'''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 PoolFormerImageProcessor







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			Dict, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Dict=7, UpperCamelCase__      :			str=3, UpperCamelCase__      :			List[Any]=30, UpperCamelCase__      :			List[str]=4_00, UpperCamelCase__      :			str=True, UpperCamelCase__      :			Dict=None, UpperCamelCase__      :			Union[str, Any]=0.9, UpperCamelCase__      :			List[Any]=None, UpperCamelCase__      :			Union[str, Any]=True, UpperCamelCase__      :			Any=[0.5, 0.5, 0.5], UpperCamelCase__      :			List[str]=[0.5, 0.5, 0.5], )  ->			Tuple:
        _A    		=  size if size is not None else {'shortest_edge': 30}
        _A    		=  crop_size if crop_size is not None else {'height': 30, 'width': 30}
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  num_channels
        _A    		=  min_resolution
        _A    		=  max_resolution
        _A    		=  do_resize_and_center_crop
        _A    		=  size
        _A    		=  crop_pct
        _A    		=  crop_size
        _A    		=  do_normalize
        _A    		=  image_mean
        _A    		=  image_std

   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Union[str, Any]:
        return {
            "size": self.size,
            "do_resize_and_center_crop": self.do_resize_and_center_crop,
            "crop_pct": self.crop_pct,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }







@require_torch
@require_vision
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   PoolFormerImageProcessor if is_vision_available() else None
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			int:
        _A    		=  PoolFormerImageProcessingTester(self     )
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Any:
        return self.image_processor_tester.prepare_image_processor_dict()
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[int]:
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(UpperCamelCase__, 'do_resize_and_center_crop'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'size'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'crop_pct'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'do_normalize'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'image_mean'     )     )
        self.assertTrue(hasattr(UpperCamelCase__, 'image_std'     )     )
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        _A    		=  self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size, {'shortest_edge': 30}     )
        self.assertEqual(image_processor.crop_size, {'height': 30, 'width': 30}     )

        _A    		=  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 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			str:
        pass
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Optional[int]:
        # Initialize image_processing
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        # create random PIL images
        _A    		=  prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCamelCase__     )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__, Image.Image     )

        # Test not batched input
        _A    		=  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
        _A    		=  image_processing(UpperCamelCase__, 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 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        # Initialize image_processing
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        _A    		=  prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCamelCase__, numpify=UpperCamelCase__     )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__, np.ndarray     )

        # Test not batched input
        _A    		=  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
        _A    		=  image_processing(UpperCamelCase__, 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 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[int]:
        # Initialize image_processing
        _A    		=  self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        _A    		=  prepare_image_inputs(self.image_processor_tester, equal_resolution=UpperCamelCase__, torchify=UpperCamelCase__     )
        for image in image_inputs:
             self.assertIsInstance(UpperCamelCase__, torch.Tensor     )

        # Test not batched input
        _A    		=  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
        _A    		=  image_processing(UpperCamelCase__, 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'],
            ), )










from typing import List, Optional, Union

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




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)