



'''simple docstring'''





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     UpperCAmelCase     (    a_      ,		a_=False      ,		a_=False      ,		a_=False		)   ->       Union[str, Any]:



   """simple docstring"""






   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

   A_  :		Union[str, Any]						=					[]
   for i in range(image_processor_tester.batch_size		):
      if equal_resolution:
         A_  :		List[Any]						=					image_processor_tester.max_resolution
      else:
         # To avoid getting image width/height 0
         A_  :		Any						=					image_processor_tester.min_resolution
         if getattr(a_      ,		"""size_divisor"""      ,		a_		):
            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
            A_  :		List[Any]						=					max(image_processor_tester.size_divisor      ,		a_		)
         A_						,  A_  :		int						=					np.random.choice(np.arange(a_      ,		image_processor_tester.max_resolution		)      ,		2		)
      image_inputs.append(
          np.random.randint(2_5_5      ,		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
      A_  :		Optional[int]						=					[Image.fromarray(np.moveaxis(a_      ,		0      ,		-1		)		) for image in image_inputs]

   if torchify:
      A_  :		int						=					[torch.from_numpy(a_		) for image in image_inputs]

   return image_inputs



def     UpperCAmelCase     (    a_      ,		a_=1_0      ,		a_=1_0      ,		a_=False      ,		a_=False		)   ->       Any:



   """simple docstring"""






   A_  :		List[Any]						=					[]
   for i in range(image_processor_tester.num_frames		):
      video.append(np.random.randint(2_5_5      ,		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
      A_  :		int						=					[Image.fromarray(np.moveaxis(a_      ,		0      ,		-1		)		) for frame in video]

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

   return video



def     UpperCAmelCase     (    a_      ,		a_=False      ,		a_=False      ,		a_=False		)   ->       Optional[int]:



   """simple docstring"""






   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

   return video_inputs







class 							_lowerCAmelCase	:


 """simple docstring"""

 lowerCamelCase									=     None







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		Optional[int]						=					self.image_processing_class(**self.image_processor_dict  )
    A_  :		Tuple						=					json.loads(image_processor.to_json_string()  )
    for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[Any]						=					self.image_processing_class(**self.image_processor_dict  )

    with tempfile.TemporaryDirectory() as tmpdirname:
       A_  :		List[Any]						=					os.path.join(_lowerCamelCase					,    """image_processor.json"""  )
       image_processor_first.to_json_file(_lowerCamelCase  )
       A_  :		List[Any]						=					self.image_processing_class.from_json_file(_lowerCamelCase  )

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







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    A_  :		int						=					self.image_processing_class(**self.image_processor_dict  )

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

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







 def       UpperCAmelCase_       (      self  )    ->    str:
    A_  :		int						=					self.image_processing_class()
    self.assertIsNotNone(_lowerCamelCase  )







 @require_torch
 @require_vision
 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    if self.test_cast_dtype is not None:
       # Initialize image_processor
       A_  :		Any						=					self.image_processing_class(**self.image_processor_dict  )

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

       A_  :		Any						=					image_processor(_lowerCamelCase					,    return_tensors="""pt"""  )
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device					,    torch.device("""cpu"""  )  )
       self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa  )

       A_  :		List[str]						=					image_processor(_lowerCamelCase					,    return_tensors="""pt"""  ).to(torch.floataa  )
       self.assertEqual(encoding.pixel_values.device					,    torch.device("""cpu"""  )  )
       self.assertEqual(encoding.pixel_values.dtype					,    torch.floataa  )

       A_  :		int						=					image_processor(_lowerCamelCase					,    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(_lowerCamelCase  ):
          A_  :		List[str]						=					image_processor(_lowerCamelCase					,    return_tensors="""pt"""  ).to(torch.bfloataa					,    """cpu"""  )

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





'''simple docstring'''





from ..utils import DummyObject, requires_backends


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Optional[int]:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )


class 							_lowerCAmelCase	(  metaclass=__A							):


 """simple docstring"""

 lowerCamelCase									=     ['''torch''', '''librosa''']







 def __init__(      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(self					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    str:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )







 @classmethod
 def       UpperCAmelCase_       (      cls					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    Dict:
    requires_backends(cls					,    ["""torch""", """librosa"""]  )
