

import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )



@require_vision
class  __snake_case					(      unittest.TestCase    ):

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  tempfile.mkdtemp()

      # fmt: off
      SCREAMING_SNAKE_CASE_													=  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      SCREAMING_SNAKE_CASE_													=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES['vocab_file'])

      with open(self.vocab_file  ,						'w'  ,						encoding='utf-8') as fp:
          fp.write(''.join([x + '\n' for x in vocab_tokens]))

      SCREAMING_SNAKE_CASE_													=  {
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 224,
          'do_center_crop': True,
          'crop_size': 224,
          'input_size_patches': 14,
          'total_mask_patches': 75,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 16,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 112,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 112,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      SCREAMING_SNAKE_CASE_													=  os.path.join(self.tmpdirname  ,						_A)
      with open(self.image_processor_file  ,						'w'  ,						encoding='utf-8') as fp:
          json.dump(_A  ,						_A)

  def   lowerCAmelCase__       (   self  ,						**_A):
      return BertTokenizer.from_pretrained(self.tmpdirname  ,						**_A)

  def   lowerCAmelCase__       (   self  ,						**_A):
      return BertTokenizerFast.from_pretrained(self.tmpdirname  ,						**_A)

  def   lowerCAmelCase__       (   self  ,						**_A):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname  ,						**_A)

  def   lowerCAmelCase__       (   self):
      shutil.rmtree(self.tmpdirname)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta)]

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

      return image_inputs

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()
      SCREAMING_SNAKE_CASE_													=  self.get_rust_tokenizer()
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor()

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)
      processor_slow.save_pretrained(self.tmpdirname)
      SCREAMING_SNAKE_CASE_													=  FlavaProcessor.from_pretrained(self.tmpdirname  ,						use_fast=_A)

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)
      processor_fast.save_pretrained(self.tmpdirname)
      SCREAMING_SNAKE_CASE_													=  FlavaProcessor.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  ,						_A)
      self.assertIsInstance(processor_fast.tokenizer  ,						_A)

      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  ,						_A)
      self.assertIsInstance(processor_fast.image_processor  ,						_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor())
      processor.save_pretrained(self.tmpdirname)

      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer(bos_token='(BOS)'  ,						eos_token='(EOS)')
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor(do_normalize=_A  ,						padding_value=1.0)

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor.from_pretrained(
          self.tmpdirname  ,						bos_token='(BOS)'  ,						eos_token='(EOS)'  ,						do_normalize=_A  ,						padding_value=1.0)

      self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer_add_kwargs.get_vocab())
      self.assertIsInstance(processor.tokenizer  ,						_A)

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)

      SCREAMING_SNAKE_CASE_													=  self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_													=  image_processor(_A  ,						return_tensors='np')
      SCREAMING_SNAKE_CASE_													=  processor(images=_A  ,						return_tensors='np')

      for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2)

      # With rest of the args
      random.seed(1234)
      SCREAMING_SNAKE_CASE_													=  image_processor(
          _A  ,						return_image_mask=_A  ,						return_codebook_pixels=_A  ,						return_tensors='np')
      random.seed(1234)
      SCREAMING_SNAKE_CASE_													=  processor(
          images=_A  ,						return_image_mask=_A  ,						return_codebook_pixels=_A  ,						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):
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)

      SCREAMING_SNAKE_CASE_													=  'lower newer'

      SCREAMING_SNAKE_CASE_													=  processor(text=_A)

      SCREAMING_SNAKE_CASE_													=  tokenizer(_A)

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)

      SCREAMING_SNAKE_CASE_													=  'lower newer'
      SCREAMING_SNAKE_CASE_													=  self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_													=  processor(text=_A  ,						images=_A)

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

      # add extra args
      SCREAMING_SNAKE_CASE_													=  processor(text=_A  ,						images=_A  ,						return_codebook_pixels=_A  ,						return_image_mask=_A)

      self.assertListEqual(
          list(inputs.keys())  ,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]  ,						)

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

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)

      SCREAMING_SNAKE_CASE_													=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      SCREAMING_SNAKE_CASE_													=  processor.batch_decode(_A)
      SCREAMING_SNAKE_CASE_													=  tokenizer.batch_decode(_A)

      self.assertListEqual(_A  ,						_A)


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.get_image_processor()
      SCREAMING_SNAKE_CASE_													=  self.get_tokenizer()

      SCREAMING_SNAKE_CASE_													=  FlavaProcessor(tokenizer=_A  ,						image_processor=_A)

      SCREAMING_SNAKE_CASE_													=  'lower newer'
      SCREAMING_SNAKE_CASE_													=  self.prepare_image_inputs()

      SCREAMING_SNAKE_CASE_													=  processor(text=_A  ,						images=_A)

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





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						float	,				_SCREAMING_SNAKE_CASE   :						float    ):





    """simple docstring"""


    return a + b


if __name__ == "__main__":
       UpperCamelCase__					: Union[str, Any]								=      5
       UpperCamelCase__					: List[Any]								=      6
       print(F'The sum of {a} + {b} is {add(a, b)}')


