


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 ViltImageProcessor







class  lowerCamelCase__  (			unittest.TestCase):





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=7						,	UpperCAmelCase=3						,	UpperCAmelCase=1_8						,	UpperCAmelCase=3_0						,	UpperCAmelCase=4_0_0						,	UpperCAmelCase=True						,	UpperCAmelCase=None						,	UpperCAmelCase=2						,	UpperCAmelCase=True						,	UpperCAmelCase=[0.5, 0.5, 0.5]						,	UpperCAmelCase=[0.5, 0.5, 0.5]						,	)	->  Union[str, Any]:
      _lowercase				 =size if size is not None else {'''shortest_edge''': 3_0}
      _lowercase				 =parent
      _lowercase				 =batch_size
      _lowercase				 =num_channels
      _lowercase				 =image_size
      _lowercase				 =min_resolution
      _lowercase				 =max_resolution
      _lowercase				 =do_resize
      _lowercase				 =size
      _lowercase				 =size_divisor
      _lowercase				 =do_normalize
      _lowercase				 =image_mean
      _lowercase				 =image_std





     def 	__A   (self			)	->  Optional[Any]:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase=False			)	->  Union[str, Any]:
      if not batched:
       _lowercase				 =self.size['''shortest_edge''']
       _lowercase				 =image_inputs[0]
       if isinstance(UpperCAmelCase						,	Image.Image			):
        _lowercase ,       _lowercase				 =image.size
       else:
        _lowercase ,       _lowercase				 =image.shape[1], image.shape[2]
       _lowercase				 =size / min(UpperCAmelCase						,	UpperCAmelCase			)
       if h < w:
        _lowercase ,       _lowercase				 =size, scale * w
       else:
        _lowercase ,       _lowercase				 =scale * h, size

       _lowercase				 =int((1_3_3_3 / 8_0_0) * size			)
       if max(UpperCAmelCase						,	UpperCAmelCase			) > max_size:
        _lowercase				 =max_size / max(UpperCAmelCase						,	UpperCAmelCase			)
        _lowercase				 =newh * scale
        _lowercase				 =neww * scale

       _lowercase ,       _lowercase				 =int(newh + 0.5			), int(neww + 0.5			)
       _lowercase ,       _lowercase				 =(
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       _lowercase				 =[]
       for image in image_inputs:
        _lowercase ,       _lowercase				 =self.get_expected_values([image]			)
        expected_values.append((expected_height, expected_width)			)
       _lowercase				 =max(UpperCAmelCase						,	key=lambda UpperCAmelCase			: item[0]			)[0]
       _lowercase				 =max(UpperCAmelCase						,	key=lambda UpperCAmelCase			: item[1]			)[1]

      return expected_height, expected_width







@require_torch
@require_vision
class  lowerCamelCase__  (			lowerCAmelCase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     ViltImageProcessor if is_vision_available() else None





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =ViltImageProcessingTester(self			)





     @property
     def 	__A   (self			)	->  Optional[int]:
      return self.image_processor_tester.prepare_image_processor_dict()





     def 	__A   (self			)	->  int:
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(UpperCAmelCase						,	'''image_mean'''			)			)
      self.assertTrue(hasattr(UpperCAmelCase						,	'''image_std'''			)			)
      self.assertTrue(hasattr(UpperCAmelCase						,	'''do_normalize'''			)			)
      self.assertTrue(hasattr(UpperCAmelCase						,	'''do_resize'''			)			)
      self.assertTrue(hasattr(UpperCAmelCase						,	'''size'''			)			)
      self.assertTrue(hasattr(UpperCAmelCase						,	'''size_divisor'''			)			)





     def 	__A   (self			)	->  Dict:
      _lowercase				 =self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size						,	{'''shortest_edge''': 3_0}			)

