






"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class   UpperCamelCase_ (       unittest.TestCase  ):



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase	    =			["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
         # fmt: on
         UpperCAmelCase	    =			os.path.join(self.tmpdirname ,		VOCAB_FILES_NAMES["""vocab_file"""]				)
         with open(self.vocab_file ,		"""w""" ,		encoding="""utf-8"""				) as vocab_writer:
                vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

         UpperCAmelCase	    =			{
             """do_resize""": True,
             """size""": {"""height""": 18, """width""": 18},
             """do_normalize""": True,
             """image_mean""": [0.5, 0.5, 0.5],
             """image_std""": [0.5, 0.5, 0.5],
         }
         UpperCAmelCase	    =			os.path.join(self.tmpdirname ,		snake_case__				)
         with open(self.image_processor_file ,		"""w""" ,		encoding="""utf-8"""				) as fp:
                json.dump(snake_case__ ,		snake_case__				)



  def 		UpperCamelCase_				(      self ,		**snake_case__				)			->			Optional[int]:



         """simple docstring"""





         return BertTokenizer.from_pretrained(self.tmpdirname ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		**snake_case__				)			->			int:



         """simple docstring"""





         return ViTImageProcessor.from_pretrained(self.tmpdirname ,		**snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         shutil.rmtree(self.tmpdirname				)



  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





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

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

         return image_inputs



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_tokenizer()
         UpperCAmelCase	    =			self.get_image_processor()

         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(tokenizer=snake_case__ ,		image_processor=snake_case__				)

         processor.save_pretrained(self.tmpdirname				)
         UpperCAmelCase	    =			VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname				)

         self.assertEqual(processor.tokenizer.get_vocab() ,		tokenizer.get_vocab()				)
         self.assertIsInstance(processor.tokenizer ,		(BertTokenizer, BertTokenizerFast)				)

         self.assertEqual(processor.image_processor.to_json_string() ,		image_processor.to_json_string()				)
         self.assertIsInstance(processor.image_processor ,		snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(
             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	    =			VisionTextDualEncoderProcessor.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 ,		(BertTokenizer, BertTokenizerFast)				)

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



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_image_processor()
         UpperCAmelCase	    =			self.get_tokenizer()

         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(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 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_image_processor()
         UpperCAmelCase	    =			self.get_tokenizer()

         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(tokenizer=snake_case__ ,		image_processor=snake_case__				)

         UpperCAmelCase	    =			"""lower newer"""

         UpperCAmelCase	    =			processor(text=snake_case__				)

         UpperCAmelCase	    =			tokenizer(snake_case__				)

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



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_image_processor()
         UpperCAmelCase	    =			self.get_tokenizer()

         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(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""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

         # test if it raises when no input is passed
         with self.assertRaises(snake_case__				):
                processor()



  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_image_processor()
         UpperCAmelCase	    =			self.get_tokenizer()

         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(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 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         UpperCAmelCase	    =			self.get_image_processor()
         UpperCAmelCase	    =			self.get_tokenizer()

         UpperCAmelCase	    =			VisionTextDualEncoderProcessor(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()				) ,		processor.model_input_names				)








"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class   UpperCamelCase_ (       a_  ):
  _A       :					Optional[int]				=	['image_processor', 'tokenizer']
  _A       :					Optional[Any]				=	'ViTImageProcessor'
  _A       :					Tuple				=	('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(      self ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                    """ instead.""" ,		snake_case__ ,		)
                UpperCAmelCase	    =			kwargs.pop("""feature_extractor"""				)

         UpperCAmelCase	    =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError("""You need to specify an `image_processor`."""				)
         if tokenizer is None:
                raise ValueError("""You need to specify a `tokenizer`."""				)

         super().__init__(snake_case__ ,		snake_case__				)



  def __call__(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		snake_case__=None ,		**snake_case__				)			->			List[str]:



         """simple docstring"""





         if text is None and visual_prompt is None and images is None:
                raise ValueError("""You have to specify either text, visual prompt or images."""				)

         if text is not None and visual_prompt is not None:
                raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""				)

         if text is not None:
                UpperCAmelCase	    =			self.tokenizer(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if images is not None:
                UpperCAmelCase	    =			self.image_processor(snake_case__ ,		return_tensors=snake_case__ ,		**snake_case__				)

         if visual_prompt is not None and images is not None:
                UpperCAmelCase	    =			{
                    """pixel_values""": image_features.pixel_values,
                    """conditional_pixel_values""": prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                UpperCAmelCase	    =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                UpperCAmelCase	    =			{
                    """conditional_pixel_values""": prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**snake_case__				) ,		tensor_type=snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Tuple:



         """simple docstring"""





         return self.tokenizer.batch_decode(*snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		*snake_case__ ,		**snake_case__				)			->			Optional[int]:



         """simple docstring"""





         return self.tokenizer.decode(*snake_case__ ,		**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,		snake_case__ ,		)
         return self.image_processor_class



  @property
  def 		UpperCamelCase_				(      self				)			->			Any:



         """simple docstring"""





         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,		snake_case__ ,		)
         return self.image_processor
