

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,
           BertTokenizerFast,
           BlipImageProcessor,
           GPTaTokenizer,
           InstructBlipProcessor,
           PreTrainedTokenizerFast,
       )



@require_vision
class       __snake_case  (    unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						tempfile.mkdtemp()

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

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(_A						,		_A						,		_A)

      processor.save_pretrained(self.tmpdirname)

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

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

  def 						lowerCAmelCase__     (					self						,		**_A):
      return AutoProcessor.from_pretrained(self.tmpdirname						,		**_A).qformer_tokenizer

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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						[np.random.randint(255						,		size=(3, 30, 400)						,		dtype=np.uinta)]

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

      return image_inputs

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(
          tokenizer=self.get_tokenizer()						,		image_processor=self.get_image_processor()						,		qformer_tokenizer=self.get_qformer_tokenizer()						,		)
      processor.save_pretrained(self.tmpdirname)

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

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor.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)
      self.assertIsInstance(processor.qformer_tokenizer						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()
      SCREAMING_SNAKE_CASE_									=						self.get_qformer_tokenizer()

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(
          tokenizer=_A						,		image_processor=_A						,		qformer_tokenizer=_A)

      SCREAMING_SNAKE_CASE_									=						self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processor(_A						,		return_tensors='np')
      SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()
      SCREAMING_SNAKE_CASE_									=						self.get_qformer_tokenizer()

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(
          tokenizer=_A						,		image_processor=_A						,		qformer_tokenizer=_A)

      SCREAMING_SNAKE_CASE_									=						'lower newer'

      SCREAMING_SNAKE_CASE_									=						processor(text=_A)

      SCREAMING_SNAKE_CASE_									=						tokenizer(_A						,		return_token_type_ids=_A)
      SCREAMING_SNAKE_CASE_									=						qformer_tokenizer(_A						,		return_token_type_ids=_A)

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

      for key in encoded_tokens_qformer.keys():
          self.assertListEqual(encoded_tokens_qformer[key]						,		encoded_processor['qformer_' + key])

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()
      SCREAMING_SNAKE_CASE_									=						self.get_qformer_tokenizer()

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(
          tokenizer=_A						,		image_processor=_A						,		qformer_tokenizer=_A)

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

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

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

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

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()
      SCREAMING_SNAKE_CASE_									=						self.get_qformer_tokenizer()

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(
          tokenizer=_A						,		image_processor=_A						,		qformer_tokenizer=_A)

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

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

      self.assertListEqual(_A						,		_A)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.get_image_processor()
      SCREAMING_SNAKE_CASE_									=						self.get_tokenizer()
      SCREAMING_SNAKE_CASE_									=						self.get_qformer_tokenizer()

      SCREAMING_SNAKE_CASE_									=						InstructBlipProcessor(
          tokenizer=_A						,		image_processor=_A						,		qformer_tokenizer=_A)

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

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

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





import pytest

import datasets


# Import fixture modules as plugins
UpperCamelCase__  :      Union[str, Any]			=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    for item in items:
        if any(marker in item.keywords for marker in ['integration', 'unit']       ):
            continue
        item.add_marker(pytest.mark.unit       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    config.addinivalue_line('markers' ,   'torchaudio_latest: mark test to run with torchaudio>=0.12'       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tmp_path_factory.getbasetemp() / 'cache'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'datasets'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'metrics'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'modules'
    monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads'
    monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads' / 'extracted'
    monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE ,   scope='session'       )
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    datasets.disable_progress_bar()


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,   _SCREAMING_SNAKE_CASE       )





@pytest.fixture
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,   _SCREAMING_SNAKE_CASE       )


