

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    _snake_case		(snake_case_				:    Optional[Any]						,					snake_case_				:    Tuple=False						,					snake_case_				:    Dict=False						,					snake_case_				:    Optional[Any]=False     )       ->		List[str]:
  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

  if torchify:
    __UpperCAmelCase  :     Any		=    [torch.from_numpy(snake_case_     ) for image in image_inputs]

  return image_inputs





def    _snake_case		(snake_case_				:    Any						,					snake_case_				:    Tuple=10						,					snake_case_				:    List[Any]=10						,					snake_case_				:    List[Any]=False						,					snake_case_				:    Any=False     )       ->		Any:
  __UpperCAmelCase  :     Optional[int]		=    []
  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
    __UpperCAmelCase  :     str		=    [Image.fromarray(np.moveaxis(snake_case_						,					0						,					-1     )     ) for frame in video]

  if torchify:
    __UpperCAmelCase  :     Optional[Any]		=    [torch.from_numpy(snake_case_     ) for frame in video]

  return video





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

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

  return video_inputs







class     lowerCamelCase_   :
 _lowerCAmelCase       		=  None




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    self.image_processing_class(**self.image_processor_dict       )
   __UpperCAmelCase  :     Tuple		=    json.loads(image_processor.to_json_string()       )
   for key, value in self.image_processor_dict.items():
     self.assertEqual(obj[key]     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    self.image_processing_class(**self.image_processor_dict       )

   with tempfile.TemporaryDirectory() as tmpdirname:
     __UpperCAmelCase  :     Optional[int]		=    os.path.join(_UpperCAmelCase     ,     'image_processor.json'       )
     image_processor_first.to_json_file(_UpperCAmelCase       )
     __UpperCAmelCase  :     Any		=    self.image_processing_class.from_json_file(_UpperCAmelCase       )

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




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    self.image_processing_class(**self.image_processor_dict       )

   with tempfile.TemporaryDirectory() as tmpdirname:
     __UpperCAmelCase  :     Dict		=    image_processor_first.save_pretrained(_UpperCAmelCase       )[0]
     check_json_file_has_correct_format(_UpperCAmelCase       )
     __UpperCAmelCase  :     Union[str, Any]		=    self.image_processing_class.from_pretrained(_UpperCAmelCase       )

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




 def 						a					( self       ):
   __UpperCAmelCase  :     str		=    self.image_processing_class()
   self.assertIsNotNone(_UpperCAmelCase       )




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

     # create random PyTorch tensors
     __UpperCAmelCase  :     Optional[int]		=    prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=_UpperCAmelCase     ,     torchify=_UpperCAmelCase       )

     __UpperCAmelCase  :     List[Any]		=    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       )

     __UpperCAmelCase  :     Dict		=    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       )

     __UpperCAmelCase  :     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       ):
       __UpperCAmelCase  :     Tuple		=    image_processor(_UpperCAmelCase     ,     return_tensors='pt'       ).to(torch.bfloataa     ,     'cpu'       )

     # Try with text + image feature
     __UpperCAmelCase  :     Any		=    image_processor(_UpperCAmelCase     ,     return_tensors='pt'       )
     encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]       )}       )
     __UpperCAmelCase  :     List[Any]		=    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
from dataclasses import dataclass
from typing import Any


class     lowerCamelCase_   :
 _lowerCAmelCase       		=  ('''_front''', '''_back''', '''_len''')


 @dataclass
 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None



 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  ('''_cur''',)




  def __init__( self     ,     _UpperCAmelCase       ):
    __UpperCAmelCase  :     Tuple		=    cur




  def __iter__( self       ):
    return self




  def 						a					( self       ):
    if self._cur is None:
      # finished iterating
      raise StopIteration
    __UpperCAmelCase  :     str		=    self._cur.val
    __UpperCAmelCase  :     Dict		=    self._cur.next_node

    return val




 def __init__( self     ,     _UpperCAmelCase = None       ):
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     int		=    0

   if iterable is not None:
     # append every value to the deque
     for val in iterable:
       self.append(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     Dict		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Tuple		=    self._back
     __UpperCAmelCase  :     Optional[Any]		=    node  # assign new back to the new node

     self._len += 1

     # make sure there were no errors
     assert not self.is_empty(), "Error on appending value."




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     str		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     List[str]		=    self._front
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Dict		=    node  # assign new front to the new node

     self._len += 1

     # make sure there were no errors
     assert not self.is_empty(), "Error on appending value."




 def 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.append(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.appendleft(_UpperCAmelCase       )




 def 						a					( self       ):
   assert not self.is_empty(), "Deque is empty."

   __UpperCAmelCase  :     List[Any]		=    self._back
   __UpperCAmelCase  :     Optional[int]		=    self._back.prev_node  # set new back
   # drop the last node - python will deallocate memory automatically
   __UpperCAmelCase  :     int		=    None

   self._len -= 1

   return topop.val




 def 						a					( self       ):
   assert not self.is_empty(), "Deque is empty."

   __UpperCAmelCase  :     List[Any]		=    self._front
   __UpperCAmelCase  :     Union[str, Any]		=    self._front.next_node  # set new front and drop the first node
   __UpperCAmelCase  :     Union[str, Any]		=    None

   self._len -= 1

   return topop.val




 def 						a					( self       ):
   return self._front is None




 def __len__( self       ):
   return self._len




 def __eq__( self     ,     _UpperCAmelCase       ):

   if not isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     return NotImplemented

   __UpperCAmelCase  :     List[str]		=    self._front
   __UpperCAmelCase  :     str		=    other._front

   # if the length of the dequeues are not the same, they are not equal
   if len(self       ) != len(_UpperCAmelCase       ):
     return False

   while me is not None and oth is not None:
     # compare every value
     if me.val != oth.val:
       return False
     __UpperCAmelCase  :     Any		=    me.next_node
     __UpperCAmelCase  :     str		=    oth.next_node

   return True




 def __iter__( self       ):
   return Deque._Iterator(self._front       )




 def __repr__( self       ):
   __UpperCAmelCase  :     int		=    []
   __UpperCAmelCase  :     Dict		=    self._front
   while aux is not None:
     # append the values in a list to display
     values_list.append(aux.val       )
     __UpperCAmelCase  :     List[str]		=    aux.next_node

   return F"[{', '.join(repr(_UpperCAmelCase       ) for val in values_list       )}]"


if __name__ == "__main__":
      import doctest

      doctest.testmod()





