



"""simple docstring"""

import warnings

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import pairwise_distances

warnings.filterwarnings('ignore')

A_	:						List[Any]				=			'K-MEANS-CLUST/ '
def    __snake_case       (  __A			:	Tuple       ,		__A			:	Any       ,		__A			:	int=None    )    ->					str:






				'''simple docstring'''







				if seed is not None:  # useful for obtaining consistent results
								np.random.seed(__A    )
				SCREAMING_SNAKE_CASE	:       Any 							=    data.shape[0]  # number of data points

				# Pick K indices from range [0, N).
				SCREAMING_SNAKE_CASE	:       int 							=    np.random.randint(0       ,		__A       ,		__A    )

				# Keep centroids as dense format, as many entries will be nonzero due to averaging.
				# As long as at least one document in a cluster contains a word,
				# it will carry a nonzero weight in the TF-IDF vector of the centroid.
				SCREAMING_SNAKE_CASE	:       Dict 							=    data[rand_indices, :]

				return centroids
def    __snake_case       (  __A			:	Optional[int]       ,		__A			:	Dict    )    ->					str:






				'''simple docstring'''
				return pairwise_distances(__A       ,		__A       ,		metric='euclidean'    )
def    __snake_case       (  __A			:	Optional[int]       ,		__A			:	Tuple    )    ->					Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Dict 							=    centroid_pairwise_dist(__A       ,		__A    )

				# Compute cluster assignments for each data point:
				# Fill in the blank (RHS only)
				SCREAMING_SNAKE_CASE	:       int 							=    np.argmin(__A       ,		axis=1    )

				return cluster_assignment
def    __snake_case       (  __A			:	List[Any]       ,		__A			:	int       ,		__A			:	Union[str, Any]    )    ->					Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       int 							=    []
				for i in range(__A    ):
								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
								SCREAMING_SNAKE_CASE	:       List[Any] 							=    data[cluster_assignment == i]
								# Compute the mean of the data points. Fill in the blank (RHS only)
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    member_data_points.mean(axis=0    )
								new_centroids.append(__A    )
				SCREAMING_SNAKE_CASE	:       Any 							=    np.array(__A    )

				return new_centroids
def    __snake_case       (  __A			:	List[str]       ,		__A			:	List[Any]       ,		__A			:	Union[str, Any]       ,		__A			:	str    )    ->					Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    0.0
				for i in range(__A    ):
								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
								SCREAMING_SNAKE_CASE	:       List[str] 							=    data[cluster_assignment == i, :]

								if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
												# Compute distances from centroid to data points (RHS only)
												SCREAMING_SNAKE_CASE	:       Any 							=    pairwise_distances(
												    __A       ,		[centroids[i]]       ,		metric='euclidean'    )
												SCREAMING_SNAKE_CASE	:       Any 							=    distances**2
												heterogeneity += np.sum(__A    )

				return heterogeneity
def    __snake_case       (  __A			:	Optional[int]       ,		__A			:	Tuple    )    ->					str:






				'''simple docstring'''
				plt.figure(figsize=(7, 4)    )
				plt.plot(__A       ,		linewidth=4    )
				plt.xlabel('# Iterations'    )
				plt.ylabel('Heterogeneity'    )
				plt.title(F"""Heterogeneity of clustering over time, K={k:d}"""    )
				plt.rcParams.update({'font.size': 16}    )
				plt.show()
def    __snake_case       (  __A			:	Tuple       ,		__A			:	Dict       ,		__A			:	Any       ,		__A			:	Dict=500       ,		__A			:	Union[str, Any]=None       ,		__A			:	str=False    )    ->					List[str]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Any 							=    initial_centroids[:]
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    None

				for itr in range(__A    ):
								if verbose:
												print(__A       ,		end=''    )

								# 1. Make cluster assignments using nearest centroids
								SCREAMING_SNAKE_CASE	:       Tuple 							=    assign_clusters(__A       ,		__A    )

								# 2. Compute a new centroid for each of the k clusters, averaging all data
								#    points assigned to that cluster.
								SCREAMING_SNAKE_CASE	:       int 							=    revise_centroids(__A       ,		__A       ,		__A    )

								# Check for convergence: if none of the assignments changed, stop
								if (
								    prev_cluster_assignment is not None
								    and (prev_cluster_assignment == cluster_assignment).all()
								):
												break

								# Print number of new assignments
								if prev_cluster_assignment is not None:
												SCREAMING_SNAKE_CASE	:       Dict 							=    np.sum(prev_cluster_assignment != cluster_assignment    )
												if verbose:
																print(
																    F"""    {num_changed:5d} elements changed their cluster assignment."""    )

        # Record heterogeneity convergence metric
								if record_heterogeneity is not None:
												# YOUR CODE HERE
												SCREAMING_SNAKE_CASE	:       Any 							=    compute_heterogeneity(__A       ,		__A       ,		__A       ,		__A    )
												record_heterogeneity.append(__A    )

								SCREAMING_SNAKE_CASE	:       Tuple 							=    cluster_assignment[:]

				return centroids, cluster_assignment


# Mock test below
if False:  # change to true to run this test case.
							from sklearn import datasets as ds

							A_	:						int				=			ds.load_iris()
							A_	:						str				=			3
							A_	:						List[str]				=			[]
							A_	:						Dict				=			get_initial_centroids(dataset['data'], k, seed=0)
							A_		,     A_	:						Optional[Any]				=			kmeans(
							    dataset['data'],
							    k,
							    initial_centroids,
							    maxiter=400,
							    record_heterogeneity=heterogeneity,
							    verbose=True,
							)
							plot_heterogeneity(heterogeneity, k)
def    __snake_case       (  __A			:	str       ,		__A			:	str       ,		__A			:	int=None    )    ->					List[str]:






