
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			,   _a=False			,   _a=False			,   _a=False  ):
  """simple docstring"""




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

  _lowerCamelCase      =						[]
  for i in range(image_processor_tester.batch_size  ):
    if equal_resolution:
      _lowerCamelCase      =						_lowerCamelCase      =						image_processor_tester.max_resolution
    else:
      # To avoid getting image width/height 0
      _lowerCamelCase      =						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`
        _lowerCamelCase      =						max(image_processor_tester.size_divisor			,   _a  )
      _lowerCamelCase    ,    _lowerCamelCase      =						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
    _lowerCamelCase      =						[Image.fromarray(np.moveaxis(_a			,   0			,   -1  )  ) for image in image_inputs]

  if torchify:
    _lowerCamelCase      =						[torch.from_numpy(_a  ) for image in image_inputs]

  return image_inputs


def 		_lowerCamelCase       (   _a			,   _a=1_0			,   _a=1_0			,   _a=False			,   _a=False  ):
  """simple docstring"""




  _lowerCamelCase      =						[]
  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
    _lowerCamelCase      =						[Image.fromarray(np.moveaxis(_a			,   0			,   -1  )  ) for frame in video]

  if torchify:
    _lowerCamelCase      =						[torch.from_numpy(_a  ) for frame in video]

  return video


def 		_lowerCamelCase       (   _a			,   _a=False			,   _a=False			,   _a=False  ):
  """simple docstring"""




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

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

  return video_inputs






class 		__magic_name__				:


  """simple docstring"""





  _UpperCamelCase						 = None


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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)

    with tempfile.TemporaryDirectory() as tmpdirname:
      _lowerCamelCase      =						os.path.join(a__	,		'''image_processor.json'''		)
      image_processor_first.to_json_file(a__		)
      _lowerCamelCase      =						self.image_processing_class.from_json_file(a__		)

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class(**self.image_processor_dict		)

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

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


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.image_processing_class()
    self.assertIsNotNone(a__		)


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

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

      _lowerCamelCase      =						image_processor(a__	,		return_tensors='''pt'''		)
      # for layoutLM compatiblity
      self.assertEqual(encoding.pixel_values.device	,		torch.device('''cpu'''		)		)
      self.assertEqual(encoding.pixel_values.dtype	,		torch.floataa		)

      _lowerCamelCase      =						image_processor(a__	,		return_tensors='''pt'''		).to(torch.floataa		)
      self.assertEqual(encoding.pixel_values.device	,		torch.device('''cpu'''		)		)
      self.assertEqual(encoding.pixel_values.dtype	,		torch.floataa		)

      _lowerCamelCase      =						image_processor(a__	,		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(a__		):
        _lowerCamelCase      =						image_processor(a__	,		return_tensors='''pt'''		).to(torch.bfloataa	,		'''cpu'''		)

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






import warnings

from ...utils import logging
from .image_processing_conditional_detr import ConditionalDetrImageProcessor


_UpperCAmelCase    			=					logging.get_logger(__name__)

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  def __init__(					self	,		*a__	,		**a__		):
    warnings.warn(
        '''The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use ConditionalDetrImageProcessor instead.'''	,		a__	,		)
    super().__init__(*a__	,		**a__		)




