





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        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):







							def 						__lowerCamelCase   (				self					:     str      )       ->List[Any]:
										lowerCamelCase__       :						Optional[Any]        =							tempfile.mkdtemp()

										lowerCamelCase__       :						List[Any]        =							BlipImageProcessor()
										lowerCamelCase__       :						Tuple        =							GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''      )
										lowerCamelCase__       :						str        =							BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )

										lowerCamelCase__       :						List[Any]        =							InstructBlipProcessor(A		, A		, A      )

										processor.save_pretrained(self.tmpdirname      )







							def 						__lowerCamelCase   (				self					:     Dict		, **A					:     Optional[Any]      )       ->List[str]:
										return AutoProcessor.from_pretrained(self.tmpdirname		, **A      ).tokenizer







							def 						__lowerCamelCase   (				self					:     Union[str, Any]		, **A					:     List[Any]      )       ->int:
										return AutoProcessor.from_pretrained(self.tmpdirname		, **A      ).image_processor







							def 						__lowerCamelCase   (				self					:     Optional[int]		, **A					:     Dict      )       ->List[str]:
										return AutoProcessor.from_pretrained(self.tmpdirname		, **A      ).qformer_tokenizer







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







							def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Dict:
										lowerCamelCase__       :						List[str]        =							[np.random.randint(2_5_5		, size=(3, 3_0, 4_0_0)		, dtype=np.uinta      )]

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

										return image_inputs







							def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Tuple:
										lowerCamelCase__       :						Tuple        =							InstructBlipProcessor(
										    tokenizer=self.get_tokenizer()		, image_processor=self.get_image_processor()		, qformer_tokenizer=self.get_qformer_tokenizer()		, )
										processor.save_pretrained(self.tmpdirname      )

										lowerCamelCase__       :						Any        =							self.get_tokenizer(bos_token='''(BOS)'''		, eos_token='''(EOS)'''      )
										lowerCamelCase__       :						Dict        =							self.get_image_processor(do_normalize=A		, padding_value=1.0      )

										lowerCamelCase__       :						Dict        =							InstructBlipProcessor.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      )
										self.assertIsInstance(processor.qformer_tokenizer		, A      )







							def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->List[str]:
										lowerCamelCase__       :						Dict        =							self.get_image_processor()
										lowerCamelCase__       :						List[str]        =							self.get_tokenizer()
										lowerCamelCase__       :						Tuple        =							self.get_qformer_tokenizer()

										lowerCamelCase__       :						str        =							InstructBlipProcessor(
										    tokenizer=A		, image_processor=A		, qformer_tokenizer=A      )

										lowerCamelCase__       :						Optional[Any]        =							self.prepare_image_inputs()

										lowerCamelCase__       :						Tuple        =							image_processor(A		, return_tensors='''np'''      )
										lowerCamelCase__       :						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 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
										lowerCamelCase__       :						List[Any]        =							self.get_image_processor()
										lowerCamelCase__       :						Any        =							self.get_tokenizer()
										lowerCamelCase__       :						Dict        =							self.get_qformer_tokenizer()

										lowerCamelCase__       :						Optional[int]        =							InstructBlipProcessor(
										    tokenizer=A		, image_processor=A		, qformer_tokenizer=A      )

										lowerCamelCase__       :						str        =							'''lower newer'''

										lowerCamelCase__       :						Dict        =							processor(text=A      )

										lowerCamelCase__       :						Tuple        =							tokenizer(A		, return_token_type_ids=A      )
										lowerCamelCase__       :						int        =							qformer_tokenizer(A		, return_token_type_ids=A      )

										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 						__lowerCamelCase   (				self					:     Any      )       ->List[str]:
										lowerCamelCase__       :						Union[str, Any]        =							self.get_image_processor()
										lowerCamelCase__       :						Tuple        =							self.get_tokenizer()
										lowerCamelCase__       :						Dict        =							self.get_qformer_tokenizer()

										lowerCamelCase__       :						Optional[int]        =							InstructBlipProcessor(
										    tokenizer=A		, image_processor=A		, qformer_tokenizer=A      )

										lowerCamelCase__       :						Any        =							'''lower newer'''
										lowerCamelCase__       :						Optional[int]        =							self.prepare_image_inputs()

										lowerCamelCase__       :						Tuple        =							processor(text=A		, images=A      )

