


'''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 LevitImageProcessor



class  lowerCAmelCase				(				unittest.TestCase							):



	def __init__(					self ,					snake_case__ ,					snake_case__=7 ,					snake_case__=3 ,					snake_case__=18 ,					snake_case__=30 ,					snake_case__=400 ,					snake_case__=True ,					snake_case__=None ,					snake_case__=True ,					snake_case__=None ,					snake_case__=True ,					snake_case__=[0.5, 0.5, 0.5] ,					snake_case__=[0.5, 0.5, 0.5] ,					):
		lowerCAmelCase					:    Union[str, Any]      							= size if size is not None else {'shortest_edge': 18}
		lowerCAmelCase					:    Optional[Any]      							= crop_size if crop_size is not None else {'height': 18, 'width': 18}
		lowerCAmelCase					:    str      							= parent
		lowerCAmelCase					:    int      							= batch_size
		lowerCAmelCase					:    int      							= num_channels
		lowerCAmelCase					:    Dict      							= image_size
		lowerCAmelCase					:    List[Any]      							= min_resolution
		lowerCAmelCase					:    Union[str, Any]      							= max_resolution
		lowerCAmelCase					:    List[str]      							= do_resize
		lowerCAmelCase					:    Tuple      							= size
		lowerCAmelCase					:    Dict      							= do_center_crop
		lowerCAmelCase					:    List[str]      							= crop_size
		lowerCAmelCase					:    List[Any]      							= do_normalize
		lowerCAmelCase					:    str      							= image_mean
		lowerCAmelCase					:    Optional[int]      							= image_std



	def 				lowercase     (					self    ):
		return {
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_normalize": self.do_normalize,
		    "do_resize": self.do_resize,
		    "do_center_crop": self.do_center_crop,
		    "size": self.size,
		    "crop_size": self.crop_size,
		}



@require_torch
@require_vision
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = LevitImageProcessor if is_vision_available() else None



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Any      							= LevitImageProcessingTester(self    )



	@property
	def 				lowercase     (					self    ):
		return self.image_processor_tester.prepare_image_processor_dict()



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= 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 ,					'do_center_crop'    )    )
		self.assertTrue(hasattr(__A ,					'size'    )    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= self.image_processing_class.from_dict(self.image_processor_dict    )
		self.assertEqual(image_processor.size ,					{'shortest_edge': 18}    )
		self.assertEqual(image_processor.crop_size ,					{'height': 18, 'width': 18}    )

		lowerCAmelCase					:    Optional[int]      							= 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    ):
		pass



	def 				lowercase     (					self    ):
		# Initialize image_processing
		lowerCAmelCase					:    int      							= self.image_processing_class(**self.image_processor_dict    )
		# create random PIL images
		lowerCAmelCase					:    str      							= prepare_image_inputs(self.image_processor_tester ,					equal_resolution=__A    )
		for image in image_inputs:
			self.assertIsInstance(__A ,					Image.Image    )

		# Test not batched input
		lowerCAmelCase					:    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
		lowerCAmelCase					:    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 				lowercase     (					self    ):
		# Initialize image_processing
		lowerCAmelCase					:    str      							= self.image_processing_class(**self.image_processor_dict    )
		# create random numpy tensors
		lowerCAmelCase					:    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
		lowerCAmelCase					:    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
		lowerCAmelCase					:    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 				lowercase     (					self    ):
		# Initialize image_processing
		lowerCAmelCase					:    int      							= self.image_processing_class(**self.image_processor_dict    )
		# create random PyTorch tensors
		lowerCAmelCase					:    Union[str, 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
		lowerCAmelCase					:    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
		lowerCAmelCase					:    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'],
		    ) ,					)




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase  :		Any		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					List[str]			      = """xlm-roberta"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

		lowerCAmelCase					:    Optional[Any]      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= num_hidden_layers
		lowerCAmelCase					:    Any      							= num_attention_heads
		lowerCAmelCase					:    Optional[int]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    Dict      							= hidden_dropout_prob
		lowerCAmelCase					:    Union[str, Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= max_position_embeddings
		lowerCAmelCase					:    Optional[int]      							= type_vocab_size
		lowerCAmelCase					:    int      							= initializer_range
		lowerCAmelCase					:    List[Any]      							= layer_norm_eps
		lowerCAmelCase					:    Union[str, Any]      							= position_embedding_type
		lowerCAmelCase					:    Union[str, Any]      							= use_cache
		lowerCAmelCase					:    List[str]      							= classifier_dropout



class  lowerCAmelCase				(				a							):



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