import unittest

import numpy as np
import requests

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      A     :  List[Any]					     =       False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def __init__(							self :       Tuple  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Optional[Any]=7  ,     __magic_name__ :       List[Any]=3  ,     __magic_name__ :       Any=18  ,     __magic_name__ :       Tuple=30  ,     __magic_name__ :       Union[str, Any]=400  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       int=True  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Optional[Any]=None  ,     )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  size if size is not None else {"height": 20, "width": 20}
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  image_size
        SCREAMING_SNAKE_CASE_          =  min_resolution
        SCREAMING_SNAKE_CASE_          =  max_resolution
        SCREAMING_SNAKE_CASE_          =  size
        SCREAMING_SNAKE_CASE_          =  do_normalize
        SCREAMING_SNAKE_CASE_          =  do_convert_rgb
        SCREAMING_SNAKE_CASE_          =  [512, 1_024, 2_048, 4_096]
        SCREAMING_SNAKE_CASE_          =  patch_size if patch_size is not None else {"height": 16, "width": 16}







  def    __A     (							self :       List[str]   )  ->	List[str]:
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
        SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__magic_name__  ,     stream=__magic_name__   ).raw   ).convert("RGB"   )
        return raw_image



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,      )
@require_torch
@require_vision
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						PixaStructImageProcessor if is_vision_available() else None







  def    __A     (							self :       Any   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  PixaStructImageProcessingTester(self   )







  @property
  def    __A     (							self :       str   )  ->	Dict:
        return self.image_processor_tester.prepare_image_processor_dict()







  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_normalize"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_convert_rgb"   )   )







  def    __A     (							self :       List[str]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.prepare_dummy_image()

        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        SCREAMING_SNAKE_CASE_          =  2_048

        SCREAMING_SNAKE_CASE_          =  image_processor(__magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   )
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()  ,     torch.tensor(0.0606   )  ,     atol=1e-3  ,     rtol=1e-3   )   )







  def    __A     (							self :       Optional[int]   )  ->	str:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







  def    __A     (							self :       List[str]   )  ->	Dict:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        SCREAMING_SNAKE_CASE_          =  True

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              with self.assertRaises(__magic_name__   ):
                    SCREAMING_SNAKE_CASE_          =  image_processor(
                        image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches

              SCREAMING_SNAKE_CASE_          =  "Hello"

              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__  ,     header_text=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__  ,     header_text=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







  def    __A     (							self :       Tuple   )  ->	str:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__  ,     numpify=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     np.ndarray   )

        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )







  def    __A     (							self :       List[Any]   )  ->	Union[str, Any]:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__  ,     torchify=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     torch.Tensor   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * self.image_processor_tester.num_channels
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,      reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,      )
@require_torch
@require_vision
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						PixaStructImageProcessor if is_vision_available() else None







  def    __A     (							self :       Any   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  PixaStructImageProcessingTester(self  ,     num_channels=4   )
        SCREAMING_SNAKE_CASE_          =  3







  @property
  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()







  def    __A     (							self :       Optional[int]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_normalize"   )   )
        self.assertTrue(hasattr(__magic_name__  ,     "do_convert_rgb"   )   )







  def    __A     (							self :       int   )  ->	List[Any]:
        # Initialize image_processor
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__magic_name__   )
        for image in image_inputs:
              self.assertIsInstance(__magic_name__  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  (
            (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
            * (self.image_processor_tester.num_channels - 1)
        ) + 2

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  image_inputs[0]  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (1, max_patch, expected_hidden_dim)  ,     )

              # Test batched
              SCREAMING_SNAKE_CASE_          =  image_processor(
                  __magic_name__  ,     return_tensors="pt"  ,     max_patches=__magic_name__   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape  ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)  ,     )



import logging
import os
import threading
import time


try:
      import warnings
except ImportError:
      A     :  Optional[int]					     =       None

try:
      import msvcrt
except ImportError:
      A     :  List[str]					     =       None

try:
      import fcntl
except ImportError:
      A     :  Union[str, Any]					     =       None


# Backward compatibility
# ------------------------------------------------
try:
      TimeoutError
except NameError:
      A     :  int					     =       OSError


# Data
# ------------------------------------------------
A     :  Any					     =       [
    "Timeout",
    "BaseFileLock",
    "WindowsFileLock",
    "UnixFileLock",
    "SoftFileLock",
    "FileLock",
]

A     :  Tuple					     =       "3.0.12"


A     :  str					     =       None


def      a__ (		):
      global _logger
      SCREAMING_SNAKE_CASE_          =  _logger or logging.getLogger(__name__				)
      return _logger


