





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 	A_       (unittest.TestCase			):
	def __init__(    self							,					_A							,					_A=7							,					_A=3							,					_A=1_8							,					_A=3_0							,					_A=4_0_0							,					_A=True							,					_A=None							,					_A=True							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				size if size is not None else {'''shortest_edge''': 2_0}
				UpperCAmelCase   		=				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				num_channels
				UpperCAmelCase   		=				image_size
				UpperCAmelCase   		=				min_resolution
				UpperCAmelCase   		=				max_resolution
				UpperCAmelCase   		=				do_resize
				UpperCAmelCase   		=				size
				UpperCAmelCase   		=				do_center_crop
				UpperCAmelCase   		=				crop_size







	def      _lowercase				(    self		):






				'''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 	A_       (a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			MobileNetVaImageProcessor if is_vision_available() else None
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				MobileNetVaImageProcessingTester(self		)
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(_A							,					'''do_resize'''		)		)
				self.assertTrue(hasattr(_A							,					'''size'''		)		)
				self.assertTrue(hasattr(_A							,					'''do_center_crop'''		)		)
				self.assertTrue(hasattr(_A							,					'''crop_size'''		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size							,					{'''shortest_edge''': 2_0}		)
				self.assertEqual(image_processor.crop_size							,					{'''height''': 1_8, '''width''': 1_8}		)

				UpperCAmelCase   		=				self.image_processing_class.from_dict(self.image_processor_dict							,					size=4_2							,					crop_size=8_4		)
				self.assertEqual(image_processor.size							,					{'''shortest_edge''': 4_2}		)
				self.assertEqual(image_processor.crop_size							,					{'''height''': 8_4, '''width''': 8_4}		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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
				UpperCAmelCase   		=				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'''],
				    )							,					)










import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
				import torch

				from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 	A_       (unittest.TestCase			):
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-base'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 7_6_8)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				XLMRobertaModel.from_pretrained('''xlm-roberta-large'''		)
				UpperCAmelCase   		=				torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]		)
				# The dog is cute and lives in the garden house

				UpperCAmelCase   		=				torch.Size((1, 1_2, 1_0_2_4)		)  # batch_size, sequence_length, embedding_vector_dim
				UpperCAmelCase   		=				torch.tensor(
				    [[-0.06_99, -0.03_18, 0.07_05, -0.12_41, 0.09_99, -0.05_20, 0.10_04, -0.18_38, -0.47_04, 0.14_37, 0.08_21, 0.01_26]]		)
				#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
				#  xlmr.eval()
				#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
				with torch.no_grad():
							UpperCAmelCase   		=				model(_A		)['''last_hidden_state'''].detach()
				self.assertEqual(output.shape							,					_A		)
				# compare the actual values for a slice of last dim
				self.assertTrue(torch.allclose(output[:, :, -1]							,					_A							,					atol=1E-3		)		)



