



'''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 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	tempfile.mkdtemp()

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	BlipImageProcessor()
					__SCREAMING_SNAKE_CASE :	str					 =	BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BlipProcessor(_lowerCAmelCase							,  _lowerCAmelCase		)

					processor.save_pretrained(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				List[str]							,  **lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Tuple							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






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

			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					shutil.rmtree(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






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

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

					return image_inputs

			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[str]					 =	BlipProcessor(tokenizer=self.get_tokenizer()							,  image_processor=self.get_image_processor()		)
					processor.save_pretrained(self.tmpdirname		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.get_tokenizer(bos_token="""(BOS)"""							,  eos_token="""(EOS)"""		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.get_image_processor(do_normalize=_lowerCAmelCase							,  padding_value=1.0		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	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 UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE :	List[str]					 =	BlipProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase		)

					__SCREAMING_SNAKE_CASE :	Any					 =	self.prepare_image_inputs()

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	image_processor(_lowerCAmelCase							,  return_tensors="""np"""		)
					__SCREAMING_SNAKE_CASE :	int					 =	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 UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE :	str					 =	BlipProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase		)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""lower newer"""

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	processor(text=_lowerCAmelCase		)

					__SCREAMING_SNAKE_CASE :	int					 =	tokenizer(_lowerCAmelCase							,  return_token_type_ids=_lowerCAmelCase		)

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

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.get_image_processor()
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.get_tokenizer()

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BlipProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	"""lower newer"""
					__SCREAMING_SNAKE_CASE :	str					 =	self.prepare_image_inputs()

					__SCREAMING_SNAKE_CASE :	List[str]					 =	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 UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	self.get_image_processor()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.get_tokenizer()

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	BlipProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase		)

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

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	processor.batch_decode(_lowerCAmelCase		)
					__SCREAMING_SNAKE_CASE :	int					 =	tokenizer.batch_decode(_lowerCAmelCase		)

					self.assertListEqual(_lowerCAmelCase							,  _lowerCAmelCase		)



			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	BlipProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	"""lower newer"""
					__SCREAMING_SNAKE_CASE :	Dict					 =	self.prepare_image_inputs()

					__SCREAMING_SNAKE_CASE :	List[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'''




import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase__ :			List[str]												=				logging.get_logger(__name__)






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Union[str, Any]							,  *lowerCAmelCase__  :				str							,  **lowerCAmelCase__  :				Any		):





					"""simple docstring"""






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