




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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()

          a     =				BlipImageProcessor()
          a     =				BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"			)

          a     =				BlipProcessor(A  ,		A			)

          processor.save_pretrained(self.tmpdirname			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Optional[int]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          shutil.rmtree(self.tmpdirname			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



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

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

          return image_inputs







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				BlipProcessor(tokenizer=self.get_tokenizer()  ,		image_processor=self.get_image_processor()			)
          processor.save_pretrained(self.tmpdirname			)

          a     =				self.get_tokenizer(bos_token="(BOS)"  ,		eos_token="(EOS)"			)
          a     =				self.get_image_processor(do_normalize=A  ,		padding_value=1.0			)

          a     =				BlipProcessor.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       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()

          a     =				BlipProcessor(tokenizer=A  ,		image_processor=A			)

          a     =				self.prepare_image_inputs()

          a     =				image_processor(A  ,		return_tensors="np"			)
          a     =				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			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()

          a     =				BlipProcessor(tokenizer=A  ,		image_processor=A			)

          a     =				"lower newer"

          a     =				processor(text=A			)

          a     =				tokenizer(A  ,		return_token_type_ids=A			)

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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()

          a     =				BlipProcessor(tokenizer=A  ,		image_processor=A			)

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

          a     =				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       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()

          a     =				BlipProcessor(tokenizer=A  ,		image_processor=A			)

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

          a     =				processor.batch_decode(A			)
          a     =				tokenizer.batch_decode(A			)

          self.assertListEqual(A  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_image_processor()
          a     =				self.get_tokenizer()

          a     =				BlipProcessor(tokenizer=A  ,		image_processor=A			)

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

          a     =				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"]			)







lowercase__				:      Optional[int]	   = [
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

