






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 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					tempfile.mkdtemp()

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

           __lowerCAmelCase								=					BlipProcessor(__UpperCamelCase					,						__UpperCamelCase	)

           processor.save_pretrained(self.tmpdirname	)


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	int:
           return AutoProcessor.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	).tokenizer


      def  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Tuple:
           return AutoProcessor.from_pretrained(self.tmpdirname					,						**__UpperCamelCase	).image_processor


      def  __UpperCAmelCase		(	self	)->	Optional[Any]:
           shutil.rmtree(self.tmpdirname	)


      def  __UpperCAmelCase		(	self	)->	Optional[int]:
           __lowerCAmelCase								=					[np.random.randint(2_5_5					,						size=(3, 3_0, 4_0_0)					,						dtype=np.uinta	)]

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

           return image_inputs


      def  __UpperCAmelCase		(	self	)->	Union[str, Any]:
           __lowerCAmelCase								=					BlipProcessor(tokenizer=self.get_tokenizer()					,						image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

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

           __lowerCAmelCase								=					BlipProcessor.from_pretrained(
               self.tmpdirname					,						bos_token="(BOS)"					,						eos_token="(EOS)"					,						do_normalize=__UpperCamelCase					,						padding_value=1.0	)

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

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


      def  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BlipProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					self.prepare_image_inputs()

           __lowerCAmelCase								=					image_processor(__UpperCamelCase					,						return_tensors="np"	)
           __lowerCAmelCase								=					processor(images=__UpperCamelCase					,						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	)->	Tuple:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BlipProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

           __lowerCAmelCase								=					"lower newer"

           __lowerCAmelCase								=					processor(text=__UpperCamelCase	)

           __lowerCAmelCase								=					tokenizer(__UpperCamelCase					,						return_token_type_ids=__UpperCamelCase	)

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


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BlipProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

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

           __lowerCAmelCase								=					processor(text=__UpperCamelCase					,						images=__UpperCamelCase	)

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

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


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BlipProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

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

           __lowerCAmelCase								=					processor.batch_decode(__UpperCamelCase	)
           __lowerCAmelCase								=					tokenizer.batch_decode(__UpperCamelCase	)

           self.assertListEqual(__UpperCamelCase					,						__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Any:
           __lowerCAmelCase								=					self.get_image_processor()
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BlipProcessor(tokenizer=__UpperCamelCase					,						image_processor=__UpperCamelCase	)

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

           __lowerCAmelCase								=					processor(text=__UpperCamelCase					,						images=__UpperCamelCase	)

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()	)					,						["pixel_values", "input_ids", "attention_mask"]	)







def       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     if not isinstance(__snake_case					, __snake_case       ):
          raise ValueError("iterations must be defined as integers"       )
     if not isinstance(__snake_case					, __snake_case       ) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"       )
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"       )

     __lowerCAmelCase								=					""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(__snake_case       )

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()






