




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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							int:
           _A							:     List[Any]			=						tempfile.mkdtemp()

           _A							:     str			=						BlipImageProcessor()
           _A							:     Union[str, Any]			=						GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""				)

           _A							:     int			=						BlipaProcessor(__a		,     __a				)

           processor.save_pretrained(self.tmpdirname				)

     def 					a__	(  self		,     **_a				)						->							int:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **__a				).tokenizer

     def 					a__	(  self		,     **_a				)						->							str:
           return AutoProcessor.from_pretrained(self.tmpdirname		,     **__a				).image_processor

     def 					a__	(  self				)						->							str:
           shutil.rmtree(self.tmpdirname				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[Any]			=						[np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta				)]

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

           return image_inputs

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     List[str]			=						BlipaProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           _A							:     str			=						self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)"""				)
           _A							:     Union[str, Any]			=						self.get_image_processor(do_normalize=__a		,     padding_value=1.0				)

           _A							:     str			=						BlipaProcessor.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 					a__	(  self				)						->							int:
           _A							:     Any			=						self.get_image_processor()
           _A							:     Optional[int]			=						self.get_tokenizer()

           _A							:     Dict			=						BlipaProcessor(tokenizer=__a		,     image_processor=__a				)

           _A							:     Dict			=						self.prepare_image_inputs()

           _A							:     Union[str, Any]			=						image_processor(__a		,     return_tensors="""np"""				)
           _A							:     Tuple			=						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 					a__	(  self				)						->							Optional[int]:
           _A							:     str			=						self.get_image_processor()
           _A							:     List[Any]			=						self.get_tokenizer()

           _A							:     Tuple			=						BlipaProcessor(tokenizer=__a		,     image_processor=__a				)

           _A							:     Union[str, Any]			=						"""lower newer"""

           _A							:     Dict			=						processor(text=__a				)

           _A							:     List[str]			=						tokenizer(__a		,     return_token_type_ids=__a				)

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

     def 					a__	(  self				)						->							List[Any]:
           _A							:     str			=						self.get_image_processor()
           _A							:     int			=						self.get_tokenizer()

           _A							:     Any			=						BlipaProcessor(tokenizer=__a		,     image_processor=__a				)

           _A							:     Optional[Any]			=						"""lower newer"""
           _A							:     Any			=						self.prepare_image_inputs()

           _A							:     int			=						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 					a__	(  self				)						->							Optional[Any]:
           _A							:     Tuple			=						self.get_image_processor()
           _A							:     Any			=						self.get_tokenizer()

           _A							:     Dict			=						BlipaProcessor(tokenizer=__a		,     image_processor=__a				)

           _A							:     Any			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:     Any			=						processor.batch_decode(__a				)
           _A							:     Any			=						tokenizer.batch_decode(__a				)

           self.assertListEqual(__a		,     __a				)



     def 					a__	(  self				)						->							Tuple:
           _A							:     Optional[Any]			=						self.get_image_processor()
           _A							:     str			=						self.get_tokenizer()

           _A							:     Optional[Any]			=						BlipaProcessor(tokenizer=__a		,     image_processor=__a				)

           _A							:     Any			=						"""lower newer"""
           _A							:     List[Any]			=						self.prepare_image_inputs()

           _A							:     List[str]			=						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"""]				)





"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ):
						raise ValueError("iterations must be defined as integers"      )
			if not isinstance(_UpperCamelCase    , _UpperCamelCase      ) 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(_UpperCamelCase      )

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()




