



"""simple docstring"""

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  lowerCamelCase_   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (DDIMScheduler,)
	_SCREAMING_SNAKE_CASE  :		Tuple     	=   (("""eta""", 0.0), ("""num_inference_steps""", 50))





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    {
					    "num_train_timesteps": 1_000,
					    "beta_start": 0.0_0_0_1,
					    "beta_end": 0.0_2,
					    "beta_schedule": "linear",
					    "clip_sample": True,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE	:       str 							=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       int 							=    scheduler_class(**_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       int 							=    10, 0.0

					SCREAMING_SNAKE_CASE	:       int 							=    self.dummy_model()
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.dummy_sample_deter

					scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						)

					for t in scheduler.timesteps:
									SCREAMING_SNAKE_CASE	:       str 							=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    scheduler.step(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						).prev_sample

					return sample





	def  _lowerCAmelCase    (			self							:					str						)	->  Dict:





					"""simple docstring"""


					for timesteps in [100, 500, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  List[str]:





					"""simple docstring"""


					for steps_offset in [0, 1]:
									self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       str 							=    self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE	:       str 							=    self.get_scheduler_config(steps_offset=1						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    scheduler_class(**_SCREAMING_SNAKE_CASE						)
					scheduler.set_timesteps(5						)
					assert torch.equal(scheduler.timesteps    ,					torch.LongTensor([801, 601, 401, 201, 1]						)						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,					[0.0_0_2, 0.0_2, 0.2, 2]						):
									self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE    ,					beta_end=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  str:





					"""simple docstring"""


					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Any:





					"""simple docstring"""


					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					for timestep_spacing in ["trailing", "leading"]:
									self.check_over_configs(timestep_spacing=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					str						)	->  Tuple:





					"""simple docstring"""


					for rescale_betas_zero_snr in [True, False]:
									self.check_over_configs(rescale_betas_zero_snr=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  Any:





					"""simple docstring"""


					self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE						)
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(
													    thresholding=_SCREAMING_SNAKE_CASE    ,					prediction_type=_SCREAMING_SNAKE_CASE    ,					sample_max_value=_SCREAMING_SNAKE_CASE    ,					)





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  List[Any]:





					"""simple docstring"""


					for t in [1, 10, 49]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Union[str, Any]:





					"""simple docstring"""


					for t, num_inference_steps in zip([1, 10, 50]    ,					[10, 50, 500]						):
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE    ,					num_inference_steps=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any						)	->  List[str]:





					"""simple docstring"""


					for t, eta in zip([1, 10, 49]    ,					[0.0, 0.5, 1.0]						):
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE    ,					eta=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_scheduler_config()
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    scheduler_class(**_SCREAMING_SNAKE_CASE						)

					assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0						) - 0.0						)						) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(420    ,					400						) - 0.1_4_7_7_1						)						) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(980    ,					960						) - 0.3_2_4_6_0						)						) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(0    ,					0						) - 0.0						)						) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(487    ,					486						) - 0.0_0_9_7_9						)						) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(999    ,					998						) - 0.0_2						)						) < 1E-5





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.full_loop()

					SCREAMING_SNAKE_CASE	:       Any 							=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

					assert abs(result_sum.item() - 172.0_067						) < 1E-2
					assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1E-3





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.full_loop(prediction_type='v_prediction'						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

					assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1E-2
					assert abs(result_mean.item() - 0.0_6_8_4						) < 1E-3





	def  _lowerCAmelCase    (			self							:					Any						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,					beta_start=0.0_1						)
					SCREAMING_SNAKE_CASE	:       Any 							=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

					assert abs(result_sum.item() - 149.8_295						) < 1E-2
					assert abs(result_mean.item() - 0.1_9_5_1						) < 1E-3







	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE    ,					beta_start=0.0_1						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
					SCREAMING_SNAKE_CASE	:       int 							=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE						)						)

					assert abs(result_sum.item() - 149.0_784						) < 1E-2
					assert abs(result_mean.item() - 0.1_9_4_1						) < 1E-3






"""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 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    tempfile.mkdtemp()

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

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    {
					    '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	:       List[Any] 							=    os.path.join(self.tmpdirname    ,					_SCREAMING_SNAKE_CASE						)
					with open(self.image_processor_file    ,					'w'    ,					encoding='utf-8'						) as fp:
									json.dump(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Any    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  str:





					"""simple docstring"""


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





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





					"""simple docstring"""


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





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





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					Dict						)	->  str:





					"""simple docstring"""


					shutil.rmtree(self.tmpdirname						)





	def  _lowerCAmelCase    (			self							:					int						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    [np.random.randint(255    ,					size=(3, 30, 400)    ,					dtype=np.uinta						)]

					SCREAMING_SNAKE_CASE	:       str 							=    [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,					0    ,					-1						)						) for x in image_inputs]

					return image_inputs





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    self.get_tokenizer()
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.get_rust_tokenizer()
					SCREAMING_SNAKE_CASE	:       str 							=    self.get_image_processor()

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_slow.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor.from_pretrained(self.tmpdirname    ,					use_fast=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)
					processor_fast.save_pretrained(self.tmpdirname						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    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    ,					_SCREAMING_SNAKE_CASE						)
					self.assertIsInstance(processor_fast.tokenizer    ,					_SCREAMING_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    ,					_SCREAMING_SNAKE_CASE						)
					self.assertIsInstance(processor_fast.image_processor    ,					_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    FlavaProcessor(tokenizer=self.get_tokenizer()    ,					image_processor=self.get_image_processor()						)
					processor.save_pretrained(self.tmpdirname						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    self.get_tokenizer(bos_token='(BOS)'    ,					eos_token='(EOS)'						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    FlavaProcessor.from_pretrained(
					    self.tmpdirname    ,					bos_token='(BOS)'    ,					eos_token='(EOS)'    ,					do_normalize=_SCREAMING_SNAKE_CASE    ,					padding_value=1.0						)

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

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





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Tuple:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Any 							=    image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					SCREAMING_SNAKE_CASE	:       str 							=    processor(images=_SCREAMING_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						)

					# With rest of the args
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       str 							=    image_processor(
					    _SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_tensors='np'						)
					random.seed(1_234						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    processor(
					    images=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_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  _lowerCAmelCase    (			self							:					Any						)	->  Optional[int]:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    'lower newer'

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    tokenizer(_SCREAMING_SNAKE_CASE						)

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





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  int:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       str 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

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

					# add extra args
					SCREAMING_SNAKE_CASE	:       Any 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE    ,					return_codebook_pixels=_SCREAMING_SNAKE_CASE    ,					return_image_mask=_SCREAMING_SNAKE_CASE						)

					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(_SCREAMING_SNAKE_CASE						):
									processor()





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.get_image_processor()
					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.get_tokenizer()

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    processor.batch_decode(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

					self.assertListEqual(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					int						)	->  Any:





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       int 							=    FlavaProcessor(tokenizer=_SCREAMING_SNAKE_CASE    ,					image_processor=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Any 							=    'lower newer'
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.prepare_image_inputs()

					SCREAMING_SNAKE_CASE	:       Dict 							=    processor(text=_SCREAMING_SNAKE_CASE    ,					images=_SCREAMING_SNAKE_CASE						)

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





