






from collections import OrderedDict
from typing import Mapping

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


A__			:    Dict         =				logging.get_logger(__name__)

A__			:    Any         =				{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= """roberta"""




				def __init__(  self		:					str,						lowerCamelCase		:					Dict=50_265,						lowerCamelCase		:					int=768,						lowerCamelCase		:					Optional[int]=12,						lowerCamelCase		:					Dict=12,						lowerCamelCase		:					Optional[Any]=3_072,						lowerCamelCase		:					Union[str, Any]="gelu",						lowerCamelCase		:					Optional[Any]=0.1,						lowerCamelCase		:					Dict=0.1,						lowerCamelCase		:					str=512,						lowerCamelCase		:					str=2,						lowerCamelCase		:					int=0.02,						lowerCamelCase		:					Any=1E-12,						lowerCamelCase		:					int=1,						lowerCamelCase		:					List[str]=0,						lowerCamelCase		:					Union[str, Any]=2,						lowerCamelCase		:					Any="absolute",						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					Union[str, Any]=None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								super().__init__(pad_token_id=lowerCamelCase,						bos_token_id=lowerCamelCase,						eos_token_id=lowerCamelCase,						**lowerCamelCase						)

								lowercase__     				=    vocab_size
								lowercase__     				=    hidden_size
								lowercase__     				=    num_hidden_layers
								lowercase__     				=    num_attention_heads
								lowercase__     				=    hidden_act
								lowercase__     				=    intermediate_size
								lowercase__     				=    hidden_dropout_prob
								lowercase__     				=    attention_probs_dropout_prob
								lowercase__     				=    max_position_embeddings
								lowercase__     				=    type_vocab_size
								lowercase__     				=    initializer_range
								lowercase__     				=    layer_norm_eps
								lowercase__     				=    position_embedding_type
								lowercase__     				=    use_cache
								lowercase__     				=    classifier_dropout



class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				@property
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								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),
								    ]						)












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 MobileNetVaImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''shortest_edge''': 20}
								lowercase__     				=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_center_crop
								lowercase__     				=    crop_size




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    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}						)

								lowercase__     				=    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        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# 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=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# 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=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# 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=lowerCamelCase,						torchify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						torch.Tensor						)

								# Test not batched input
								lowercase__     				=    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
								lowercase__     				=    image_processing(lowerCamelCase,						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'''],
								    ),						)




