






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

   lowercase__    :						List[str]       =			[]
   for i in range(image_processor_tester.batch_size       ):
      if equal_resolution:
         lowercase__    :						Any       =			image_processor_tester.max_resolution
      else:
         # To avoid getting image width/height 0
         lowercase__    :						List[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`
            lowercase__    :						Tuple       =			max(image_processor_tester.size_divisor		,					A       )
         lowercase__  ,       lowercase__    :						Tuple       =			np.random.choice(np.arange(A		,					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__    :						Union[str, Any]       =			[Image.fromarray(np.moveaxis(A		,					0		,					-1       )       ) for image in image_inputs]

   if torchify:
      lowercase__    :						Optional[Any]       =			[torch.from_numpy(A       ) for image in image_inputs]

   return image_inputs
def    a__    (	A		,					A=10		,					A=10		,					A=False		,					A=False       ):
   lowercase__    :						Optional[Any]       =			[]
   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__    :						List[str]       =			[Image.fromarray(np.moveaxis(A		,					0		,					-1       )       ) for frame in video]

   if torchify:
      lowercase__    :						str       =			[torch.from_numpy(A       ) for frame in video]

   return video
def    a__    (	A		,					A=False		,					A=False		,					A=False       ):
   assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

   return video_inputs






class 							__snake_case:
 lowercase			:							Any		   =		None



 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






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



 def     UpperCAmelCase     (						self : Optional[int]						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
       lowercase__    :						Union[str, Any]       =			os.path.join(UpperCAmelCase__			,		"""image_processor.json"""						)
       image_processor_first.to_json_file(UpperCAmelCase__						)
       lowercase__    :						Any       =			self.image_processing_class.from_json_file(UpperCAmelCase__						)

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



 def     UpperCAmelCase     (						self : Tuple						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict						)

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

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



 def     UpperCAmelCase     (						self : Any						):

    """simple docstring"""






    lowercase__    :						Tuple       =			self.image_processing_class()
    self.assertIsNotNone(UpperCAmelCase__						)







 @require_torch
 @require_vision
 def     UpperCAmelCase     (						self : Optional[Any]						):

    """simple docstring"""






    if self.test_cast_dtype is not None:
       # Initialize image_processor
       lowercase__    :						int       =			self.image_processing_class(**self.image_processor_dict						)

       # create random PyTorch tensors
       lowercase__    :						Optional[Any]       =			prepare_image_inputs(self.image_processor_tester			,		equal_resolution=UpperCAmelCase__			,		torchify=UpperCAmelCase__						)

       lowercase__    :						int       =			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__    :						int       =			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__    :						Tuple       =			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__    :						int       =			image_processor(UpperCAmelCase__			,		return_tensors="""pt"""						).to(torch.bfloataa			,		"""cpu"""						)

       # Try with text + image feature
       lowercase__    :						List[Any]       =			image_processor(UpperCAmelCase__			,		return_tensors="""pt"""						)
       encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]						)}						)
       lowercase__    :						List[str]       =			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 typing import Any
class 							__snake_case:



 def __init__(						self : str			,		UpperCAmelCase__ : Any			,		UpperCAmelCase__ : Any						):  # noqa: A002

    """simple docstring"""






    lowercase__    :						Optional[int]       =			item
    lowercase__    :						Union[str, Any]       =			next
class 							__snake_case:



 def __init__(						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Node | None       =			None
    lowercase__    :						Optional[Any]       =			0



 def     UpperCAmelCase     (						self : List[Any]			,		UpperCAmelCase__ : Any						):

    """simple docstring"""






    lowercase__    :						Any       =			Node(UpperCAmelCase__			,		self.head						)
    self.size += 1



 def     UpperCAmelCase     (						self : Dict						):

    """simple docstring"""






    if self.head is None:
       return None
    else:
       lowercase__    :						List[Any]       =			self.head.item
       lowercase__    :						Optional[Any]       =			self.head.next
       self.size -= 1
       return item



 def     UpperCAmelCase     (						self : Union[str, Any]						):

    """simple docstring"""






    return self.head is None



 def __str__(						self : List[str]						):

    """simple docstring"""






    if self.is_empty():
       return ""
    else:
       lowercase__    :						Optional[Any]       =			self.head
       lowercase__    :						str       =			""""""
       lowercase__    :						list[str]       =			[]
       while iterate:
          item_list.append(str(iterate.item						)						)
          lowercase__    :						List[str]       =			iterate.next

       lowercase__    :						List[str]       =			""" --> """.join(UpperCAmelCase__						)

       return item_str







 def __len__(						self : List[Any]						):

    """simple docstring"""






    return self.size


