





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast

@require_vision
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				tempfile.mkdtemp()

				UpperCAmelCase   		=				BlipImageProcessor()
				UpperCAmelCase   		=				GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''		)

				UpperCAmelCase   		=				BlipaProcessor(_A							,					_A		)

				processor.save_pretrained(self.tmpdirname		)
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				return AutoProcessor.from_pretrained(self.tmpdirname							,					**_A		).tokenizer
	def      _lowercase				(    self							,					**_A		):






				'''simple docstring'''







				return AutoProcessor.from_pretrained(self.tmpdirname							,					**_A		).image_processor
	def      _lowercase				(    self		):






				'''simple docstring'''







				shutil.rmtree(self.tmpdirname		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				[np.random.randint(2_5_5							,					size=(3, 3_0, 4_0_0)							,					dtype=np.uinta		)]

				UpperCAmelCase   		=				[Image.fromarray(np.moveaxis(_A							,					0							,					-1		)		) for x in image_inputs]

				return image_inputs
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				BlipaProcessor(tokenizer=self.get_tokenizer()							,					image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				UpperCAmelCase   		=				self.get_tokenizer(bos_token='''(BOS)'''							,					eos_token='''(EOS)'''		)
				UpperCAmelCase   		=				self.get_image_processor(do_normalize=_A							,					padding_value=1.0		)

				UpperCAmelCase   		=				BlipaProcessor.from_pretrained(
				    self.tmpdirname							,					bos_token='''(BOS)'''							,					eos_token='''(EOS)'''							,					do_normalize=_A							,					padding_value=1.0		)

				self.assertEqual(processor.tokenizer.get_vocab()							,					tokenizer_add_kwargs.get_vocab()		)
				self.assertIsInstance(processor.tokenizer							,					_A		)

				self.assertEqual(processor.image_processor.to_json_string()							,					image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipaProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				image_processor(_A							,					return_tensors='''np'''		)
				UpperCAmelCase   		=				processor(images=_A							,					return_tensors='''np'''		)

				for key in input_feat_extract.keys():
							self.assertAlmostEqual(input_feat_extract[key].sum()							,					input_processor[key].sum()							,					delta=1E-2		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipaProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''lower newer'''

				UpperCAmelCase   		=				processor(text=_A		)

				UpperCAmelCase   		=				tokenizer(_A							,					return_token_type_ids=_A		)

				for key in encoded_tok.keys():
							self.assertListEqual(encoded_tok[key]							,					encoded_processor[key]		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipaProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''lower newer'''
				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				processor(text=_A							,					images=_A		)

				self.assertListEqual(list(inputs.keys()		)							,					['''pixel_values''', '''input_ids''', '''attention_mask''']		)

				# test if it raises when no input is passed
				with pytest.raises(_A		):
							processor()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipaProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				UpperCAmelCase   		=				processor.batch_decode(_A		)
				UpperCAmelCase   		=				tokenizer.batch_decode(_A		)

				self.assertListEqual(_A							,					_A		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.get_image_processor()
				UpperCAmelCase   		=				self.get_tokenizer()

				UpperCAmelCase   		=				BlipaProcessor(tokenizer=_A							,					image_processor=_A		)

				UpperCAmelCase   		=				'''lower newer'''
				UpperCAmelCase   		=				self.prepare_image_inputs()

				UpperCAmelCase   		=				processor(text=_A							,					images=_A		)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()		)							,					['''pixel_values''', '''input_ids''', '''attention_mask''']		)










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		)		)



