




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, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    tempfile.mkdtemp()

          __UpperCamelCase			=    CLIPImageProcessor()
          __UpperCamelCase			=    BertTokenizer.from_pretrained(
              '''hf-internal-testing/tiny-random-BertModel'''			,							model_input_names=['''input_ids''', '''attention_mask''']					)

          __UpperCamelCase			=    GitProcessor(snake_case			,							snake_case					)

          processor.save_pretrained(self.tmpdirname					)







      def    snake_case						(  self       :			Optional[Any]			,							**snake_case       :			List[Any]					):
          return AutoProcessor.from_pretrained(self.tmpdirname			,							**snake_case					).tokenizer







      def    snake_case						(  self       :			Optional[int]			,							**snake_case       :			Dict					):
          return AutoProcessor.from_pretrained(self.tmpdirname			,							**snake_case					).image_processor







      def    snake_case						(  self       :			List[Any]					):
          shutil.rmtree(self.tmpdirname					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta					)]

          __UpperCamelCase			=    [Image.fromarray(np.moveaxis(snake_case			,							0			,							-1					)					) for x in image_inputs]

          return image_inputs







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    GitProcessor(tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()					)
          processor.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''					)
          __UpperCamelCase			=    self.get_image_processor(do_normalize=snake_case			,							padding_value=1.0					)

          __UpperCamelCase			=    GitProcessor.from_pretrained(
              self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=snake_case			,							padding_value=1.0					)

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

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







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    self.prepare_image_inputs()

          __UpperCamelCase			=    image_processor(snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(images=snake_case			,							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    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    '''lower newer'''

          __UpperCamelCase			=    processor(text=snake_case					)

          __UpperCamelCase			=    tokenizer(snake_case			,							return_token_type_ids=snake_case					)

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







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    '''lower newer'''
          __UpperCamelCase			=    self.prepare_image_inputs()

          __UpperCamelCase			=    processor(text=snake_case			,							images=snake_case					)

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

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







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_image_processor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    GitProcessor(tokenizer=snake_case			,							image_processor=snake_case					)

          __UpperCamelCase			=    '''lower newer'''
          __UpperCamelCase			=    self.prepare_image_inputs()

          __UpperCamelCase			=    processor(text=snake_case			,							images=snake_case					)

          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys()					)			,							['''input_ids''', '''attention_mask''', '''pixel_values''']					)













import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


_UpperCAmelCase										=					logging.get_logger(__name__)



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       def __init__(		self:			Tuple       , *_SCREAMING_SNAKE_CASE:			List[Any]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              warnings.warn(
                  "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
                  " Please use ConvNextImageProcessor instead."       , _SCREAMING_SNAKE_CASE       , )
              super().__init__(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