      _lowercase				 =self.image_processing_class.from_dict(self.image_processor_dict						,	size=4_2			)
      self.assertEqual(image_processor.size						,	{'''shortest_edge''': 4_2}			)





     def 	__A   (self			)	->  int:
      pass





     def 	__A   (self			)	->  Optional[Any]:
      # Initialize image_processing
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _lowercase				 =prepare_image_inputs(self.image_processor_tester						,	equal_resolution=UpperCAmelCase			)
      for image in image_inputs:
       self.assertIsInstance(UpperCAmelCase						,	Image.Image			)

      # Test not batched input
      _lowercase				 =image_processing(image_inputs[0]						,	return_tensors='''pt'''			).pixel_values

      _lowercase ,       _lowercase				 =self.image_processor_tester.get_expected_values(UpperCAmelCase			)
      self.assertEqual(
          encoded_images.shape						,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,	)

      # Test batched
      _lowercase				 =image_processing(UpperCAmelCase						,	return_tensors='''pt'''			).pixel_values

      _lowercase ,       _lowercase				 =self.image_processor_tester.get_expected_values(UpperCAmelCase						,	batched=UpperCAmelCase			)
      self.assertEqual(
          encoded_images.shape						,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,	)





     def 	__A   (self			)	->  List[str]:
      # Initialize image_processing
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _lowercase				 =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
      _lowercase				 =image_processing(image_inputs[0]						,	return_tensors='''pt'''			).pixel_values

      _lowercase ,       _lowercase				 =self.image_processor_tester.get_expected_values(UpperCAmelCase			)
      self.assertEqual(
          encoded_images.shape						,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,	)

      # Test batched
      _lowercase				 =image_processing(UpperCAmelCase						,	return_tensors='''pt'''			).pixel_values

      _lowercase ,       _lowercase				 =self.image_processor_tester.get_expected_values(UpperCAmelCase						,	batched=UpperCAmelCase			)
      self.assertEqual(
          encoded_images.shape						,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,	)





     def 	__A   (self			)	->  Optional[Any]:
      # Initialize image_processing
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _lowercase				 =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
      _lowercase				 =image_processing(image_inputs[0]						,	return_tensors='''pt'''			).pixel_values

      _lowercase ,       _lowercase				 =self.image_processor_tester.get_expected_values(UpperCAmelCase			)
      self.assertEqual(
          encoded_images.shape						,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)						,	)

      # Test batched
      _lowercase				 =image_processing(UpperCAmelCase						,	return_tensors='''pt'''			).pixel_values

      _lowercase ,       _lowercase				 =self.image_processor_tester.get_expected_values(UpperCAmelCase						,	batched=UpperCAmelCase			)
      self.assertEqual(
          encoded_images.shape						,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,	)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__        =				logging.get_logger(__name__)

UpperCAmelCase__        =				{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     '''rembert'''





     def __init__(self						,	UpperCAmelCase=2_5_0_3_0_0						,	UpperCAmelCase=1_1_5_2						,	UpperCAmelCase=3_2						,	UpperCAmelCase=1_8						,	UpperCAmelCase=2_5_6						,	UpperCAmelCase=1_6_6_4						,	UpperCAmelCase=4_6_0_8						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=1e-12						,	UpperCAmelCase=True						,	UpperCAmelCase=0						,	UpperCAmelCase=3_1_2						,	UpperCAmelCase=3_1_3						,	**UpperCAmelCase						,	)	->  Tuple:
      super().__init__(pad_token_id=UpperCAmelCase						,	bos_token_id=UpperCAmelCase						,	eos_token_id=UpperCAmelCase						,	**UpperCAmelCase			)

      _lowercase				 =vocab_size
      _lowercase				 =input_embedding_size
      _lowercase				 =output_embedding_size
      _lowercase				 =max_position_embeddings
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =intermediate_size
      _lowercase				 =hidden_act
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =classifier_dropout_prob
      _lowercase				 =initializer_range
      _lowercase				 =type_vocab_size
      _lowercase				 =layer_norm_eps
      _lowercase				 =use_cache
      _lowercase				 =False







class  lowerCamelCase__  (			lowerCAmelCase):





     @property
     def 	__A   (self			)	->  Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       _lowercase				 ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       _lowercase				 ={0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]			)





     @property
     def 	__A   (self			)	->  float:
      return 1e-4

