



'''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 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    List[Any]      ):
      a		:   Dict	  =	tempfile.mkdtemp()

      a		:   List[Any]	  =	BlipImageProcessor()
      a		:   Optional[Any]	  =	GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'      )

      a		:   int	  =	BlipaProcessor(__lowerCamelCase		,     __lowerCamelCase      )

      processor.save_pretrained(self.tmpdirname      )







    def      lowercase_			( self					:    int		,     **__snake_case					:    Optional[int]      ):
      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCamelCase      ).tokenizer







    def      lowercase_			( self					:    int		,     **__snake_case					:    List[Any]      ):
      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCamelCase      ).image_processor







    def      lowercase_			( self					:    List[str]      ):
      shutil.rmtree(self.tmpdirname      )







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   List[str]	  =	[np.random.randint(2_55		,     size=(3, 30, 4_00)		,     dtype=np.uinta      )]

      a		:   Optional[Any]	  =	[Image.fromarray(np.moveaxis(__lowerCamelCase		,     0		,     -1      )      ) for x in image_inputs]

      return image_inputs







    def      lowercase_			( self					:    str      ):
      a		:   Union[str, Any]	  =	BlipaProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor()      )
      processor.save_pretrained(self.tmpdirname      )

      a		:   Optional[int]	  =	self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)'      )
      a		:   str	  =	self.get_image_processor(do_normalize=__lowerCamelCase		,     padding_value=1.0      )

      a		:   Optional[int]	  =	BlipaProcessor.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      lowercase_			( self					:    int      ):
      a		:   Any	  =	self.get_image_processor()
      a		:   List[str]	  =	self.get_tokenizer()

      a		:   Dict	  =	BlipaProcessor(tokenizer=__lowerCamelCase		,     image_processor=__lowerCamelCase      )

      a		:   str	  =	self.prepare_image_inputs()

      a		:   List[Any]	  =	image_processor(__lowerCamelCase		,     return_tensors='np'      )
      a		:   List[str]	  =	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      lowercase_			( self					:    Dict      ):
      a		:   Optional[Any]	  =	self.get_image_processor()
      a		:   List[Any]	  =	self.get_tokenizer()

      a		:   Dict	  =	BlipaProcessor(tokenizer=__lowerCamelCase		,     image_processor=__lowerCamelCase      )

      a		:   Optional[Any]	  =	'lower newer'

      a		:   Any	  =	processor(text=__lowerCamelCase      )

      a		:   str	  =	tokenizer(__lowerCamelCase		,     return_token_type_ids=__lowerCamelCase      )

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







    def      lowercase_			( self					:    int      ):
      a		:   Union[str, Any]	  =	self.get_image_processor()
      a		:   Tuple	  =	self.get_tokenizer()

      a		:   Optional[int]	  =	BlipaProcessor(tokenizer=__lowerCamelCase		,     image_processor=__lowerCamelCase      )

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

      a		:   Any	  =	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      lowercase_			( self					:    Optional[int]      ):
      a		:   Tuple	  =	self.get_image_processor()
      a		:   Optional[Any]	  =	self.get_tokenizer()

      a		:   Dict	  =	BlipaProcessor(tokenizer=__lowerCamelCase		,     image_processor=__lowerCamelCase      )

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

      a		:   str	  =	processor.batch_decode(__lowerCamelCase      )
      a		:   Tuple	  =	tokenizer.batch_decode(__lowerCamelCase      )

      self.assertListEqual(__lowerCamelCase		,     __lowerCamelCase      )







    def      lowercase_			( self					:    str      ):
      a		:   Any	  =	self.get_image_processor()
      a		:   str	  =	self.get_tokenizer()

      a		:   List[str]	  =	BlipaProcessor(tokenizer=__lowerCamelCase		,     image_processor=__lowerCamelCase      )

      a		:   str	  =	'lower newer'
      a		:   Optional[Any]	  =	self.prepare_image_inputs()

      a		:   str	  =	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']      )



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''image_processor''', '''tokenizer''']
      UpperCamelCase										=							'''BridgeTowerImageProcessor'''
      UpperCamelCase										=							('''RobertaTokenizer''', '''RobertaTokenizerFast''')





      def __init__(		self    :       int       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       List[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         super().__init__(__lowerCamelCase       ,							__lowerCamelCase							)





      def __call__(		self    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[bool, str, PaddingStrategy] = False       ,							__lowerCamelCase    :       Union[bool, str, TruncationStrategy] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       int = 0       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> BatchEncoding:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(
             text=__lowerCamelCase       ,							add_special_tokens=__lowerCamelCase       ,							padding=__lowerCamelCase       ,							truncation=__lowerCamelCase       ,							max_length=__lowerCamelCase       ,							stride=__lowerCamelCase       ,							pad_to_multiple_of=__lowerCamelCase       ,							return_token_type_ids=__lowerCamelCase       ,							return_attention_mask=__lowerCamelCase       ,							return_overflowing_tokens=__lowerCamelCase       ,							return_special_tokens_mask=__lowerCamelCase       ,							return_offsets_mapping=__lowerCamelCase       ,							return_length=__lowerCamelCase       ,							verbose=__lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							**__lowerCamelCase       ,							)
         # add pixel_values + pixel_mask
         UpperCAmelCase      					=  self.image_processor(
             __lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_center_crop=__lowerCamelCase       ,							**__lowerCamelCase							)
         encoding.update(__lowerCamelCase							)

         return encoding





      def 							_lowercase					(		self    :       Optional[Any]       ,							*__lowerCamelCase    :       Optional[int]       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







         return self.tokenizer.batch_decode(*__lowerCamelCase       ,							**__lowerCamelCase							)





      def 							_lowercase					(		self    :       Dict       ,							*__lowerCamelCase    :       Any       ,							**__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         return self.tokenizer.decode(*__lowerCamelCase       ,							**__lowerCamelCase							)





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer.model_input_names
         UpperCAmelCase      					=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