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  lock_file
        return None







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



class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  lock
        return None







  def __enter__(							self :       int   )  ->	int:
        return self.lock







  def __exit__(							self :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	Optional[int]:
        self.lock.release()
        return None



class   lowerCamelCase					:
  """simple docstring"""







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

        # The default timeout value.
        SCREAMING_SNAKE_CASE_          =  timeout

        # We use this lock primarily for the lock counter.
        SCREAMING_SNAKE_CASE_          =  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.
        SCREAMING_SNAKE_CASE_          =  0
        return None







  @property
  def    __A     (							self :       int   )  ->	Optional[Any]:
        return self._lock_file







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







  @timeout.setter
  def    __A     (							self :       List[str]  ,     __magic_name__ :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  float(__magic_name__   )
        return None







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        raise NotImplementedError()







  def    __A     (							self :       Optional[Any]   )  ->	int:
        raise NotImplementedError()







  @property
  def    __A     (							self :       Dict   )  ->	List[str]:
        return self._lock_file_fd is not None







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Optional[int]=0.05   )  ->	str:
        # Use the default timeout, if no timeout is provided.
        if timeout is None:
              SCREAMING_SNAKE_CASE_          =  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

        SCREAMING_SNAKE_CASE_          =  id(self   )
        SCREAMING_SNAKE_CASE_          =  self._lock_file
        SCREAMING_SNAKE_CASE_          =  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(__magic_name__   )
        except:  # noqa
              # Something did go wrong, so decrement the counter.
              with self._thread_lock:
                    SCREAMING_SNAKE_CASE_          =  max(0  ,     self._lock_counter - 1   )

              raise
        return _Acquire_ReturnProxy(lock=self   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       Tuple=False   )  ->	Optional[int]:
        with self._thread_lock:
              if self.is_locked:
                    self._lock_counter -= 1

                    if self._lock_counter == 0 or force:
                          SCREAMING_SNAKE_CASE_          =  id(self   )
                          SCREAMING_SNAKE_CASE_          =  self._lock_file

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

        return None







  def __enter__(							self :       Optional[Any]   )  ->	Tuple:
        self.acquire()
        return self







  def __exit__(							self :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       Union[str, Any]   )  ->	Union[str, Any]:
        self.release()
        return None







  def __del__(							self :       Optional[int]   )  ->	Optional[Any]:
        self.release(force=__magic_name__   )
        return None







  def    __A     (							self :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.path.basename(__magic_name__   )
        if len(__magic_name__   ) > max_length and max_length > 0:
              SCREAMING_SNAKE_CASE_          =  os.path.dirname(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  str(hash(__magic_name__   )   )
              SCREAMING_SNAKE_CASE_          =  filename[: max_length - len(__magic_name__   ) - 8] + "..." + hashed_filename + ".lock"
              return os.path.join(__magic_name__  ,     __magic_name__   )
        else:
              return path



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Optional[int]=None   )  ->	Dict:
        from .file_utils import relative_to_absolute_path

        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  "\\\\?\\" + relative_to_absolute_path(self.lock_file   )







  def    __A     (							self :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC

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







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        msvcrt.locking(__magic_name__  ,     msvcrt.LK_UNLCK  ,     1   )
        os.close(__magic_name__   )

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



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def __init__(							self :       Dict  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple=-1  ,     __magic_name__ :       Dict=None   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  os.statvfs(os.path.dirname(__magic_name__   )   ).f_namemax
        super().__init__(__magic_name__  ,     timeout=__magic_name__  ,     max_filename_length=__magic_name__   )







  def    __A     (							self :       int   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  os.O_RDWR | os.O_CREAT | os.O_TRUNC
        SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )

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







  def    __A     (							self :       List[str]   )  ->	Optional[Any]:
        # 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
        SCREAMING_SNAKE_CASE_          =  self._lock_file_fd
        SCREAMING_SNAKE_CASE_          =  None
        fcntl.flock(__magic_name__  ,     fcntl.LOCK_UN   )
        os.close(__magic_name__   )
        return None



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
        try:
              SCREAMING_SNAKE_CASE_          =  os.open(self._lock_file  ,     __magic_name__   )
        except OSError:
              pass
        else:
              SCREAMING_SNAKE_CASE_          =  fd
        return None







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        os.close(self._lock_file_fd   )
        SCREAMING_SNAKE_CASE_          =  None

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



A     :  List[str]					     =       None

if msvcrt:
      A     :  Tuple					     =       WindowsFileLock
elif fcntl:
      A     :  Union[str, Any]					     =       UnixFileLock
else:
      A     :  List[Any]					     =       SoftFileLock

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


