

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 OwlViTImageProcessor



class    __snake_case	(     unittest.TestCase							):

  def __init__(       self    ,		_A    ,		_A=7    ,		_A=3    ,		_A=18    ,		_A=30    ,		_A=400    ,		_A=True    ,		_A=None    ,		_A=True    ,		_A=None    ,		_A=True    ,		_A=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,		_A=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,		_A=True    ,		):
      SCREAMING_SNAKE_CASE_						=    parent
      SCREAMING_SNAKE_CASE_						=    batch_size
      SCREAMING_SNAKE_CASE_						=    num_channels
      SCREAMING_SNAKE_CASE_						=    image_size
      SCREAMING_SNAKE_CASE_						=    min_resolution
      SCREAMING_SNAKE_CASE_						=    max_resolution
      SCREAMING_SNAKE_CASE_						=    do_resize
      SCREAMING_SNAKE_CASE_						=    size if size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_						=    do_center_crop
      SCREAMING_SNAKE_CASE_						=    crop_size if crop_size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_						=    do_normalize
      SCREAMING_SNAKE_CASE_						=    image_mean
      SCREAMING_SNAKE_CASE_						=    image_std
      SCREAMING_SNAKE_CASE_						=    do_convert_rgb


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






@require_torch
@require_vision
class    __snake_case	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						Optional[int]    			=	OwlViTImageProcessor if is_vision_available() else None

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    OwlViTImageProcessingTester(self)

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

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    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    ,		'center_crop'))
      self.assertTrue(hasattr(_A    ,		'do_normalize'))
      self.assertTrue(hasattr(_A    ,		'image_mean'))
      self.assertTrue(hasattr(_A    ,		'image_std'))
      self.assertTrue(hasattr(_A    ,		'do_convert_rgb'))

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size    ,		{'height': 18, 'width': 18})
      self.assertEqual(image_processor.crop_size    ,		{'height': 18, 'width': 18})

      SCREAMING_SNAKE_CASE_						=    self.image_processing_class.from_dict(self.image_processor_dict    ,		size=42    ,		crop_size=84)
      self.assertEqual(image_processor.size    ,		{'height': 42, 'width': 42})
      self.assertEqual(image_processor.crop_size    ,		{'height': 84, 'width': 84})

  def     lowerCAmelCase__  (       self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_						=    self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_						=    prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=_A)

      for image in image_inputs:
          self.assertIsInstance(_A    ,		Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_						=    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
      SCREAMING_SNAKE_CASE_						=    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     lowerCAmelCase__  (       self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_						=    self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_						=    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
      SCREAMING_SNAKE_CASE_						=    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
      SCREAMING_SNAKE_CASE_						=    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     lowerCAmelCase__  (       self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_						=    self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_						=    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
      SCREAMING_SNAKE_CASE_						=    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
      SCREAMING_SNAKE_CASE_						=    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 ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__	:   List[str]								=     get_tests_dir("fixtures/spiece.model")



@require_sentencepiece
@require_tokenizers
class    __snake_case	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						Union[str, Any]    			=	ErnieMTokenizer
  __lowerCAmelCase  :						Union[str, Any]    			=	False
  __lowerCAmelCase  :						Optional[int]    			=	True
  __lowerCAmelCase  :						List[str]    			=	False
  __lowerCAmelCase  :						List[str]    			=	False

  def     lowerCAmelCase__  (       self):
      super().setUp()

      # We have a SentencePiece fixture for testing
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')
      tokenizer.save_pretrained(self.tmpdirname)

  def     lowerCAmelCase__  (       self    ,		_A):
      SCREAMING_SNAKE_CASE_						=    'this is a test'
      SCREAMING_SNAKE_CASE_						=    'this is a test'
      return input_text, output_text

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    '<pad>'
      SCREAMING_SNAKE_CASE_						=    0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A)    ,		_A)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A)    ,		_A)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    list(self.get_tokenizer().get_vocab().keys())

      self.assertEqual(vocab_keys[0]    ,		'<pad>')
      self.assertEqual(vocab_keys[1]    ,		'<unk>')
      self.assertEqual(vocab_keys[-1]    ,		'▁eloquent')
      self.assertEqual(len(_A)    ,		30000)

  def     lowerCAmelCase__  (       self):
      self.assertEqual(self.get_tokenizer().vocab_size    ,		30000)

  def     lowerCAmelCase__  (       self):
      if not self.test_rust_tokenizer:
          return

      SCREAMING_SNAKE_CASE_						=    self.get_tokenizer()
      SCREAMING_SNAKE_CASE_						=    self.get_rust_tokenizer()

      SCREAMING_SNAKE_CASE_						=    'I was born in 92000, and this is falsé.'

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize(_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.tokenize(_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode(_A    ,		add_special_tokens=_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.encode(_A    ,		add_special_tokens=_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    self.get_rust_tokenizer()
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode(_A)
      SCREAMING_SNAKE_CASE_						=    rust_tokenizer.encode(_A)
      self.assertListEqual(_A    ,		_A)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		do_lower_case=_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('This is a test')
      self.assertListEqual(_A    ,		['▁this', '▁is', '▁a', '▁test'])

      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)    ,		[48, 25, 21, 1289])

      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('I was born in 92000, and this is falsé.')
      # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
      self.assertListEqual(
          _A    ,		['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.'])
      SCREAMING_SNAKE_CASE_						=    tokenizer.convert_tokens_to_ids(_A)
      self.assertListEqual(_A    ,		[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])

      SCREAMING_SNAKE_CASE_						=    tokenizer.convert_ids_to_tokens(_A)
      self.assertListEqual(
          _A    ,		['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']    ,		)

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    ErnieMTokenizer(_A    ,		unk_token='<unk>'    ,		pad_token='<pad>')

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('sequence builders')
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('multi-sequence build')

      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A    ,		_A)

      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
          tokenizer.sep_token_id
      ] + text_a + [tokenizer.sep_token_id]


  @slow
  def     lowerCAmelCase__  (       self):
      # fmt: off
      SCREAMING_SNAKE_CASE_						=    {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=_A    ,		model_name='susnato/ernie-m-base_pytorch'    ,		sequences=[
              'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
              'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
              'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
              'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
              'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
              'conditioning on both left and right context in all layers.',
              'The quick brown fox jumps over the lazy dog.',
          ]    ,		)


