
'''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  UpperCAmelCase__						(       unittest.TestCase ):



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Tuple								=   tempfile.mkdtemp()

          _lowerCamelCase				:   Optional[Any]								=   BlipImageProcessor()
          _lowerCamelCase				:   Union[str, Any]								=   GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"     )

          _lowerCamelCase				:   List[str]								=   BlipaProcessor(__A,__A     )

          processor.save_pretrained(self.tmpdirname     )



    def   lowerCamelCase_  (	self      :       Union[str, Any],**__A      :       Any     ):
          return AutoProcessor.from_pretrained(self.tmpdirname,**__A     ).tokenizer



    def   lowerCamelCase_  (	self      :       Tuple,**__A      :       Any     ):
          return AutoProcessor.from_pretrained(self.tmpdirname,**__A     ).image_processor



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          shutil.rmtree(self.tmpdirname     )



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

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

          return image_inputs



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   List[Any]								=   BlipaProcessor(tokenizer=self.get_tokenizer(),image_processor=self.get_image_processor()     )
          processor.save_pretrained(self.tmpdirname     )

          _lowerCamelCase				:   Dict								=   self.get_tokenizer(bos_token="(BOS)",eos_token="(EOS)"     )
          _lowerCamelCase				:   Union[str, Any]								=   self.get_image_processor(do_normalize=__A,padding_value=1.0     )

          _lowerCamelCase				:   Union[str, Any]								=   BlipaProcessor.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      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   self.get_image_processor()
          _lowerCamelCase				:   Tuple								=   self.get_tokenizer()

          _lowerCamelCase				:   List[Any]								=   BlipaProcessor(tokenizer=__A,image_processor=__A     )

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

          _lowerCamelCase				:   Tuple								=   image_processor(__A,return_tensors="np"     )
          _lowerCamelCase				:   int								=   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      :       Dict     ):
          _lowerCamelCase				:   Tuple								=   self.get_image_processor()
          _lowerCamelCase				:   Optional[int]								=   self.get_tokenizer()

          _lowerCamelCase				:   Tuple								=   BlipaProcessor(tokenizer=__A,image_processor=__A     )

          _lowerCamelCase				:   Dict								=   "lower newer"

          _lowerCamelCase				:   Optional[Any]								=   processor(text=__A     )

          _lowerCamelCase				:   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      :       Union[str, Any]     ):
          _lowerCamelCase				:   Optional[int]								=   self.get_image_processor()
          _lowerCamelCase				:   Dict								=   self.get_tokenizer()

          _lowerCamelCase				:   int								=   BlipaProcessor(tokenizer=__A,image_processor=__A     )

          _lowerCamelCase				:   Any								=   "lower newer"
          _lowerCamelCase				:   int								=   self.prepare_image_inputs()

          _lowerCamelCase				:   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]     ):
          _lowerCamelCase				:   str								=   self.get_image_processor()
          _lowerCamelCase				:   Tuple								=   self.get_tokenizer()

          _lowerCamelCase				:   Tuple								=   BlipaProcessor(tokenizer=__A,image_processor=__A     )

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

          _lowerCamelCase				:   Tuple								=   processor.batch_decode(__A     )
          _lowerCamelCase				:   Any								=   tokenizer.batch_decode(__A     )

          self.assertListEqual(__A,__A     )



    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   int								=   self.get_image_processor()
          _lowerCamelCase				:   List[str]								=   self.get_tokenizer()

          _lowerCamelCase				:   str								=   BlipaProcessor(tokenizer=__A,image_processor=__A     )

          _lowerCamelCase				:   List[Any]								=   "lower newer"
          _lowerCamelCase				:   List[str]								=   self.prepare_image_inputs()

          _lowerCamelCase				:   List[Any]								=   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 typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_       :   Tuple   =       {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 UpperCAmelCase_       :   Tuple   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)