_       )



