
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__lowerCamelCase							=       logging.get_logger(__name__)






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     ["pixel_values"]



   def __init__(self		:					str    ,						snake_case__		:					bool = True    ,						snake_case__		:					Union[int, float] = 1 / 2_55    ,						snake_case__		:					bool = True    ,						snake_case__		:					int = 8    ,						**snake_case__		:					str    ,						)		->	None:


          '''simple docstring'''


          super().__init__(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_rescale
          snake_case       :   Union[str, Any]							=      rescale_factor
          snake_case       :   str							=      do_pad
          snake_case       :   Union[str, Any]							=      pad_size



   def 				_SCREAMING_SNAKE_CASE   (self		:					int    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					float    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Union[str, Any] )		->	np.ndarray:


          '''simple docstring'''


          return rescale(snake_case__    ,						scale=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					int    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None )		->	Optional[int]:


          '''simple docstring'''


          snake_case				,					snake_case       :   Tuple							=      get_image_size(snake_case__ )
          snake_case       :   List[Any]							=      (old_height // size + 1) * size - old_height
          snake_case       :   int							=      (old_width // size + 1) * size - old_width

          return pad(snake_case__    ,						((0, pad_height), (0, pad_width))    ,						mode="symmetric"    ,						data_format=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					ImageInput    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[float] = None    ,						snake_case__		:					Optional[bool] = None    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**snake_case__		:					Tuple    ,						)		->	List[str]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   Tuple							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   int							=      do_pad if do_pad is not None else self.do_pad
          snake_case       :   Tuple							=      pad_size if pad_size is not None else self.pad_size

          snake_case       :   Tuple							=      make_list_of_images(snake_case__ )

          if not valid_images(snake_case__ ):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray." )

          if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True." )

          # All transformations expect numpy arrays.
          snake_case       :   List[str]							=      [to_numpy_array(snake_case__ ) for image in images]

          if do_rescale:
                 snake_case       :   Optional[Any]							=      [self.rescale(image=snake_case__    ,						scale=snake_case__ ) for image in images]

          if do_pad:
                 snake_case       :   List[str]							=      [self.pad(snake_case__    ,						size=snake_case__ ) for image in images]

          snake_case       :   Any							=      [to_channel_dimension_format(snake_case__    ,						snake_case__ ) for image in images]

          snake_case       :   Tuple							=      {"pixel_values": images}
          return BatchFeature(data=snake_case__    ,						tensor_type=snake_case__ )







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 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      tempfile.mkdtemp()

          # fmt: off
          snake_case       :   Optional[int]							=      ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
          # fmt: on
          snake_case       :   List[str]							=      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] ) )

          snake_case       :   int							=      {
              "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],
          }
          snake_case       :   Optional[Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					str )		->	Optional[int]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						**snake_case__		:					List[str] )		->	int:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	str:


          '''simple docstring'''


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

          snake_case       :   Optional[int]							=      [Image.fromarray(np.moveaxis(snake_case__    ,						0    ,						-1 ) ) for x in image_inputs]

          return image_inputs



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


          '''simple docstring'''


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

          snake_case       :   Optional[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          processor.save_pretrained(self.tmpdirname )
          snake_case       :   Any							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   str							=      VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

          snake_case       :   Optional[int]							=      self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)" )
          snake_case       :   Tuple							=      self.get_image_processor(do_normalize=snake_case__    ,						padding_value=1.0 )

          snake_case       :   List[str]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	int:


          '''simple docstring'''


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

          snake_case       :   List[Any]							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

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

          snake_case       :   str							=      image_processor(snake_case__    ,						return_tensors="np" )
          snake_case       :   Any							=      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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.get_image_processor()
          snake_case       :   int							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Tuple							=      "lower newer"

          snake_case       :   Tuple							=      processor(text=snake_case__ )

          snake_case       :   Union[str, Any]							=      tokenizer(snake_case__ )

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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	Optional[int]:


          '''simple docstring'''


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

          snake_case       :   Dict							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   int							=      "lower newer"
          snake_case       :   Dict							=      self.prepare_image_inputs()

          snake_case       :   Union[str, Any]							=      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 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Tuple:


          '''simple docstring'''


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

          snake_case       :   Tuple							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

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

          snake_case       :   List[Any]							=      processor.batch_decode(snake_case__ )
          snake_case       :   Union[str, Any]							=      tokenizer.batch_decode(snake_case__ )

          self.assertListEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   str							=      self.get_image_processor()
          snake_case       :   Union[str, Any]							=      self.get_tokenizer()

          snake_case       :   Any							=      VisionTextDualEncoderProcessor(tokenizer=snake_case__    ,						image_processor=snake_case__ )

          snake_case       :   Optional[Any]							=      "lower newer"
          snake_case       :   List[Any]							=      self.prepare_image_inputs()

          snake_case       :   Tuple							=      processor(text=snake_case__    ,						images=snake_case__ )

          self.assertListEqual(list(inputs.keys() )    ,						processor.model_input_names )





