






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_			: Any				=			{
    "configuration_swin2sr": ["SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swin2SRConfig"],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: Any				=			[
          "SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST",
          "Swin2SRForImageSuperResolution",
          "Swin2SRModel",
          "Swin2SRPreTrainedModel",
      ]


try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_			: List[str]				=			["Swin2SRImageProcessor"]


if TYPE_CHECKING:
      from .configuration_swinasr import SWIN2SR_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinaSRConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_swinasr import (
                SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST,
                SwinaSRForImageSuperResolution,
                SwinaSRModel,
                SwinaSRPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_swinasr import SwinaSRImageProcessor


else:
      import sys

      A_			: Optional[Any]				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









"""simple docstring"""
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 		a_      (	unittest.TestCase ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						tempfile.mkdtemp()

       # fmt: off
       lowerCamelCase__     :			List[str]     				=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
       # fmt: on
       lowerCamelCase__     :			List[Any]     				=						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]					)					)

       lowerCamelCase__     :			List[Any]     				=						{
           'image_mean': FLAVA_IMAGE_MEAN,
           'image_std': FLAVA_IMAGE_STD,
           'do_normalize': True,
           'do_resize': True,
           'size': 2_2_4,
           'do_center_crop': True,
           'crop_size': 2_2_4,
           'input_size_patches': 1_4,
           'total_mask_patches': 7_5,
           'mask_group_max_patches': None,
           'mask_group_min_patches': 1_6,
           'mask_group_min_aspect_ratio': 0.3,
           'mask_group_max_aspect_ratio': None,
           'codebook_do_resize': True,
           'codebook_size': 1_1_2,
           'codebook_do_center_crop': True,
           'codebook_crop_size': 1_1_2,
           'codebook_do_map_pixels': True,
           'codebook_do_normalize': True,
           'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
           'codebook_image_std': FLAVA_CODEBOOK_STD,
       }

       lowerCamelCase__     :			List[str]     				=						os.path.join(self.tmpdirname,     lowerCamelCase_					)
       with open(self.image_processor_file,     'w',     encoding='utf-8'					) as fp:
        json.dump(lowerCamelCase_,     lowerCamelCase_					)




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



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




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



       return BertTokenizerFast.from_pretrained(self.tmpdirname,     **lowerCamelCase_					)




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



       return FlavaImageProcessor.from_pretrained(self.tmpdirname,     **lowerCamelCase_					)




      def 			a__							(self					):

       '''simple docstring'''



       shutil.rmtree(self.tmpdirname					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						[np.random.randint(2_5_5,     size=(3, 3_0, 4_0_0),     dtype=np.uinta					)]

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

       return image_inputs




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						self.get_tokenizer()
       lowerCamelCase__     :			Dict     				=						self.get_rust_tokenizer()
       lowerCamelCase__     :			Union[str, Any]     				=						self.get_image_processor()

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)
       processor_slow.save_pretrained(self.tmpdirname					)
       lowerCamelCase__     :			str     				=						FlavaProcessor.from_pretrained(self.tmpdirname,     use_fast=lowerCamelCase_					)

       lowerCamelCase__     :			List[str]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)
       processor_fast.save_pretrained(self.tmpdirname					)
       lowerCamelCase__     :			List[str]     				=						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,     lowerCamelCase_					)
       self.assertIsInstance(processor_fast.tokenizer,     lowerCamelCase_					)

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




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						FlavaProcessor(tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       lowerCamelCase__     :			List[str]     				=						self.get_tokenizer(bos_token='(BOS)',     eos_token='(EOS)'					)
       lowerCamelCase__     :			List[str]     				=						self.get_image_processor(do_normalize=lowerCamelCase_,     padding_value=1.0					)

       lowerCamelCase__     :			Dict     				=						FlavaProcessor.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 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Optional[Any]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			Tuple     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			List[str]     				=						image_processor(lowerCamelCase_,     return_tensors='np'					)
       lowerCamelCase__     :			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					)

       # With rest of the args
       random.seed(1_2_3_4					)
       lowerCamelCase__     :			Optional[Any]     				=						image_processor(
           lowerCamelCase_,     return_image_mask=lowerCamelCase_,     return_codebook_pixels=lowerCamelCase_,     return_tensors='np'					)
       random.seed(1_2_3_4					)
       lowerCamelCase__     :			Optional[Any]     				=						processor(
           images=lowerCamelCase_,     return_image_mask=lowerCamelCase_,     return_codebook_pixels=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 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			Optional[int]     				=						'lower newer'

       lowerCamelCase__     :			Union[str, Any]     				=						processor(text=lowerCamelCase_					)

       lowerCamelCase__     :			Union[str, Any]     				=						tokenizer(lowerCamelCase_					)

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




      def 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			List[str]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			str     				=						'lower newer'
       lowerCamelCase__     :			Union[str, Any]     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			Optional[int]     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_					)

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

       # add extra args
       lowerCamelCase__     :			Tuple     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_,     return_codebook_pixels=lowerCamelCase_,     return_image_mask=lowerCamelCase_					)

       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(lowerCamelCase_					):
        processor()




      def 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

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

       lowerCamelCase__     :			str     				=						processor.batch_decode(lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						tokenizer.batch_decode(lowerCamelCase_					)

       self.assertListEqual(lowerCamelCase_,     lowerCamelCase_					)





      def 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Union[str, Any]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			List[Any]     				=						'lower newer'
       lowerCamelCase__     :			Optional[int]     				=						self.prepare_image_inputs()

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

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

