



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 		UpperCAmelCase__      (      unittest.TestCase       ):




							'''simple docstring'''






							def 			snake_case__					(       self							:			Optional[Any]			):
									'''simple docstring'''





									__UpperCAmelCase   :					str													=     tempfile.mkdtemp()

									# fmt: off
									__UpperCAmelCase   :					int													=     ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
									# fmt: on
									__UpperCAmelCase   :					Optional[int]													=     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   :					Union[str, Any]													=     os.path.join(self.tmpdirname	,	__lowercase			)
									with open(self.image_processor_file	,	'''w'''	,	encoding='''utf-8'''			) as fp:
											json.dump(__lowercase	,	__lowercase			)




							def 			snake_case__					(       self							:			Optional[Any]	,	**a_							:			int			):
									'''simple docstring'''





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




							def 			snake_case__					(       self							:			Any	,	**a_							:			List[str]			):
									'''simple docstring'''





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




							def 			snake_case__					(       self							:			List[Any]	,	**a_							:			int			):
									'''simple docstring'''





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




							def 			snake_case__					(       self							:			List[str]			):
									'''simple docstring'''





									shutil.rmtree(self.tmpdirname			)




							def 			snake_case__					(       self							:			Tuple			):
									'''simple docstring'''





									__UpperCAmelCase   :					Any													=     [np.random.randint(2_55	,	size=(3, 30, 4_00)	,	dtype=np.uinta			)]

									__UpperCAmelCase   :					Optional[Any]													=     [Image.fromarray(np.moveaxis(__lowercase	,	0	,	-1			)			) for x in image_inputs]

									return image_inputs




							def 			snake_case__					(       self							:			Union[str, Any]			):
									'''simple docstring'''





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

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

									__UpperCAmelCase   :					List[Any]													=     FlavaProcessor(tokenizer=__lowercase	,	image_processor=__lowercase			)
									processor_fast.save_pretrained(self.tmpdirname			)
									__UpperCAmelCase   :					Dict													=     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	,	__lowercase			)
									self.assertIsInstance(processor_fast.tokenizer	,	__lowercase			)

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




							def 			snake_case__					(       self							:			Optional[int]			):
									'''simple docstring'''





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

									__UpperCAmelCase   :					Dict													=     self.get_tokenizer(bos_token='''(BOS)'''	,	eos_token='''(EOS)'''			)
									__UpperCAmelCase   :					int													=     self.get_image_processor(do_normalize=__lowercase	,	padding_value=1.0			)

									__UpperCAmelCase   :					Any													=     FlavaProcessor.from_pretrained(
									    self.tmpdirname	,	bos_token='''(BOS)'''	,	eos_token='''(EOS)'''	,	do_normalize=__lowercase	,	padding_value=1.0			)

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

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




							def 			snake_case__					(       self							:			Any			):
									'''simple docstring'''





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

									__UpperCAmelCase   :					Union[str, Any]													=     FlavaProcessor(tokenizer=__lowercase	,	image_processor=__lowercase			)

									__UpperCAmelCase   :					int													=     self.prepare_image_inputs()

									__UpperCAmelCase   :					Optional[Any]													=     image_processor(__lowercase	,	return_tensors='''np'''			)
									__UpperCAmelCase   :					Tuple													=     processor(images=__lowercase	,	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(
									    __lowercase	,	return_image_mask=__lowercase	,	return_codebook_pixels=__lowercase	,	return_tensors='''np'''			)
									random.seed(12_34			)
									__UpperCAmelCase   :					Optional[int]													=     processor(
									    images=__lowercase	,	return_image_mask=__lowercase	,	return_codebook_pixels=__lowercase	,	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 			snake_case__					(       self							:			str			):
									'''simple docstring'''





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

									__UpperCAmelCase   :					List[str]													=     FlavaProcessor(tokenizer=__lowercase	,	image_processor=__lowercase			)

									__UpperCAmelCase   :					Tuple													=     '''lower newer'''

									__UpperCAmelCase   :					Union[str, Any]													=     processor(text=__lowercase			)

									__UpperCAmelCase   :					str													=     tokenizer(__lowercase			)

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




							def 			snake_case__					(       self							:			Any			):
									'''simple docstring'''





									__UpperCAmelCase   :					Dict													=     self.get_image_processor()
									__UpperCAmelCase   :					List[Any]													=     self.get_tokenizer()

									__UpperCAmelCase   :					Union[str, Any]													=     FlavaProcessor(tokenizer=__lowercase	,	image_processor=__lowercase			)

									__UpperCAmelCase   :					str													=     '''lower newer'''
									__UpperCAmelCase   :					List[str]													=     self.prepare_image_inputs()

									__UpperCAmelCase   :					int													=     processor(text=__lowercase	,	images=__lowercase			)

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

									# add extra args
									__UpperCAmelCase   :					Dict													=     processor(text=__lowercase	,	images=__lowercase	,	return_codebook_pixels=__lowercase	,	return_image_mask=__lowercase			)

									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(__lowercase			):
											processor()




							def 			snake_case__					(       self							:			int			):
									'''simple docstring'''





									__UpperCAmelCase   :					int													=     self.get_image_processor()
									__UpperCAmelCase   :					Any													=     self.get_tokenizer()

									__UpperCAmelCase   :					Optional[Any]													=     FlavaProcessor(tokenizer=__lowercase	,	image_processor=__lowercase			)

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

									__UpperCAmelCase   :					str													=     processor.batch_decode(__lowercase			)
									__UpperCAmelCase   :					int													=     tokenizer.batch_decode(__lowercase			)

									self.assertListEqual(__lowercase	,	__lowercase			)




							def 			snake_case__					(       self							:			Union[str, Any]			):
									'''simple docstring'''





									__UpperCAmelCase   :					Optional[Any]													=     self.get_image_processor()
									__UpperCAmelCase   :					List[Any]													=     self.get_tokenizer()

									__UpperCAmelCase   :					Dict													=     FlavaProcessor(tokenizer=__lowercase	,	image_processor=__lowercase			)

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

									__UpperCAmelCase   :					Union[str, Any]													=     processor(text=__lowercase	,	images=__lowercase			)

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










import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	42

class 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     get_tests_dir('''fixtures/test_sentencepiece.model'''			)
					__UpperCAmelCase   :					Union[str, Any]													=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''			)

					__UpperCAmelCase   :					List[str]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Optional[int]													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	0			)

					__UpperCAmelCase   :					Optional[int]													=     FakeOriginalTokenizer(vocab_file=a_			)

					with warnings.catch_warnings(record=a_			) as w:
							__UpperCAmelCase   :					Dict													=     SpmConverter(a_			)
					self.assertEqual(len(a_			)	,	1			)

					self.assertIn(
					    '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
					    ''' which is not implemented in the fast tokenizers.'''	,	str(w[0].message			)	,	)





