



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 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   tempfile.mkdtemp()

					_snake_case					:							Optional[Any]					     =   CLIPImageProcessor()
					_snake_case					:							Optional[Any]					     =   BertTokenizer.from_pretrained(
					    """hf-internal-testing/tiny-random-BertModel"""						,	model_input_names=["""input_ids""", """attention_mask"""]      )

					_snake_case					:							List[Any]					     =   GitProcessor(lowerCamelCase_						,	lowerCamelCase_      )

					processor.save_pretrained(self.tmpdirname      )







				def 		__lowerCamelCase(				self						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self						,	**SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					shutil.rmtree(self.tmpdirname      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   [np.random.randint(2_55						,	size=(3, 30, 4_00)						,	dtype=np.uinta      )]

					_snake_case					:							Union[str, Any]					     =   [Image.fromarray(np.moveaxis(lowerCamelCase_						,	0						,	-1      )      ) for x in image_inputs]

					return image_inputs







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   GitProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()      )
					processor.save_pretrained(self.tmpdirname      )

					_snake_case					:							List[Any]					     =   self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
					_snake_case					:							int					     =   self.get_image_processor(do_normalize=lowerCamelCase_						,	padding_value=1.0      )

					_snake_case					:							List[Any]					     =   GitProcessor.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 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							Dict					     =   GitProcessor(tokenizer=lowerCamelCase_						,	image_processor=lowerCamelCase_      )

					_snake_case					:							Dict					     =   self.prepare_image_inputs()

					_snake_case					:							Optional[Any]					     =   image_processor(lowerCamelCase_						,	return_tensors="""np"""      )
					_snake_case					:							Optional[Any]					     =   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 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							Union[str, Any]					     =   GitProcessor(tokenizer=lowerCamelCase_						,	image_processor=lowerCamelCase_      )

					_snake_case					:							Union[str, Any]					     =   """lower newer"""

					_snake_case					:							int					     =   processor(text=lowerCamelCase_      )

					_snake_case					:							List[Any]					     =   tokenizer(lowerCamelCase_						,	return_token_type_ids=lowerCamelCase_      )

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   self.get_image_processor()
					_snake_case					:							Optional[int]					     =   self.get_tokenizer()

					_snake_case					:							List[str]					     =   GitProcessor(tokenizer=lowerCamelCase_						,	image_processor=lowerCamelCase_      )

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

					_snake_case					:							List[str]					     =   processor(text=lowerCamelCase_						,	images=lowerCamelCase_      )

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

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							List[str]					     =   GitProcessor(tokenizer=lowerCamelCase_						,	image_processor=lowerCamelCase_      )

					_snake_case					:							str					     =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					_snake_case					:							Optional[int]					     =   processor.batch_decode(lowerCamelCase_      )
					_snake_case					:							Any					     =   tokenizer.batch_decode(lowerCamelCase_      )

					self.assertListEqual(lowerCamelCase_						,	lowerCamelCase_      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							Optional[int]					     =   GitProcessor(tokenizer=lowerCamelCase_						,	image_processor=lowerCamelCase_      )

					_snake_case					:							Tuple					     =   """lower newer"""
					_snake_case					:							Any					     =   self.prepare_image_inputs()

					_snake_case					:							Any					     =   processor(text=lowerCamelCase_						,	images=lowerCamelCase_      )

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







"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


SCREAMING_SNAKE_CASE_  					= logging.get_logger(__name__)






class      __a      (       lowerCAmelCase_      ):







       def __init__(       self				,*lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          warnings.warn(
              "The class MobileNetV1FeatureExtractor is deprecated and will be removed in version 5 of Transformers."
              " Please use MobileNetV1ImageProcessor instead."				,lowerCamelCase_				,)
          super().__init__(*lowerCamelCase_				,**lowerCamelCase_		)

