





import json
import os
import tempfile

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


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 				_lowerCAmelCase			(	_a :       int       , _a :       str=False       , _a :       Optional[Any]=False       , _a :       int=False      )						->      List[Any]:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     lowerCAmelCase_       :							List[str]			 =		[]
     for i in range(image_processor_tester.batch_size      ):
          if equal_resolution:
               lowerCAmelCase_       :							List[Any]			 =		image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               lowerCAmelCase_       :							Optional[int]			 =		image_processor_tester.min_resolution
               if getattr(__lowerCamelCase       , """size_divisor"""       , __lowerCamelCase      ):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    lowerCAmelCase_       :							str			 =		max(image_processor_tester.size_divisor       , __lowerCamelCase      )
               lowerCAmelCase_       :							Tuple			 =		np.random.choice(np.arange(__lowerCamelCase       , image_processor_tester.max_resolution      )       , 2      )
          image_inputs.append(
              np.random.randint(2_55       , size=(image_processor_tester.num_channels, width, height)       , dtype=np.uinta      )      )

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          lowerCAmelCase_       :							Dict			 =		[Image.fromarray(np.moveaxis(__lowerCamelCase       , 0       , -1      )      ) for image in image_inputs]

     if torchify:
          lowerCAmelCase_       :							Dict			 =		[torch.from_numpy(__lowerCamelCase      ) for image in image_inputs]

     return image_inputs






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]=10       , _a :       Any=10       , _a :       int=False       , _a :       List[str]=False      )						->      List[str]:
     lowerCAmelCase_       :							Tuple			 =		[]
     for i in range(image_processor_tester.num_frames      ):
          video.append(np.random.randint(2_55       , size=(image_processor_tester.num_channels, width, height)       , dtype=np.uinta      )      )

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          lowerCAmelCase_       :							Optional[int]			 =		[Image.fromarray(np.moveaxis(__lowerCamelCase       , 0       , -1      )      ) for frame in video]

     if torchify:
          lowerCAmelCase_       :							List[Any]			 =		[torch.from_numpy(__lowerCamelCase      ) for frame in video]

     return video






def 				_lowerCAmelCase			(	_a :       Any       , _a :       Dict=False       , _a :       Optional[int]=False       , _a :       Any=False      )						->      Union[str, Any]:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     lowerCAmelCase_       :							Dict			 =		[]
     for i in range(image_processor_tester.batch_size      ):
          if equal_resolution:
               lowerCAmelCase_       :							List[Any]			 =		image_processor_tester.max_resolution
          else:
               lowerCAmelCase_       :							Dict			 =		np.random.choice(
                   np.arange(image_processor_tester.min_resolution       , image_processor_tester.max_resolution      )       , 2      )
               lowerCAmelCase_       :							Union[str, Any]			 =		prepare_video(
                   image_processor_tester=__lowerCamelCase       , width=__lowerCamelCase       , height=__lowerCamelCase       , numpify=__lowerCamelCase       , torchify=__lowerCamelCase       , )
          video_inputs.append(__lowerCamelCase      )

     return video_inputs







class   lowercase__     :
  __UpperCamelCase						  =	None







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Union[str, Any]			 =		self.image_processing_class(**self.image_processor_dict    )
       lowerCAmelCase_       :							str			 =		json.loads(image_processor.to_json_string()    )
       for key, value in self.image_processor_dict.items():
            self.assertEqual(obj[key]     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Optional[Any]			 =		self.image_processing_class(**self.image_processor_dict    )

       with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCAmelCase_       :							Dict			 =		os.path.join(_lowercase     ,    """image_processor.json"""    )
            image_processor_first.to_json_file(_lowercase    )
            lowerCAmelCase_       :							Any			 =		self.image_processing_class.from_json_file(_lowercase    )

       self.assertEqual(image_processor_second.to_dict()     ,    image_processor_first.to_dict()    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							int			 =		self.image_processing_class(**self.image_processor_dict    )

       with tempfile.TemporaryDirectory() as tmpdirname:
            lowerCAmelCase_       :							Any			 =		image_processor_first.save_pretrained(_lowercase    )[0]
            check_json_file_has_correct_format(_lowercase    )
            lowerCAmelCase_       :							List[Any]			 =		self.image_processing_class.from_pretrained(_lowercase    )

       self.assertEqual(image_processor_second.to_dict()     ,    image_processor_first.to_dict()    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		self.image_processing_class()
       self.assertIsNotNone(_lowercase    )







