

'''simple docstring'''
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch


_lowerCamelCase	 =			random.Random()


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    int=1.0     ,							_SCREAMING_SNAKE_CASE					:    int=None     ,							_SCREAMING_SNAKE_CASE					:    str=None							)			-> Any:


	"""simple docstring"""





	if rng is None:
		UpperCAmelCase_			:	int      =   global_rng

	UpperCAmelCase_			:	Union[str, Any]      =   []
	for batch_idx in range(shape[0]							):
		values.append([]							)
		for _ in range(shape[1]							):
			values[-1].append(rng.random() * scale							)

	return values
@require_torch
class  _snake_case						(unittest.TestCase):






			def __init__(   self       ,_snake_case       ,_snake_case=7       ,_snake_case=4_00       ,_snake_case=20_00       ,_snake_case=1       ,_snake_case=0.0       ,_snake_case=2_40_00       ,_snake_case=True       ,):
				UpperCAmelCase_			:	Optional[Any]      =   parent
				UpperCAmelCase_			:	Optional[Any]      =   batch_size
				UpperCAmelCase_			:	Tuple      =   min_seq_length
				UpperCAmelCase_			:	int      =   max_seq_length
				UpperCAmelCase_			:	Dict      =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				UpperCAmelCase_			:	Dict      =   feature_size
				UpperCAmelCase_			:	Optional[int]      =   padding_value
				UpperCAmelCase_			:	Any      =   sampling_rate
				UpperCAmelCase_			:	Union[str, Any]      =   return_attention_mask






			def 						UpperCamelCase__				(   self			):
				return {
				    "feature_size": self.feature_size,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				}






			def 						UpperCamelCase__				(   self       ,_snake_case=False       ,_snake_case=False			):



				def _flatten(_snake_case			):
					return list(itertools.chain(*lowerCamelCase_			)			)

				if equal_length:
					UpperCAmelCase_			:	str      =   floats_list((self.batch_size, self.max_seq_length)			)
				else:
					# make sure that inputs increase in size
					UpperCAmelCase_			:	Tuple      =   [
					    _flatten(floats_list((x, self.feature_size)			)			)
					    for x in range(self.min_seq_length       ,self.max_seq_length       ,self.seq_length_diff			)
					]

				if numpify:
					UpperCAmelCase_			:	int      =   [np.asarray(lowerCamelCase_			) for x in audio_inputs]

				return audio_inputs




@require_torch
class  _snake_case						(snake_case_       ,       unittest.TestCase):
			__A   :     Dict          =EncodecFeatureExtractor






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Optional[int]      =   EnCodecFeatureExtractionTester(self			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	str      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
				# create three inputs of length 800, 1000, and 1200
				UpperCAmelCase_			:	List[str]      =   [floats_list((1, x)			)[0] for x in range(8_00       ,14_00       ,2_00			)]
				UpperCAmelCase_			:	Optional[int]      =   [np.asarray(lowerCamelCase_			) for audio_input in audio_inputs]

				# Test not batched input
				UpperCAmelCase_			:	int      =   feat_extract(audio_inputs[0]       ,return_tensors="np"			).input_values
				UpperCAmelCase_			:	Dict      =   feat_extract(np_audio_inputs[0]       ,return_tensors="np"			).input_values
				self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1E-3			)			)

				# Test batched
				UpperCAmelCase_			:	Optional[int]      =   feat_extract(lowerCamelCase_       ,padding=lowerCamelCase_       ,return_tensors="np"			).input_values
				UpperCAmelCase_			:	Optional[int]      =   feat_extract(lowerCamelCase_       ,padding=lowerCamelCase_       ,return_tensors="np"			).input_values
				for enc_seq_a, enc_seq_a in zip(lowerCamelCase_       ,lowerCamelCase_			):
					self.assertTrue(np.allclose(lowerCamelCase_       ,lowerCamelCase_       ,atol=1E-3			)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Dict      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
				UpperCAmelCase_			:	List[str]      =   np.random.rand(1_00			).astype(np.floataa			)
				UpperCAmelCase_			:	Union[str, Any]      =   np_audio_inputs.tolist()

				for inputs in [py_audio_inputs, np_audio_inputs]:
					UpperCAmelCase_			:	Optional[Any]      =   feature_extractor.pad([{"input_values": inputs}]       ,return_tensors="np"			)
					self.assertTrue(np_processed.input_values.dtype == np.floataa			)
					UpperCAmelCase_			:	Tuple      =   feature_extractor.pad([{"input_values": inputs}]       ,return_tensors="pt"			)
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa			)






			def 						UpperCamelCase__				(   self       ,_snake_case			):
				from datasets import load_dataset

