






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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			List[str]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    tempfile.mkdtemp()

         _UpperCAmelCase							=    BlipImageProcessor()
         _UpperCAmelCase							=    GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)

         _UpperCAmelCase							=    BlipaProcessor(lowerCamelCase       ,				lowerCamelCase							)

         processor.save_pretrained(self.tmpdirname							)


  def  lowerCamelCase       (      self		:			Tuple       ,				**lowerCamelCase		:			Tuple							)						->	str:


         """simple docstring"""
         return AutoProcessor.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							).tokenizer


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				**lowerCamelCase		:			Any							)						->	Optional[int]:


         """simple docstring"""
         return AutoProcessor.from_pretrained(self.tmpdirname       ,				**lowerCamelCase							).image_processor


  def  lowerCamelCase       (      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         shutil.rmtree(self.tmpdirname							)


  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    [np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta							)]

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

         return image_inputs


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Tuple:


         """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=lowerCamelCase       ,				padding_value=1.0							)

         _UpperCAmelCase							=    BlipaProcessor.from_pretrained(
             self.tmpdirname       ,				bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""       ,				do_normalize=lowerCamelCase       ,				padding_value=1.0							)

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

         self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string()							)
         self.assertIsInstance(processor.image_processor       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    image_processor(lowerCamelCase       ,				return_tensors="""np"""							)
         _UpperCAmelCase							=    processor(images=lowerCamelCase       ,				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  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """lower newer"""

         _UpperCAmelCase							=    processor(text=lowerCamelCase							)

         _UpperCAmelCase							=    tokenizer(lowerCamelCase       ,				return_token_type_ids=lowerCamelCase							)

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


  def  lowerCamelCase       (      self		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    processor(text=lowerCamelCase       ,				images=lowerCamelCase							)

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

         # test if it raises when no input is passed
         with pytest.raises(lowerCamelCase							):
                processor()


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

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

         _UpperCAmelCase							=    processor.batch_decode(lowerCamelCase							)
         _UpperCAmelCase							=    tokenizer.batch_decode(lowerCamelCase							)

         self.assertListEqual(lowerCamelCase       ,				lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    self.get_image_processor()
         _UpperCAmelCase							=    self.get_tokenizer()

         _UpperCAmelCase							=    BlipaProcessor(tokenizer=lowerCamelCase       ,				image_processor=lowerCamelCase							)

         _UpperCAmelCase							=    """lower newer"""
         _UpperCAmelCase							=    self.prepare_image_inputs()

         _UpperCAmelCase							=    processor(text=lowerCamelCase       ,				images=lowerCamelCase							)

         # 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 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @slow
  def  lowerCamelCase       (      self		:			List[str]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-base"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
         # The dog is cute and lives in the garden house

         _UpperCAmelCase							=    torch.Size((1, 12, 768)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
         #  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(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    XLMRobertaModel.from_pretrained("""xlm-roberta-large"""							)
         _UpperCAmelCase							=    torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]							)
         # The dog is cute and lives in the garden house

         _UpperCAmelCase							=    torch.Size((1, 12, 1024)							)  # batch_size, sequence_length, embedding_vector_dim
         _UpperCAmelCase							=    torch.tensor(
             [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
         #  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(lowerCamelCase							)["""last_hidden_state"""].detach()
         self.assertEqual(output.shape       ,				lowerCamelCase							)
         # compare the actual values for a slice of last dim
         self.assertTrue(torch.allclose(output[:, :, -1]       ,				lowerCamelCase       ,				atol=1E-3							)							)