




'''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       ):

							def       UpperCAmelCase__		(							self							:       Tuple				)   ->    Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					tempfile.mkdtemp()

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

													UpperCAmelCase_     : str         =					{
													    '''image_mean''': FLAVA_IMAGE_MEAN,
													    '''image_std''': FLAVA_IMAGE_STD,
													    '''do_normalize''': True,
													    '''do_resize''': True,
													    '''size''': 2_24,
													    '''do_center_crop''': True,
													    '''crop_size''': 2_24,
													    '''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''': 1_12,
													    '''codebook_do_center_crop''': True,
													    '''codebook_crop_size''': 1_12,
													    '''codebook_do_map_pixels''': True,
													    '''codebook_do_normalize''': True,
													    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
													    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
													}

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

							def       UpperCAmelCase__		(							self							:       str    ,       **__magic_name__							:       Any				)   ->    List[str]:







													"""simple docstring"""
													return BertTokenizer.from_pretrained(self.tmpdirname    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       str    ,       **__magic_name__							:       Union[str, Any]				)   ->    List[Any]:







													"""simple docstring"""
													return BertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       **__magic_name__							:       Dict				)   ->    Union[str, Any]:







													"""simple docstring"""
													return FlavaImageProcessor.from_pretrained(self.tmpdirname    ,       **__magic_name__				)

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







													"""simple docstring"""
													shutil.rmtree(self.tmpdirname				)

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







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					[np.random.randint(2_55    ,       size=(3, 30, 4_00)    ,       dtype=np.uinta				)]

													UpperCAmelCase_     : Optional[int]         =					[Image.fromarray(np.moveaxis(__magic_name__    ,       0    ,       -1				)				) for x in image_inputs]

													return image_inputs

							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Tuple         =					self.get_tokenizer()
													UpperCAmelCase_     : int         =					self.get_rust_tokenizer()
													UpperCAmelCase_     : Optional[int]         =					self.get_image_processor()

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

													UpperCAmelCase_     : List[Any]         =					FlavaProcessor(tokenizer=__magic_name__    ,       image_processor=__magic_name__				)
													processor_fast.save_pretrained(self.tmpdirname				)
													UpperCAmelCase_     : Optional[int]         =					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    ,       __magic_name__				)
													self.assertIsInstance(processor_fast.tokenizer    ,       __magic_name__				)

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

							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Optional[Any]         =					FlavaProcessor(tokenizer=self.get_tokenizer()    ,       image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													UpperCAmelCase_     : Optional[int]         =					self.get_tokenizer(bos_token='''(BOS)'''    ,       eos_token='''(EOS)'''				)
													UpperCAmelCase_     : str         =					self.get_image_processor(do_normalize=__magic_name__    ,       padding_value=1.0				)

													UpperCAmelCase_     : Union[str, Any]         =					FlavaProcessor.from_pretrained(
													    self.tmpdirname    ,       bos_token='''(BOS)'''    ,       eos_token='''(EOS)'''    ,       do_normalize=__magic_name__    ,       padding_value=1.0				)

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

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

							def       UpperCAmelCase__		(							self							:       Tuple				)   ->    Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_     : Any         =					self.get_image_processor()
													UpperCAmelCase_     : List[str]         =					self.get_tokenizer()

													UpperCAmelCase_     : Dict         =					FlavaProcessor(tokenizer=__magic_name__    ,       image_processor=__magic_name__				)

													UpperCAmelCase_     : Any         =					self.prepare_image_inputs()

													UpperCAmelCase_     : Tuple         =					image_processor(__magic_name__    ,       return_tensors='''np'''				)
													UpperCAmelCase_     : Any         =					processor(images=__magic_name__    ,       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(12_34				)
													UpperCAmelCase_     : List[Any]         =					image_processor(
													    __magic_name__    ,       return_image_mask=__magic_name__    ,       return_codebook_pixels=__magic_name__    ,       return_tensors='''np'''				)
													random.seed(12_34				)
													UpperCAmelCase_     : Optional[Any]         =					processor(
													    images=__magic_name__    ,       return_image_mask=__magic_name__    ,       return_codebook_pixels=__magic_name__    ,       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       UpperCAmelCase__		(							self							:       str				)   ->    Any:







													"""simple docstring"""
													UpperCAmelCase_     : Any         =					self.get_image_processor()
													UpperCAmelCase_     : Optional[int]         =					self.get_tokenizer()

													UpperCAmelCase_     : Dict         =					FlavaProcessor(tokenizer=__magic_name__    ,       image_processor=__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					'''lower newer'''

													UpperCAmelCase_     : Dict         =					processor(text=__magic_name__				)

													UpperCAmelCase_     : Any         =					tokenizer(__magic_name__				)

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

							def       UpperCAmelCase__		(							self							:       Any				)   ->    Any:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.get_image_processor()
													UpperCAmelCase_     : str         =					self.get_tokenizer()

													UpperCAmelCase_     : Optional[Any]         =					FlavaProcessor(tokenizer=__magic_name__    ,       image_processor=__magic_name__				)

													UpperCAmelCase_     : Tuple         =					'''lower newer'''
													UpperCAmelCase_     : Optional[int]         =					self.prepare_image_inputs()

													UpperCAmelCase_     : Dict         =					processor(text=__magic_name__    ,       images=__magic_name__				)

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

													# add extra args
													UpperCAmelCase_     : List[Any]         =					processor(text=__magic_name__    ,       images=__magic_name__    ,       return_codebook_pixels=__magic_name__    ,       return_image_mask=__magic_name__				)

													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(__magic_name__				):
																			processor()

							def       UpperCAmelCase__		(							self							:       List[str]				)   ->    Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					self.get_image_processor()
													UpperCAmelCase_     : Optional[Any]         =					self.get_tokenizer()

													UpperCAmelCase_     : Any         =					FlavaProcessor(tokenizer=__magic_name__    ,       image_processor=__magic_name__				)

													UpperCAmelCase_     : Dict         =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													UpperCAmelCase_     : List[Any]         =					processor.batch_decode(__magic_name__				)
													UpperCAmelCase_     : Dict         =					tokenizer.batch_decode(__magic_name__				)

													self.assertListEqual(__magic_name__    ,       __magic_name__				)



							def       UpperCAmelCase__		(							self							:       List[Any]				)   ->    List[Any]:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.get_image_processor()
													UpperCAmelCase_     : Optional[Any]         =					self.get_tokenizer()

													UpperCAmelCase_     : int         =					FlavaProcessor(tokenizer=__magic_name__    ,       image_processor=__magic_name__				)

													UpperCAmelCase_     : str         =					'''lower newer'''
													UpperCAmelCase_     : int         =					self.prepare_image_inputs()

													UpperCAmelCase_     : List[Any]         =					processor(text=__magic_name__    ,       images=__magic_name__				)

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












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





