'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				logging.get_logger(__name__)

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




class 				_snake_case	(			SCREAMING_SNAKE_CASE__							):
			lowerCAmelCase_  :   Dict		 =					"rembert"




			def __init__(      self       ,							a__=250_300       ,							a__=1_152       ,							a__=32       ,							a__=18       ,							a__=256       ,							a__=1_664       ,							a__=4_608       ,							a__="gelu"       ,							a__=0.0       ,							a__=0.0       ,							a__=0.1       ,							a__=512       ,							a__=2       ,							a__=0.0_2       ,							a__=1e-12       ,							a__=True       ,							a__=0       ,							a__=312       ,							a__=313       ,							**a__       ,							)       ->       Any:

										'''simple docstring'''




										super().__init__(pad_token_id=a__       ,							bos_token_id=a__       ,							eos_token_id=a__       ,							**a__							)

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




class 				_snake_case	(			SCREAMING_SNAKE_CASE__							):




			@property
			def      lowerCAmelCase__       (      self							)       ->       Mapping[str, Mapping[int, str]]:

										'''simple docstring'''




										if self.task == "multiple-choice":
																	snake_case_			      =   {0: "batch", 1: "choice", 2: "sequence"}
										else:
																	snake_case_			      =   {0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ]							)




			@property
			def      lowerCAmelCase__       (      self							)       ->       float:

										'''simple docstring'''




										return 1e-4










'''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 ViltImageProcessor




class 				_snake_case	(			unittest.TestCase							):




			def __init__(      self       ,							a__       ,							a__=7       ,							a__=3       ,							a__=18       ,							a__=30       ,							a__=400       ,							a__=True       ,							a__=None       ,							a__=2       ,							a__=True       ,							a__=[0.5, 0.5, 0.5]       ,							a__=[0.5, 0.5, 0.5]       ,							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   size if size is not None else {"shortest_edge": 30}
										snake_case_			      =   parent
										snake_case_			      =   batch_size
										snake_case_			      =   num_channels
										snake_case_			      =   image_size
										snake_case_			      =   min_resolution
										snake_case_			      =   max_resolution
										snake_case_			      =   do_resize
										snake_case_			      =   size
										snake_case_			      =   size_divisor
										snake_case_			      =   do_normalize
										snake_case_			      =   image_mean
										snake_case_			      =   image_std




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										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      lowerCAmelCase__       (      self       ,							a__       ,							a__=False							)       ->       str:

										'''simple docstring'''




										if not batched:
																	snake_case_			      =   self.size["shortest_edge"]
																	snake_case_			      =   image_inputs[0]
																	if isinstance(a__       ,							Image.Image							):
																								snake_case_      ,   snake_case_			      =   image.size
																	else:
																								snake_case_      ,   snake_case_			      =   image.shape[1], image.shape[2]
																	snake_case_			      =   size / min(a__       ,							a__							)
																	if h < w:
																								snake_case_      ,   snake_case_			      =   size, scale * w
																	else:
																								snake_case_      ,   snake_case_			      =   scale * h, size

																	snake_case_			      =   int((1_333 / 800) * size							)
																	if max(a__       ,							a__							) > max_size:
																								snake_case_			      =   max_size / max(a__       ,							a__							)
																								snake_case_			      =   newh * scale
																								snake_case_			      =   neww * scale

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

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

										return expected_height, expected_width




@require_torch
@require_vision
class 				_snake_case	(			lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   Union[str, Any]		 =					ViltImageProcessor if is_vision_available() else None




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   ViltImageProcessingTester(self							)




			@property
			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										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"							)							)
										self.assertTrue(hasattr(a__       ,							"size_divisor"							)							)




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 30}							)

										snake_case_			      =   self.image_processing_class.from_dict(self.image_processor_dict       ,							size=42							)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 42}							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[int]:

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_			      =   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__       ,							Image.Image							)

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

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

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




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_			      =   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
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

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




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_			      =   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
										snake_case_			      =   image_processing(image_inputs[0]       ,							return_tensors="pt"							).pixel_values

										snake_case_      ,   snake_case_			      =   self.image_processor_tester.get_expected_values(a__							)
										self.assertEqual(
										    encoded_images.shape       ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,							)

										# Test batched
										snake_case_			      =   image_processing(a__       ,							return_tensors="pt"							).pixel_values

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