										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(A      ):
													processor()







							def 						__lowerCamelCase   (				self					:     str      )       ->Tuple:
										lowerCamelCase__       :						Optional[Any]        =							self.get_image_processor()
										lowerCamelCase__       :						List[Any]        =							self.get_tokenizer()
										lowerCamelCase__       :						Dict        =							self.get_qformer_tokenizer()

										lowerCamelCase__       :						int        =							InstructBlipProcessor(
										    tokenizer=A		, image_processor=A		, qformer_tokenizer=A      )

										lowerCamelCase__       :						int        =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       :						Optional[Any]        =							processor.batch_decode(A      )
										lowerCamelCase__       :						int        =							tokenizer.batch_decode(A      )

										self.assertListEqual(A		, A      )







							def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
										lowerCamelCase__       :						List[str]        =							self.get_image_processor()
										lowerCamelCase__       :						Any        =							self.get_tokenizer()
										lowerCamelCase__       :						Tuple        =							self.get_qformer_tokenizer()

										lowerCamelCase__       :						Dict        =							InstructBlipProcessor(
										    tokenizer=A		, image_processor=A		, qformer_tokenizer=A      )

										lowerCamelCase__       :						int        =							'''lower newer'''
										lowerCamelCase__       :						Union[str, Any]        =							self.prepare_image_inputs()

										lowerCamelCase__       :						Dict        =							processor(text=A		, images=A      )

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










import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def 	_a						(   *UpperCAmelCase      ,  UpperCAmelCase = None      ,  UpperCAmelCase=True      ,  UpperCAmelCase=2					)    ->    str:







   """simple docstring"""
   from .. import __version__

   lowerCamelCase__       :						Optional[Any]        =							take_from
   lowerCamelCase__       :						Any        =							()
   if not isinstance(args[0]      ,  UpperCAmelCase					):
      lowerCamelCase__       :						Optional[int]        =							(args,)

   for attribute, version_name, message in args:
      if version.parse(version.parse(UpperCAmelCase					).base_version					) >= version.parse(UpperCAmelCase					):
         raise ValueError(
             f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
             f" version {__version__} is >= {version_name}"					)

      lowerCamelCase__       :						List[Any]        =							None
      if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and attribute in deprecated_kwargs:
         values += (deprecated_kwargs.pop(UpperCAmelCase					),)
         lowerCamelCase__       :						Tuple        =							f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
      elif hasattr(UpperCAmelCase      ,  UpperCAmelCase					):
         values += (getattr(UpperCAmelCase      ,  UpperCAmelCase					),)
         lowerCamelCase__       :						Union[str, Any]        =							f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
      elif deprecated_kwargs is None:
         lowerCamelCase__       :						int        =							f"`{attribute}` is deprecated and will be removed in version {version_name}."

      if warning is not None:
         lowerCamelCase__       :						int        =							warning + ''' ''' if standard_warn else ''''''
         warnings.warn(warning + message      ,  UpperCAmelCase      ,  stacklevel=UpperCAmelCase					)

   if isinstance(UpperCAmelCase      ,  UpperCAmelCase					) and len(UpperCAmelCase					) > 0:
      lowerCamelCase__       :						Tuple        =							inspect.getouterframes(inspect.currentframe()					)[1]
      lowerCamelCase__       :						Optional[Any]        =							call_frame.filename
      lowerCamelCase__       :						List[Any]        =							call_frame.lineno
      lowerCamelCase__       :						Optional[Any]        =							call_frame.function
      lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							next(iter(deprecated_kwargs.items()					)					)
      raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"					)

   if len(UpperCAmelCase					) == 0:
      return
   elif len(UpperCAmelCase					) == 1:
      return values[0]
   return values



