





'''simple docstring'''

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 __a (   unittest.TestCase			):





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __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(lowerCamelCase				,lowerCamelCase				,lowerCamelCase    )

      processor.save_pretrained(self.tmpdirname    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,**lowerCamelCase				:			Optional[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,**lowerCamelCase				:			List[Any]    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			List[Any]				,**lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      return AutoProcessor.from_pretrained(self.tmpdirname				,**lowerCamelCase    ).qformer_tokenizer





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      shutil.rmtree(self.tmpdirname    )





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       [np.random.randint(255				,size=(3, 30, 400)				,dtype=np.uinta    )]

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

      return image_inputs





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __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=lowerCamelCase				,padding_value=1.0    )

      __SCREAMING_SNAKE_CASE      							=       InstructBlipProcessor.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    )
      self.assertIsInstance(processor.qformer_tokenizer				,lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __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=lowerCamelCase				,image_processor=lowerCamelCase				,qformer_tokenizer=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE      							=       image_processor(lowerCamelCase				,return_tensors="""np"""    )
      __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __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=lowerCamelCase				,image_processor=lowerCamelCase				,qformer_tokenizer=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """lower newer"""

      __SCREAMING_SNAKE_CASE      							=       processor(text=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       tokenizer(lowerCamelCase				,return_token_type_ids=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       qformer_tokenizer(lowerCamelCase				,return_token_type_ids=lowerCamelCase    )

      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      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      __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=lowerCamelCase				,image_processor=lowerCamelCase				,qformer_tokenizer=lowerCamelCase    )

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

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

      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(lowerCamelCase    ):
         processor()





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      __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=lowerCamelCase				,image_processor=lowerCamelCase				,qformer_tokenizer=lowerCamelCase    )

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

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

      self.assertListEqual(lowerCamelCase				,lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __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=lowerCamelCase				,image_processor=lowerCamelCase				,qformer_tokenizer=lowerCamelCase    )

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

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

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












'''simple docstring'''

import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


a        =						"__DUMMY_TRANSFORMERS_USER__"
a        =						"Dummy User"
a        =						"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"

a        =						"https://hub-ci.huggingface.co"
a        =						CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
a        =						CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
a        =						Path("~/.huggingface/hub_ci_token").expanduser()

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   monkeypatch.setattr(
       """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   monkeypatch.setattr("""datasets.config.HF_ENDPOINT"""							,	__UpperCAmelCase      )
   monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token"""							,	__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   HfFolder.save_token(__UpperCAmelCase      )
   yield
   HfFolder.delete_token()

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							)						->					Optional[Any]:

   '''simple docstring'''







   return HfApi(endpoint=__UpperCAmelCase      )

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       HfFolder.get_token()
   HfFolder.save_token(__UpperCAmelCase      )
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(__UpperCAmelCase      )

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   def _cleanup_repo(__UpperCAmelCase      ):
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )

   return _cleanup_repo

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







   @contextmanager
   def _temporary_repo(__UpperCAmelCase      ):
      try:
         yield repo_id
      finally:
         cleanup_repo(__UpperCAmelCase      )

   return _temporary_repo

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data/text_data.txt"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass

@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   return hf_private_dataset_repo_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data.zip"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass

@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_img_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       f"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""							,	private=__UpperCAmelCase      )
   hf_api.upload_file(
       token=__UpperCAmelCase							,	path_or_fileobj=str(__UpperCAmelCase      )							,	path_in_repo="""data.zip"""							,	repo_id=__UpperCAmelCase							,	repo_type="""dataset"""							,	)
   yield repo_id
   try:
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass







@pytest.fixture()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_img_data_





