




'''simple docstring'''







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 			_a  (	__a ):
       __a				:     List[str]   				=					["""image_processor""", """tokenizer"""]
       __a				:     List[str]   				=					"""BlipImageProcessor"""
       __a				:     Union[str, Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Any   ,				lowercase						:	List[str]   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		False
           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	ImageInput = None   ,				lowercase						:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,				lowercase						:	bool = True   ,				lowercase						:	Union[bool, str, PaddingStrategy] = False   ,				lowercase						:	Union[bool, str, TruncationStrategy] = None   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	int = 0   ,				lowercase						:	Optional[int] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = False   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				**lowercase						:	Any   ,				):



           '''simple docstring'''

           if images is None and text is None:
               raise ValueError('''You have to specify either images or text.'''				)

           # Get only text
           if images is None:
               UpperCAmelCase			       =		self.tokenizer
               UpperCAmelCase			       =		self.tokenizer(
                   text=lowercase   ,				add_special_tokens=lowercase   ,				padding=lowercase   ,				truncation=lowercase   ,				max_length=lowercase   ,				stride=lowercase   ,				pad_to_multiple_of=lowercase   ,				return_attention_mask=lowercase   ,				return_overflowing_tokens=lowercase   ,				return_special_tokens_mask=lowercase   ,				return_offsets_mapping=lowercase   ,				return_token_type_ids=lowercase   ,				return_length=lowercase   ,				verbose=lowercase   ,				return_tensors=lowercase   ,				**lowercase   ,				)
               return text_encoding

           # add pixel_values
           UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase				)

           if text is not None:
               UpperCAmelCase			       =		self.tokenizer(
                   text=lowercase   ,				add_special_tokens=lowercase   ,				padding=lowercase   ,				truncation=lowercase   ,				max_length=lowercase   ,				stride=lowercase   ,				pad_to_multiple_of=lowercase   ,				return_attention_mask=lowercase   ,				return_overflowing_tokens=lowercase   ,				return_special_tokens_mask=lowercase   ,				return_offsets_mapping=lowercase   ,				return_token_type_ids=lowercase   ,				return_length=lowercase   ,				verbose=lowercase   ,				return_tensors=lowercase   ,				**lowercase   ,				)
           else:
               UpperCAmelCase			       =		None

           if text_encoding is not None:
               encoding_image_processor.update(lowercase				)

           return encoding_image_processor
       def     A      (	self						:	Union[str, Any]   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Dict   ,				*lowercase						:	List[str]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)







       @property
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)





'''simple docstring'''




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 MobileViTImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[Any]						,					A						:				Dict						,					A						:				Dict=7						,					A						:				Optional[int]=3						,					A						:				Optional[int]=18						,					A						:				Dict=30						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=True						,					A						:				Tuple=None						,					A						:				List[Any]=True						,					A						:				int=None						,					A						:				Optional[int]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    size if size is not None else {"shortest_edge": 20}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}
             _UpperCAmelCase     :  List[Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  Optional[Any]							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    image_size
             _UpperCAmelCase     :  int							      =    min_resolution
             _UpperCAmelCase     :  Optional[int]							      =    max_resolution
             _UpperCAmelCase     :  List[str]							      =    do_resize
             _UpperCAmelCase     :  Optional[Any]							      =    size
             _UpperCAmelCase     :  Tuple							      =    do_center_crop
             _UpperCAmelCase     :  Optional[int]							      =    crop_size
             _UpperCAmelCase     :  Optional[Any]							      =    do_flip_channel_order





      def      _A	(       self						:				Dict       ):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Tuple						=						MobileViTImageProcessor if is_vision_available() else None




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Any							      =    MobileViTImageProcessingTester(self       )




      @property
      def      _A	(       self						:				int       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  int							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"center_crop"       )       )
             self.assertTrue(hasattr(A						,					"do_flip_channel_order"       )       )




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 20}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Dict							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Any       ):
             pass




      def      _A	(       self						:				Dict       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _UpperCAmelCase     :  Union[str, Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  List[Any]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




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

             # Test not batched input
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				Any       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Any							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[Any]							      =    prepare_image_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for image in image_inputs:
                    self.assertIsInstance(A						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  List[str]							      =    image_processing(image_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Any							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_images.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