				UpperCAmelCase_			:	Any      =   load_dataset("hf-internal-testing/librispeech_asr_dummy"       ,"clean"       ,split="validation"			)
				# automatic decoding with librispeech
				UpperCAmelCase_			:	Union[str, Any]      =   ds.sort("id"			).select(range(lowerCamelCase_			)			)[:num_samples]['audio']

				return [x["array"] for x in audio_samples]






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Dict      =   torch.tensor(
				    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
				     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
				     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
				     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
				     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
				     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]			)
				# fmt: on
				UpperCAmelCase_			:	Dict      =   self._load_datasamples(1			)
				UpperCAmelCase_			:	Optional[Any]      =   EncodecFeatureExtractor()
				UpperCAmelCase_			:	List[Any]      =   feature_extractor(lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(input_values.shape       ,(1, 1, 9_36_80)			)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,lowerCamelCase_       ,atol=1E-6			)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Tuple      =   torch.tensor(
				    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
				     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
				     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
				     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
				     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
				     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]			)
				# fmt: on
				UpperCAmelCase_			:	Union[str, Any]      =   self._load_datasamples(1			)
				UpperCAmelCase_			:	Optional[int]      =   [np.tile(input_audio[0][None]       ,reps=(2, 1)			)]
				input_audio[0][1] *= 0.5
				UpperCAmelCase_			:	Optional[int]      =   EncodecFeatureExtractor(feature_size=2			)
				UpperCAmelCase_			:	str      =   feature_extractor(lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(input_values.shape       ,(1, 2, 9_36_80)			)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,lowerCamelCase_       ,atol=1E-6			)			)
				self.assertTrue(torch.allclose(input_values[0, 1, :30]       ,EXPECTED_INPUT_VALUES * 0.5       ,atol=1E-6			)			)






			def 						UpperCamelCase__				(   self			):
				UpperCAmelCase_			:	Dict      =   self._load_datasamples(2			)
				# would be easier if the stride was like
				UpperCAmelCase_			:	Union[str, Any]      =   EncodecFeatureExtractor(feature_size=1       ,chunk_length_s=1       ,overlap=0.01			)

				# pad and trunc raise an error ?
				with self.assertRaisesRegex(
				    lowerCamelCase_       ,"^Both padding and truncation were set. Make sure you only set one.$"       ,):
					UpperCAmelCase_			:	Dict      =   feature_extractor(
					    lowerCamelCase_       ,padding="max_length"       ,truncation=lowerCamelCase_       ,return_tensors="pt"			).input_values

				# truncate to chunk
				UpperCAmelCase_			:	str      =   feature_extractor(lowerCamelCase_       ,truncation=lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(2, 1, 7_15_20)			)  # 2 chunks

				# force truncate to max_length
				UpperCAmelCase_			:	Any      =   feature_extractor(
				    lowerCamelCase_       ,truncation=lowerCamelCase_       ,max_length=4_80_00       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(2, 1, 4_80_00)			)

				# pad to chunk
				UpperCAmelCase_			:	Optional[Any]      =   feature_extractor(lowerCamelCase_       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(padded_outputs.shape       ,(2, 1, 9_52_80)			)

				# pad to chunk
				UpperCAmelCase_			:	List[str]      =   feature_extractor(lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(2, 1, 9_52_80)			)

				# force pad to max length
				UpperCAmelCase_			:	str      =   feature_extractor(
				    lowerCamelCase_       ,padding="max_length"       ,max_length=10_00_00       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(2, 1, 10_00_00)			)

				# force no pad
				with self.assertRaisesRegex(
				    lowerCamelCase_       ,"^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"       ,):
					UpperCAmelCase_			:	Any      =   feature_extractor(lowerCamelCase_       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values

				UpperCAmelCase_			:	List[str]      =   feature_extractor(input_audio[0]       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(1, 1, 9_36_80)			)

				# no pad if no chunk_length_s
				UpperCAmelCase_			:	Optional[int]      =   None
				with self.assertRaisesRegex(
				    lowerCamelCase_       ,"^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"       ,):
					UpperCAmelCase_			:	List[Any]      =   feature_extractor(lowerCamelCase_       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values

				UpperCAmelCase_			:	Any      =   feature_extractor(input_audio[0]       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(1, 1, 9_36_80)			)

				# no pad if no overlap
				UpperCAmelCase_			:	Any      =   2
				UpperCAmelCase_			:	Optional[int]      =   None
				with self.assertRaisesRegex(
				    lowerCamelCase_       ,"^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$"       ,):
					UpperCAmelCase_			:	Optional[int]      =   feature_extractor(lowerCamelCase_       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values

				UpperCAmelCase_			:	List[str]      =   feature_extractor(input_audio[0]       ,padding=lowerCamelCase_       ,return_tensors="pt"			).input_values
				self.assertEquals(truncated_outputs.shape       ,(1, 1, 9_36_80)			)












"""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					)

