


'''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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast


@require_vision
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   tempfile.mkdtemp()

       __a       =   BlipImageProcessor()
       __a       =   GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)

       __a       =   BlipaProcessor(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

       processor.save_pretrained(self.tmpdirname						)

  def __a						(					self     :							List[Any]				,							**SCREAMING_SNAKE_CASE__     :							Dict						):





       '''simple docstring'''

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

  def __a						(					self     :							Tuple				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

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

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       shutil.rmtree(self.tmpdirname						)

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   [np.random.randint(2_5_5				,							size=(3, 3_0, 4_0_0)				,							dtype=np.uinta						)]

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

       return image_inputs

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   BlipaProcessor(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=SCREAMING_SNAKE_CASE__				,							padding_value=1.0						)

       __a       =   BlipaProcessor.from_pretrained(
           self.tmpdirname				,							bos_token="""(BOS)"""				,							eos_token="""(EOS)"""				,							do_normalize=SCREAMING_SNAKE_CASE__				,							padding_value=1.0						)

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

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

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   self.get_image_processor()
       __a       =   self.get_tokenizer()

       __a       =   BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__						)

       __a       =   self.prepare_image_inputs()

       __a       =   image_processor(SCREAMING_SNAKE_CASE__				,							return_tensors="""np"""						)
       __a       =   processor(images=SCREAMING_SNAKE_CASE__				,							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     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   self.get_image_processor()
       __a       =   self.get_tokenizer()

       __a       =   BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__						)

       __a       =   """lower newer"""

       __a       =   processor(text=SCREAMING_SNAKE_CASE__						)

       __a       =   tokenizer(SCREAMING_SNAKE_CASE__				,							return_token_type_ids=SCREAMING_SNAKE_CASE__						)

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

  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   self.get_image_processor()
       __a       =   self.get_tokenizer()

       __a       =   BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__						)

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

       __a       =   processor(text=SCREAMING_SNAKE_CASE__				,							images=SCREAMING_SNAKE_CASE__						)

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

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

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.get_image_processor()
       __a       =   self.get_tokenizer()

       __a       =   BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__						)

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

       __a       =   processor.batch_decode(SCREAMING_SNAKE_CASE__						)
       __a       =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE__						)

       self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)


  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   self.get_image_processor()
       __a       =   self.get_tokenizer()

       __a       =   BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__						)

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

       __a       =   processor(text=SCREAMING_SNAKE_CASE__				,							images=SCREAMING_SNAKE_CASE__						)

       # 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'''







import enum
import shutil
import sys


SCREAMING_SNAKE_CASE_      ,     SCREAMING_SNAKE_CASE_      =			shutil.get_terminal_size()

SCREAMING_SNAKE_CASE_      =			{'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}


class  lowerCAmelCase_						(       enum.Enum      ):





  """simple docstring"""



  a_       :List[Any]												=0
  a_       :Optional[Any]												=1




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=""		)				->					List[Any]:
     """simple docstring"""




     sys.stdout.write(str(__SCREAMING_SNAKE_CASE		) + end		)
     sys.stdout.flush()




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=""		)				->					Tuple:
     """simple docstring"""




     forceWrite(F'''\u001b[{color}m{content}\u001b[0m'''				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      )				->					Union[str, Any]:
     """simple docstring"""




     forceWrite("""\r"""		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     forceWrite(F'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''		)




def        __lowercase						(      )				->					int:
     """simple docstring"""




     forceWrite(""" """ * TERMINAL_WIDTH		)
     reset_cursor()




def        __lowercase						(      )				->					str:
     """simple docstring"""




     reset_cursor()
     forceWrite("""-""" * TERMINAL_WIDTH		)






