

"""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     _A    (    UpperCamelCase_	:			Dict,     UpperCamelCase_	:			Optional[int]=False,     UpperCamelCase_	:			Union[str, Any]=False,     UpperCamelCase_	:			List[str]=False)      ->		List[str]:
   '''simple docstring'''


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

   __lowercase								=		[]
   for i in range(image_processor_tester.batch_size):
      if equal_resolution:
         __lowercase								=		__lowercase								=		image_processor_tester.max_resolution
      else:
         # To avoid getting image width/height 0
         __lowercase								=		image_processor_tester.min_resolution
         if getattr(UpperCamelCase_,     "size_divisor",     UpperCamelCase_):
            # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
            __lowercase								=		max(image_processor_tester.size_divisor,     UpperCamelCase_)
         __lowercase						,__lowercase								=		np.random.choice(np.arange(UpperCamelCase_,     image_processor_tester.max_resolution),     2)
      image_inputs.append(
          np.random.randint(255,     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
      __lowercase								=		[Image.fromarray(np.moveaxis(UpperCamelCase_,     0,     -1)) for image in image_inputs]

   if torchify:
      __lowercase								=		[torch.from_numpy(UpperCamelCase_) for image in image_inputs]

   return image_inputs




def     _A    (    UpperCamelCase_	:			Optional[Any],     UpperCamelCase_	:			Union[str, Any]=10,     UpperCamelCase_	:			List[Any]=10,     UpperCamelCase_	:			str=False,     UpperCamelCase_	:			Union[str, Any]=False)      ->		Dict:
   '''simple docstring'''


   __lowercase								=		[]
   for i in range(image_processor_tester.num_frames):
      video.append(np.random.randint(255,     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
      __lowercase								=		[Image.fromarray(np.moveaxis(UpperCamelCase_,     0,     -1)) for frame in video]

   if torchify:
      __lowercase								=		[torch.from_numpy(UpperCamelCase_) for frame in video]

   return video




def     _A    (    UpperCamelCase_	:			Optional[Any],     UpperCamelCase_	:			int=False,     UpperCamelCase_	:			Optional[Any]=False,     UpperCamelCase_	:			Optional[int]=False)      ->		List[Any]:
   '''simple docstring'''


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

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

   return video_inputs




class    _lowerCAmelCase    :







 """simple docstring"""







 __UpperCAmelCase      :   List[str]  						=      None


 def    _lowercase					(     self				:     Optional[int]      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )
    __lowercase								=		json.loads(image_processor.to_json_string()      )
    for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key],   UpperCAmelCase__      )


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       __lowercase								=		os.path.join(UpperCAmelCase__,   "image_processor.json"      )
       image_processor_first.to_json_file(UpperCAmelCase__      )
       __lowercase								=		self.image_processing_class.from_json_file(UpperCAmelCase__      )

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


 def    _lowercase					(     self				:     Any      ):
    __lowercase								=		self.image_processing_class(**self.image_processor_dict      )

    with tempfile.TemporaryDirectory() as tmpdirname:
       __lowercase								=		image_processor_first.save_pretrained(UpperCAmelCase__      )[0]
       check_json_file_has_correct_format(UpperCAmelCase__      )
       __lowercase								=		self.image_processing_class.from_pretrained(UpperCAmelCase__      )

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


 def    _lowercase					(     self				:     Union[str, Any]      ):
    __lowercase								=		self.image_processing_class()
    self.assertIsNotNone(UpperCAmelCase__      )



 @require_torch
 @require_vision
 def    _lowercase					(     self				:     Any      ):
    if self.test_cast_dtype is not None:
       # Initialize image_processor
       __lowercase								=		self.image_processing_class(**self.image_processor_dict      )

       # create random PyTorch tensors
       __lowercase								=		prepare_image_inputs(self.image_processor_tester,   equal_resolution=UpperCAmelCase__,   torchify=UpperCAmelCase__      )

       __lowercase								=		image_processor(UpperCAmelCase__,   return_tensors="pt"      )
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device,   torch.device("cpu"      )      )
       self.assertEqual(encoding.pixel_values.dtype,   torch.floataa      )

       __lowercase								=		image_processor(UpperCAmelCase__,   return_tensors="pt"      ).to(torch.floataa      )
       self.assertEqual(encoding.pixel_values.device,   torch.device("cpu"      )      )
       self.assertEqual(encoding.pixel_values.dtype,   torch.floataa      )

       __lowercase								=		image_processor(UpperCAmelCase__,   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(UpperCAmelCase__      ):
          __lowercase								=		image_processor(UpperCAmelCase__,   return_tensors="pt"      ).to(torch.bfloataa,   "cpu"      )

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





def     _A    (    UpperCamelCase_	:			str)      ->		int:
   '''simple docstring'''


   __lowercase								=		0
   for plain_chr in plain_text:
      __lowercase								=		(
          ord(UpperCamelCase_) + (hash_value << 6) + (hash_value << 16) - hash_value
      )
   return hash_value