				'''simple docstring'''







				if fill_missing_report:
								SCREAMING_SNAKE_CASE	:       Any 							=    df.fillna(value=__A    )
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    1
				SCREAMING_SNAKE_CASE	:       str 							=    df.select_dtypes(np.number    ).columns
				SCREAMING_SNAKE_CASE	:       Any 							=    (
				    df.groupby(['Cluster']    )[  # construct report dataframe
				        numeric_cols
				    ]  # group by cluster number
				    .agg(
				        [
				            ('sum', np.sum),
				            ('mean_with_zeros', lambda __A    : np.mean(np.nan_to_num(__A    )    )),
				            ('mean_without_zeros', lambda __A    : x.replace(0       ,		np.NaN    ).mean()),
				            (
				                'mean_25-75',
				                lambda __A    : np.mean(
				                    np.nan_to_num(
				                        sorted(__A    )[
				                            round(len(__A    ) * 25 / 100    ) : round(len(__A    ) * 75 / 100    )
				                        ]    )    ),
				            ),
				            ('mean_with_na', np.mean),
				            ('min', lambda __A    : x.min()),
				            ('5%', lambda __A    : x.quantile(0.0_5    )),
				            ('25%', lambda __A    : x.quantile(0.2_5    )),
				            ('50%', lambda __A    : x.quantile(0.5_0    )),
				            ('75%', lambda __A    : x.quantile(0.7_5    )),
				            ('95%', lambda __A    : x.quantile(0.9_5    )),
				            ('max', lambda __A    : x.max()),
				            ('count', lambda __A    : x.count()),
				            ('stdev', lambda __A    : x.std()),
				            ('mode', lambda __A    : x.mode()[0]),
				            ('median', lambda __A    : x.median()),
				            ('# > 0', lambda __A    : (x > 0).sum()),
				        ]    )
				    .T.reset_index()
				    .rename(index=__A       ,		columns={'level_0': 'Features', 'level_1': 'Type'}    )
				)  # rename columns
				# calculate the size of cluster(count of clientID's)
				SCREAMING_SNAKE_CASE	:       Any 							=    report[
				    (report['Features'] == 'dummy') & (report['Type'] == 'count')
				].copy()  # avoid SettingWithCopyWarning
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    (
				    'ClusterSize'  # rename created cluster df to match report column names
				)
				SCREAMING_SNAKE_CASE	:       str 							=    '# of Customers'
				SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    pd.DataFrame(
				    clustersize.iloc[:, 2:].values
				    / clustersize.iloc[:, 2:].values.sum()    )
				SCREAMING_SNAKE_CASE	:       List[str] 							=    '% of Customers'  # rename created cluster df to match report column names
				SCREAMING_SNAKE_CASE	:       List[str] 							=    'ClusterProportion'
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    clusterproportion.columns.tolist()
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    cols[-2:] + cols[:-2]
				SCREAMING_SNAKE_CASE	:       Dict 							=    clusterproportion[cols]  # rearrange columns to match report
				SCREAMING_SNAKE_CASE	:       int 							=    report.columns
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    pd.DataFrame(
				    abs(
				        report[report['Type'] == 'count'].iloc[:, 2:].values
				        - clustersize.iloc[:, 2:].values    )    )  # generating df with count of nan values
				SCREAMING_SNAKE_CASE	:       Tuple 							=    0
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    '# of nan'
				SCREAMING_SNAKE_CASE	:       Any 							=    report[
				    report['Type'] == 'count'
				].Features.tolist()  # filling values in order to match report
				SCREAMING_SNAKE_CASE	:       Dict 							=    a.columns.tolist()
				SCREAMING_SNAKE_CASE	:       str 							=    cols[-2:] + cols[:-2]
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    a[cols]  # rearrange columns to match report
				SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    report.columns  # rename columns to match report
				SCREAMING_SNAKE_CASE	:       List[str] 							=    report.drop(
				    report[report.Type == 'count'].index    )  # drop count values except cluster size
				SCREAMING_SNAKE_CASE	:       Any 							=    pd.concat(
				    [report, a, clustersize, clusterproportion]       ,		axis=0    )  # concat report with clustert size and nan values
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    report['Features'].isin(__A    )
				SCREAMING_SNAKE_CASE	:       Dict 							=    report.columns.tolist()
				SCREAMING_SNAKE_CASE	:       Any 							=    cols[0:2] + cols[-1:] + cols[2:-1]
				SCREAMING_SNAKE_CASE	:       Dict 							=    report[cols]
				SCREAMING_SNAKE_CASE	:       int 							=    {
				    'ClusterSize': 9,
				    'ClusterProportion': 8,
				    'mean_with_zeros': 7,
				    'mean_with_na': 6,
				    'max': 5,
				    '50%': 4,
				    'min': 3,
				    '25%': 2,
				    '75%': 1,
				    '# of nan': 0,
				    '# > 0': -1,
				    'sum_with_na': -2,
				}
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    (
				    report.assign(
				        Sortera=lambda __A    : x.Type.map(__A    )       ,		Sortera=lambda __A    : list(reversed(range(len(__A    )    )    )    )       ,		)
				    .sort_values(['Sorter1', 'Mark', 'Sorter2']       ,		ascending=__A    )
				    .drop(['Sorter1', 'Sorter2']       ,		axis=1    )
				)
				SCREAMING_SNAKE_CASE	:       List[str] 							=    ''
				SCREAMING_SNAKE_CASE	:       List[str] 							=    report.reset_index()
				SCREAMING_SNAKE_CASE	:       Tuple 							=    report.drop(columns=['index']    )
				return report


if __name__ == "__main__":
							import doctest

							doctest.testmod()






"""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						)





