






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 GLPNImageProcessor




class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def __init__(   self    ,     _lowerCamelCase    ,     _lowerCamelCase=7    ,     _lowerCamelCase=3    ,     _lowerCamelCase=1_8    ,     _lowerCamelCase=3_0    ,     _lowerCamelCase=4_0_0    ,     _lowerCamelCase=True    ,     _lowerCamelCase=3_2    ,     _lowerCamelCase=True    ,     ):
         UpperCamelCase_:   Any        =							parent
         UpperCamelCase_:   Optional[Any]        =							batch_size
         UpperCamelCase_:   List[str]        =							num_channels
         UpperCamelCase_:   Optional[Any]        =							image_size
         UpperCamelCase_:   Optional[int]        =							min_resolution
         UpperCamelCase_:   List[str]        =							max_resolution
         UpperCamelCase_:   Tuple        =							do_resize
         UpperCamelCase_:   Union[str, Any]        =							size_divisor
         UpperCamelCase_:   Optional[int]        =							do_rescale





       def      _a       (   self				):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







@require_torch
@require_vision
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						str										=GLPNImageProcessor if is_vision_available() else None





       def      _a       (   self				):
         UpperCamelCase_:   Tuple        =							GLPNImageProcessingTester(self				)





       @property
       def      _a       (   self				):
         return self.image_processor_tester.prepare_image_processor_dict()





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'do_resize'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'size_divisor'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'resample'				)				)
         self.assertTrue(hasattr(_lowerCamelCase    ,     'do_rescale'				)				)





       def      _a       (   self				):
         pass





       def      _a       (   self				):
         # Initialize image_processing
         UpperCamelCase_:   int        =							self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCamelCase_:   List[Any]        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     Image.Image				)

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         UpperCamelCase_:   List[Any]        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0				)
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0				)





       def      _a       (   self				):
         # Initialize image_processing
         UpperCamelCase_:   Union[str, Any]        =							self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCamelCase_:   Dict        =							prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=_lowerCamelCase    ,     numpify=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     np.ndarray				)

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         UpperCamelCase_:   Tuple        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0				)
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0				)





       def      _a       (   self				):
         # Initialize image_processing
         UpperCamelCase_:   Optional[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=_lowerCamelCase    ,     torchify=_lowerCamelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCamelCase    ,     torch.Tensor				)

         # Test not batched input (GLPNImageProcessor doesn't support batching)
         UpperCamelCase_:   str        =							image_processing(image_inputs[0]    ,     return_tensors='pt'				).pixel_values
         self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0				)
         self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0				)




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Optional[Any]      						=					['''image_processor''', '''tokenizer''']
 _lowercase   :    Optional[int]      						=					'''ChineseCLIPImageProcessor'''
 _lowercase   :    List[Any]      						=					('''BertTokenizer''', '''BertTokenizerFast''')

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= None
  if "feature_extractor" in kwargs:
   warnings.warn(
       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
       ''' instead.'''		,				SCREAMING_SNAKE_CASE		,				)
   a__												= kwargs.pop('''feature_extractor'''      )

  a__												= 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__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor

 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:

  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:
   a__												= self.tokenizer(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if images is not None:
   a__												= self.image_processor(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

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

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  return self.tokenizer.decode(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

 @property
 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= self.tokenizer.model_input_names
  a__												= 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.'''		,				SCREAMING_SNAKE_CASE		,				)
  return self.image_processor_class





