






"""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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast






@require_vision
class 		snake_case						(						unittest.TestCase ):





    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      a  :    Tuple       =     tempfile.mkdtemp()

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

      a  :    Optional[int]       =     BlipProcessor(A       ,	A    )

      processor.save_pretrained(self.tmpdirname    )





    def        lowerCamelCase__   (							self			:      List[Any]       ,	**A			:      Tuple    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	**A			:      List[Any]    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      shutil.rmtree(self.tmpdirname    )





    def        lowerCamelCase__   (							self			:      str    ):


      '''simple docstring'''




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

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

      return image_inputs





    def        lowerCamelCase__   (							self			:      Optional[Any]    ):


      '''simple docstring'''




      a  :    Dict       =     BlipProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

      a  :    str       =     self.get_tokenizer(bos_token='(BOS)'       ,	eos_token='(EOS)'    )
      a  :    Union[str, Any]       =     self.get_image_processor(do_normalize=A       ,	padding_value=1.0    )

      a  :    Union[str, Any]       =     BlipProcessor.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    )





    def        lowerCamelCase__   (							self			:      Union[str, Any]    ):


      '''simple docstring'''




      a  :    int       =     self.get_image_processor()
      a  :    Dict       =     self.get_tokenizer()

      a  :    Union[str, Any]       =     BlipProcessor(tokenizer=A       ,	image_processor=A    )

      a  :    Tuple       =     self.prepare_image_inputs()

      a  :    Tuple       =     image_processor(A       ,	return_tensors='np'    )
      a  :    Dict       =     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			:      List[str]    ):


      '''simple docstring'''




      a  :    int       =     self.get_image_processor()
      a  :    List[Any]       =     self.get_tokenizer()

      a  :    Dict       =     BlipProcessor(tokenizer=A       ,	image_processor=A    )

      a  :    Tuple       =     'lower newer'

      a  :    Dict       =     processor(text=A    )

      a  :    Union[str, Any]       =     tokenizer(A       ,	return_token_type_ids=A    )

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





    def        lowerCamelCase__   (							self			:      Dict    ):


      '''simple docstring'''




      a  :    Dict       =     self.get_image_processor()
      a  :    int       =     self.get_tokenizer()

      a  :    Optional[Any]       =     BlipProcessor(tokenizer=A       ,	image_processor=A    )

      a  :    int       =     'lower newer'
      a  :    Tuple       =     self.prepare_image_inputs()

      a  :    List[Any]       =     processor(text=A       ,	images=A    )

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

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





    def        lowerCamelCase__   (							self			:      List[Any]    ):


      '''simple docstring'''




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

      a  :    Dict       =     BlipProcessor(tokenizer=A       ,	image_processor=A    )

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

      a  :    int       =     processor.batch_decode(A    )
      a  :    List[str]       =     tokenizer.batch_decode(A    )

      self.assertListEqual(A       ,	A    )





    def        lowerCamelCase__   (							self			:      Optional[int]    ):


      '''simple docstring'''




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

      a  :    Union[str, Any]       =     BlipProcessor(tokenizer=A       ,	image_processor=A    )

      a  :    Dict       =     'lower newer'
      a  :    Tuple       =     self.prepare_image_inputs()

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

      # 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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments




