




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

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

          __UpperCamelCase  		=						[
              '''[UNK]''',
              '''[CLS]''',
              '''[SEP]''',
              '''[PAD]''',
              '''[MASK]''',
              '''want''',
              '''##want''',
              '''##ed''',
              '''wa''',
              '''un''',
              '''runn''',
              '''##ing''',
              ''',''',
              '''low''',
              '''lowest''',
          ]
          __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''': 20,
              '''do_center_crop''': True,
              '''crop_size''': 18,
              '''do_normalize''': True,
              '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
              '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
          }
          __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       snake_case	(     self					: Union[str, Any] ,       **snake_case					: str				):
          return BertTokenizer.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: List[str] ,       **snake_case					: Dict				):
          return BertTokenizerFast.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: Union[str, Any] ,       **snake_case					: Optional[int]				):
          return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,       **snake_case				)







      def       snake_case	(     self					: Tuple				):
          shutil.rmtree(self.tmpdirname				)







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

          __UpperCamelCase  		=						AlignProcessor(tokenizer=snake_case ,       image_processor=snake_case				)
          processor_slow.save_pretrained(self.tmpdirname				)
          __UpperCamelCase  		=						AlignProcessor.from_pretrained(self.tmpdirname ,       use_fast=snake_case				)

          __UpperCamelCase  		=						AlignProcessor(tokenizer=snake_case ,       image_processor=snake_case				)
          processor_fast.save_pretrained(self.tmpdirname				)
          __UpperCamelCase  		=						AlignProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor_slow.tokenizer.get_vocab() ,       tokenizer_slow.get_vocab()				)
          self.assertEqual(processor_fast.tokenizer.get_vocab() ,       tokenizer_fast.get_vocab()				)
          self.assertEqual(tokenizer_slow.get_vocab() ,       tokenizer_fast.get_vocab()				)
          self.assertIsInstance(processor_slow.tokenizer ,       snake_case				)
          self.assertIsInstance(processor_fast.tokenizer ,       snake_case				)

          self.assertEqual(processor_slow.image_processor.to_json_string() ,       image_processor.to_json_string()				)
          self.assertEqual(processor_fast.image_processor.to_json_string() ,       image_processor.to_json_string()				)
          self.assertIsInstance(processor_slow.image_processor ,       snake_case				)
          self.assertIsInstance(processor_fast.image_processor ,       snake_case				)







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

          __UpperCamelCase  		=						AlignProcessor(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_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum() ,       input_processor[key].sum() ,       delta=1E-2				)







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

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

          __UpperCamelCase  		=						'''lower newer'''

          __UpperCamelCase  		=						processor(text=snake_case				)

          __UpperCamelCase  		=						tokenizer(snake_case ,       padding='''max_length''' ,       max_length=64				)

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







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

          __UpperCamelCase  		=						AlignProcessor(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 pytest.raises(snake_case				):
              processor()







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

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

          __UpperCamelCase  		=						AlignProcessor(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				)












import warnings

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

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __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					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          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       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          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       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






