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 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  tempfile.mkdtemp()

         UpperCAmelCase__							:				Union[str, Any]   				=  BlipImageProcessor()
         UpperCAmelCase__							:				str   				=  BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''							)

         UpperCAmelCase__							:				str   				=  BlipProcessor(__lowerCAmelCase					,__lowerCAmelCase							)

         processor.save_pretrained(self.tmpdirname							)




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			int:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         shutil.rmtree(self.tmpdirname							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  [np.random.randint(255					,size=(3, 30, 400)					,dtype=np.uinta							)]

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

         return image_inputs




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



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

         UpperCAmelCase__							:				str   				=  self.get_tokenizer(bos_token='''(BOS)'''					,eos_token='''(EOS)'''							)
         UpperCAmelCase__							:				Union[str, 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 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.get_image_processor()
         UpperCAmelCase__							:				Tuple   				=  self.get_tokenizer()

         UpperCAmelCase__							:				str   				=  BlipProcessor(tokenizer=__lowerCAmelCase					,image_processor=__lowerCAmelCase							)

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

         UpperCAmelCase__							:				Union[str, Any]   				=  image_processor(__lowerCAmelCase					,return_tensors='''np'''							)
         UpperCAmelCase__							:				Dict   				=  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 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_image_processor()
         UpperCAmelCase__							:				List[Any]   				=  self.get_tokenizer()

         UpperCAmelCase__							:				str   				=  BlipProcessor(tokenizer=__lowerCAmelCase					,image_processor=__lowerCAmelCase							)

         UpperCAmelCase__							:				Any   				=  '''lower newer'''

         UpperCAmelCase__							:				Optional[Any]   				=  processor(text=__lowerCAmelCase							)

         UpperCAmelCase__							:				Optional[Any]   				=  tokenizer(__lowerCAmelCase					,return_token_type_ids=__lowerCAmelCase							)

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




      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.get_image_processor()
         UpperCAmelCase__							:				Dict   				=  self.get_tokenizer()

         UpperCAmelCase__							:				str   				=  BlipProcessor(tokenizer=__lowerCAmelCase					,image_processor=__lowerCAmelCase							)

         UpperCAmelCase__							:				Tuple   				=  '''lower newer'''
         UpperCAmelCase__							:				Optional[Any]   				=  self.prepare_image_inputs()

         UpperCAmelCase__							:				Dict   				=  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 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.get_image_processor()
         UpperCAmelCase__							:				str   				=  self.get_tokenizer()

         UpperCAmelCase__							:				Union[str, Any]   				=  BlipProcessor(tokenizer=__lowerCAmelCase					,image_processor=__lowerCAmelCase							)

         UpperCAmelCase__							:				List[str]   				=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         UpperCAmelCase__							:				Tuple   				=  processor.batch_decode(__lowerCAmelCase							)
         UpperCAmelCase__							:				int   				=  tokenizer.batch_decode(__lowerCAmelCase							)

         self.assertListEqual(__lowerCAmelCase					,__lowerCAmelCase							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.get_image_processor()
         UpperCAmelCase__							:				str   				=  self.get_tokenizer()

         UpperCAmelCase__							:				Any   				=  BlipProcessor(tokenizer=__lowerCAmelCase					,image_processor=__lowerCAmelCase							)

         UpperCAmelCase__							:				List[Any]   				=  '''lower newer'''
         UpperCAmelCase__							:				Tuple   				=  self.prepare_image_inputs()

         UpperCAmelCase__							:				Optional[Any]   				=  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'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__     :	List[Any]				=			{'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__     :	int				=			['YolosFeatureExtractor']
  UpperCamelCase__     :	int				=			['YolosImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__     :	int				=			[
      'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'YolosForObjectDetection',
      'YolosModel',
      'YolosPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_yolos import YolosFeatureExtractor
    from .image_processing_yolos import YolosImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_yolos import (
        YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
        YolosForObjectDetection,
        YolosModel,
        YolosPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__     :	int				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




