
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor

class    _a				(unittest.TestCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__=13       ,							A__=3       ,							A__=2_24       ,							A__=30       ,							A__=4_00       ,							A__=True       ,							A__=None       ,							A__=True       ,							A__=[0.5, 0.5, 0.5]       ,							A__=[0.5, 0.5, 0.5]       ,							)    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE      =				size if size is not None else {"""height""": 18, """width""": 18}
          _SCREAMING_SNAKE_CASE      =				parent
          _SCREAMING_SNAKE_CASE      =				batch_size
          _SCREAMING_SNAKE_CASE      =				num_channels
          _SCREAMING_SNAKE_CASE      =				image_size
          _SCREAMING_SNAKE_CASE      =				min_resolution
          _SCREAMING_SNAKE_CASE      =				max_resolution
          _SCREAMING_SNAKE_CASE      =				do_resize
          _SCREAMING_SNAKE_CASE      =				size
          _SCREAMING_SNAKE_CASE      =				do_normalize
          _SCREAMING_SNAKE_CASE      =				image_mean
          _SCREAMING_SNAKE_CASE      =				image_std






       def        UpperCamelCase				(							self      )    ->			List[str]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }






@require_torch
@require_vision
class    _a				(_lowerCamelCase  ,					unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ViTImageProcessor if is_vision_available() else None






       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          _SCREAMING_SNAKE_CASE      =				EfficientFormerImageProcessorTester(self      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[str]:
          return self.image_proc_tester.prepare_image_processor_dict()






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(A__       ,							"""image_mean"""      )      )
          self.assertTrue(hasattr(A__       ,							"""image_std"""      )      )
          self.assertTrue(hasattr(A__       ,							"""do_normalize"""      )      )
          self.assertTrue(hasattr(A__       ,							"""do_resize"""      )      )
          self.assertTrue(hasattr(A__       ,							"""size"""      )      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          pass






       def        UpperCamelCase				(							self      )    ->			int:
          # Initialize image_processor
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          _SCREAMING_SNAKE_CASE      =				prepare_image_inputs(self.image_proc_tester       ,							equal_resolution=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							Image.Image      )

          # Test not batched input
          _SCREAMING_SNAKE_CASE      =				image_processor(image_inputs[0]       ,							return_tensors="""pt"""      ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,							(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["""height"""],
                  self.image_proc_tester.size["""width"""],
              )       ,							)

          # Test batched
          _SCREAMING_SNAKE_CASE      =				image_processor(A__       ,							return_tensors="""pt"""      ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,							(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["""height"""],
                  self.image_proc_tester.size["""width"""],
              )       ,							)






       def        UpperCamelCase				(							self      )    ->			Dict:
          # Initialize image_processor
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          _SCREAMING_SNAKE_CASE      =				prepare_image_inputs(self.image_proc_tester       ,							equal_resolution=A__       ,							numpify=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							np.ndarray      )

          # Test not batched input
          _SCREAMING_SNAKE_CASE      =				image_processor(image_inputs[0]       ,							return_tensors="""pt"""      ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,							(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["""height"""],
                  self.image_proc_tester.size["""width"""],
              )       ,							)

          # Test batched
          _SCREAMING_SNAKE_CASE      =				image_processor(A__       ,							return_tensors="""pt"""      ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,							(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["""height"""],
                  self.image_proc_tester.size["""width"""],
              )       ,							)






       def        UpperCamelCase				(							self      )    ->			int:
          # Initialize image_processor
          _SCREAMING_SNAKE_CASE      =				self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          _SCREAMING_SNAKE_CASE      =				prepare_image_inputs(self.image_proc_tester       ,							equal_resolution=A__       ,							torchify=A__      )
          for image in image_inputs:
             self.assertIsInstance(A__       ,							torch.Tensor      )

          # Test not batched input
          _SCREAMING_SNAKE_CASE      =				image_processor(image_inputs[0]       ,							return_tensors="""pt"""      ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,							(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["""height"""],
                  self.image_proc_tester.size["""width"""],
              )       ,							)

          # Test batched
          _SCREAMING_SNAKE_CASE      =				image_processor(A__       ,							return_tensors="""pt"""      ).pixel_values
          self.assertEqual(
              encoded_images.shape       ,							(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["""height"""],
                  self.image_proc_tester.size["""width"""],
              )       ,							)








'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ['image_processor', 'tokenizer']
       SCREAMING_SNAKE_CASE      =  'ChineseCLIPImageProcessor'
       SCREAMING_SNAKE_CASE      =  ('BertTokenizer', 'BertTokenizerFast')






       def __init__(							self       ,							A__=None       ,							A__=None       ,							**A__      )    ->			int:
          _SCREAMING_SNAKE_CASE      =				None
          if "feature_extractor" in kwargs:
             warnings.warn(
                 """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                 """ instead."""       ,							A__       ,							)
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""feature_extractor"""      )

          _SCREAMING_SNAKE_CASE      =				image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
             raise ValueError("""You need to specify an `image_processor`."""      )
          if tokenizer is None:
             raise ValueError("""You need to specify a `tokenizer`."""      )

          super().__init__(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				self.image_processor






       def __call__(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:

          if text is None and images is None:
             raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

          if text is not None:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer(A__       ,							return_tensors=A__       ,							**A__      )

          if images is not None:
             _SCREAMING_SNAKE_CASE      =				self.image_processor(A__       ,							return_tensors=A__       ,							**A__      )

          if text is not None and images is not None:
             _SCREAMING_SNAKE_CASE      =				image_features.pixel_values
             return encoding
          elif text is not None:
             return encoding
          else:
             return BatchEncoding(data=dict(**A__      )       ,							tensor_type=A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Dict:
          return self.tokenizer.batch_decode(*A__       ,							**A__      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			Optional[Any]:
          return self.tokenizer.decode(*A__       ,							**A__      )






       @property
       def        UpperCamelCase				(							self      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.tokenizer.model_input_names
          _SCREAMING_SNAKE_CASE      =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       @property
       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							A__       ,							)
          return self.image_processor_class