  @require_torch
  @require_vision
  def 						UpperCAmelCase__		(						self    ):
       if self.test_cast_dtype is not None:
            # Initialize image_processor
            lowerCAmelCase_       :							Optional[Any]			 =		self.image_processing_class(**self.image_processor_dict    )

            # create random PyTorch tensors
            lowerCAmelCase_       :							int			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase     ,    torchify=_lowercase    )

            lowerCAmelCase_       :							Union[str, Any]			 =		image_processor(_lowercase     ,    return_tensors="""pt"""    )
            # for layoutLM compatiblity
            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.floataa    )

            lowerCAmelCase_       :							int			 =		image_processor(_lowercase     ,    return_tensors="""pt"""    ).to(torch.floataa    )
            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.floataa    )

            lowerCAmelCase_       :							Dict			 =		image_processor(_lowercase     ,    return_tensors="""pt"""    ).to("""cpu"""     ,    torch.bfloataa    )
            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.bfloataa    )

            with self.assertRaises(_lowercase    ):
                 lowerCAmelCase_       :							List[Any]			 =		image_processor(_lowercase     ,    return_tensors="""pt"""    ).to(torch.bfloataa     ,    """cpu"""    )

            # Try with text + image feature
            lowerCAmelCase_       :							Any			 =		image_processor(_lowercase     ,    return_tensors="""pt"""    )
            encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]    )}    )
            lowerCAmelCase_       :							Optional[int]			 =		encoding.to(torch.floataa    )

            self.assertEqual(encoding.pixel_values.device     ,    torch.device("""cpu"""    )    )
            self.assertEqual(encoding.pixel_values.dtype     ,    torch.floataa    )
            self.assertEqual(encoding.input_ids.dtype     ,    torch.long    )





from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_lowerCamelCase         =	TypeVar("""T""", bound=bool)

class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     T							=						value
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None





     @property
     def 						__snake_case     (							self			:				Dict					)->T:
      return self._value






     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				SkewNode[T] | None  ,	UpperCamelCase			:				SkewNode[T] | None					)->SkewNode[T] | None:
      if not roota:
       return roota

      if not roota:
       return roota

      if roota.value > roota.value:
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						roota, roota

      __SCREAMING_SNAKE_CASE							:     int							=						roota
      __SCREAMING_SNAKE_CASE							:     str							=						roota.right
      __SCREAMING_SNAKE_CASE							:     int							=						roota.left
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						SkewNode.merge(UpperCamelCase  ,	UpperCamelCase					)

      return result






class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				Iterable[T] | None = ()					)->None:
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      if data:
       for item in data:
        self.insert(UpperCamelCase					)





     def __bool__(							self			:				str					)->bool:
      return self._root is not None





     def __iter__(							self			:				Any					)->Iterator[T]:
      __SCREAMING_SNAKE_CASE							:     list[Any]							=						[]
      while self:
       result.append(self.pop()					)

      # Pushing items back to the heap not to clear it.
      for item in result:
       self.insert(UpperCamelCase					)

      return iter(UpperCamelCase					)





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     str							=						SkewNode.merge(self._root  ,	SkewNode(UpperCamelCase					)					)





     def 						__snake_case     (							self			:				List[Any]					)->T | None:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.top()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						(
          SkewNode.merge(self._root.left  ,	self._root.right					) if self._root else None
      )

      return result





     def 						__snake_case     (							self			:				Optional[int]					)->T:
      if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value






     def 						__snake_case     (							self			:				Dict					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						None


if __name__ == "__main__":
      import doctest

      doctest.testmod()






