






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 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							tempfile.mkdtemp()

         UpperCamelCase_:   str        =							BlipImageProcessor()
         UpperCamelCase_:   str        =							BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'				)

         UpperCamelCase_:   Dict        =							BlipProcessor(_lowerCamelCase    ,     _lowerCamelCase				)

         processor.save_pretrained(self.tmpdirname				)





       def      _a       (   self    ,     **_lowerCamelCase				):
         return AutoProcessor.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase				).tokenizer





       def      _a       (   self    ,     **_lowerCamelCase				):
         return AutoProcessor.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase				).image_processor





       def      _a       (   self				):
         shutil.rmtree(self.tmpdirname				)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							[np.random.randint(2_5_5    ,     size=(3, 3_0, 4_0_0)    ,     dtype=np.uinta				)]

         UpperCamelCase_:   int        =							[Image.fromarray(np.moveaxis(_lowerCamelCase    ,     0    ,     -1				)				) for x in image_inputs]

         return image_inputs





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							BlipProcessor(tokenizer=self.get_tokenizer()    ,     image_processor=self.get_image_processor()				)
         processor.save_pretrained(self.tmpdirname				)

         UpperCamelCase_:   List[Any]        =							self.get_tokenizer(bos_token='(BOS)'    ,     eos_token='(EOS)'				)
         UpperCamelCase_:   Any        =							self.get_image_processor(do_normalize=_lowerCamelCase    ,     padding_value=1.0				)

         UpperCamelCase_:   List[str]        =							BlipProcessor.from_pretrained(
             self.tmpdirname    ,     bos_token='(BOS)'    ,     eos_token='(EOS)'    ,     do_normalize=_lowerCamelCase    ,     padding_value=1.0				)

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

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





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							self.get_image_processor()
         UpperCamelCase_:   Optional[int]        =							self.get_tokenizer()

         UpperCamelCase_:   Tuple        =							BlipProcessor(tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase				)

         UpperCamelCase_:   Union[str, Any]        =							self.prepare_image_inputs()

         UpperCamelCase_:   Optional[int]        =							image_processor(_lowerCamelCase    ,     return_tensors='np'				)
         UpperCamelCase_:   Optional[Any]        =							processor(images=_lowerCamelCase    ,     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				):
         UpperCamelCase_:   Union[str, Any]        =							self.get_image_processor()
         UpperCamelCase_:   Tuple        =							self.get_tokenizer()

         UpperCamelCase_:   Union[str, Any]        =							BlipProcessor(tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase				)

         UpperCamelCase_:   Any        =							'lower newer'

         UpperCamelCase_:   List[str]        =							processor(text=_lowerCamelCase				)

         UpperCamelCase_:   int        =							tokenizer(_lowerCamelCase    ,     return_token_type_ids=_lowerCamelCase				)

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





       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							self.get_image_processor()
         UpperCamelCase_:   Optional[int]        =							self.get_tokenizer()

         UpperCamelCase_:   Optional[int]        =							BlipProcessor(tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase				)

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

         UpperCamelCase_:   List[str]        =							processor(text=_lowerCamelCase    ,     images=_lowerCamelCase				)

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

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





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							self.get_image_processor()
         UpperCamelCase_:   Optional[int]        =							self.get_tokenizer()

         UpperCamelCase_:   Union[str, Any]        =							BlipProcessor(tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase				)

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

         UpperCamelCase_:   List[str]        =							processor.batch_decode(_lowerCamelCase				)
         UpperCamelCase_:   Union[str, Any]        =							tokenizer.batch_decode(_lowerCamelCase				)

         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							self.get_image_processor()
         UpperCamelCase_:   int        =							self.get_tokenizer()

         UpperCamelCase_:   List[Any]        =							BlipProcessor(tokenizer=_lowerCamelCase    ,     image_processor=_lowerCamelCase				)

         UpperCamelCase_:   Tuple        =							'lower newer'
         UpperCamelCase_:   List[str]        =							self.prepare_image_inputs()

         UpperCamelCase_:   Dict        =							processor(text=_lowerCamelCase    ,     images=_lowerCamelCase				)

         # 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 functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def   _A					(	*_a	:						int				):



      """simple docstring"""






      if not isinstance(_a     ,   _a				):
            A				  =		list(_a				)
      for i in range(len(_a				)				):
            A				  =		None
      gc.collect()
      if is_xpu_available():
            torch.xpu.empty_cache()
      elif is_npu_available():
            torch.npu.empty_cache()
      else:
            torch.cuda.empty_cache()
      return objects



def   _A					(	_a	:						Exception				):



      """simple docstring"""






      A				  =		[
          """CUDA out of memory.""",  # CUDA OOM
          """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
          """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
      ]
      if isinstance(_a     ,   _a				) and len(exception.args				) == 1:
            return any(err in exception.args[0] for err in _statements				)
      return False





def   _A					(	_a	:						callable = None     ,   _a	:						int = 1_2_8				):



      """simple docstring"""






      if function is None:
            return functools.partial(_a     ,   starting_batch_size=_a				)

      A				  =		starting_batch_size

      def decorator(*_a	:						Union[str, Any]     ,   **_a	:						List[Any]				):
            nonlocal batch_size
            gc.collect()
            if is_xpu_available():
                  torch.xpu.empty_cache()
            elif is_npu_available():
                  torch.npu.empty_cache()
            else:
                  torch.cuda.empty_cache()
            A				  =		list(inspect.signature(_a				).parameters.keys()				)
            # Guard against user error
            if len(_a				) < (len(_a				) + 1):
                  A				  =		""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]     ,   args[1:]				)]				)
                  raise TypeError(
                      f'Batch size was passed into `{function.__name__}` as the first argument when called.'
                      f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'				)
            while True:
                  if batch_size == 0:
                        raise RuntimeError("""No executable batch size found, reached zero."""				)
                  try:
                        return function(_a     ,   *_a     ,   **_a				)
                  except Exception as e:
                        if should_reduce_batch_size(_a				):
                              gc.collect()
                              if is_xpu_available():
                                    torch.xpu.empty_cache()
                              elif is_npu_available():
                                    torch.npu.empty_cache()
                              else:
                                    torch.cuda.empty_cache()
                              batch_size //= 2
                        else:
                              raise

      return decorator




