
'''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 						__snake_case				(				self						:					Dict   ):
										snake_case							:  List[Any]			=     tempfile.mkdtemp()

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

										snake_case							:  int			=     {
										    '''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,
										}

										snake_case							:  int			=     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 						__snake_case				(				self						:					Optional[int],     **SCREAMING_SNAKE_CASE_						:					int   ):
										return BertTokenizer.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					str,     **SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return BertTokenizerFast.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										return FlavaImageProcessor.from_pretrained(self.tmpdirname,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any]   ):
										shutil.rmtree(self.tmpdirname   )




				def 						__snake_case				(				self						:					List[Any]   ):
										snake_case							:  Tuple			=     [np.random.randint(2_55,     size=(3, 30, 4_00),     dtype=np.uinta   )]

										snake_case							:  Union[str, Any]			=     [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_,     0,     -1   )   ) for x in image_inputs]

										return image_inputs




				def 						__snake_case				(				self						:					str   ):
										snake_case							:  Optional[int]			=     self.get_tokenizer()
										snake_case							:  str			=     self.get_rust_tokenizer()
										snake_case							:  Any			=     self.get_image_processor()

										snake_case							:  int			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )
										processor_slow.save_pretrained(self.tmpdirname   )
										snake_case							:  Any			=     FlavaProcessor.from_pretrained(self.tmpdirname,     use_fast=SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )
										processor_fast.save_pretrained(self.tmpdirname   )
										snake_case							:  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,     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 						__snake_case				(				self						:					List[str]   ):
										snake_case							:  int			=     FlavaProcessor(tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor()   )
										processor.save_pretrained(self.tmpdirname   )

										snake_case							:  Union[str, Any]			=     self.get_tokenizer(bos_token='''(BOS)''',     eos_token='''(EOS)'''   )
										snake_case							:  Optional[Any]			=     self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,     padding_value=1.0   )

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

										snake_case							:  int			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[Any]			=     self.prepare_image_inputs()

										snake_case							:  Optional[int]			=     image_processor(SCREAMING_SNAKE_CASE_,     return_tensors='''np'''   )
										snake_case							:  Optional[int]			=     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(12_34   )
										snake_case							:  Dict			=     image_processor(
										    SCREAMING_SNAKE_CASE_,     return_image_mask=SCREAMING_SNAKE_CASE_,     return_codebook_pixels=SCREAMING_SNAKE_CASE_,     return_tensors='''np'''   )
										random.seed(12_34   )
										snake_case							:  Union[str, Any]			=     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 						__snake_case				(				self						:					Tuple   ):
										snake_case							:  List[Any]			=     self.get_image_processor()
										snake_case							:  Dict			=     self.get_tokenizer()

										snake_case							:  Optional[Any]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Any			=     '''lower newer'''

										snake_case							:  Tuple			=     processor(text=SCREAMING_SNAKE_CASE_   )

										snake_case							:  int			=     tokenizer(SCREAMING_SNAKE_CASE_   )

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




				def 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Any			=     self.get_image_processor()
										snake_case							:  Optional[Any]			=     self.get_tokenizer()

										snake_case							:  Optional[Any]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

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

										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
										snake_case							:  Tuple			=     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 						__snake_case				(				self						:					Dict   ):
										snake_case							:  Any			=     self.get_image_processor()
										snake_case							:  Union[str, Any]			=     self.get_tokenizer()

										snake_case							:  Dict			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										snake_case							:  List[str]			=     processor.batch_decode(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_   )

										self.assertListEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Any   ):
										snake_case							:  int			=     self.get_image_processor()
										snake_case							:  Union[str, Any]			=     self.get_tokenizer()

										snake_case							:  List[Any]			=     FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_,     image_processor=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Dict			=     '''lower newer'''
										snake_case							:  Any			=     self.prepare_image_inputs()

										snake_case							:  Optional[Any]			=     processor(text=SCREAMING_SNAKE_CASE_,     images=SCREAMING_SNAKE_CASE_   )

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








'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


_lowerCamelCase					:						str					     =							{
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						List[str]					     =							['ErnieMTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase					:						Union[str, Any]					     =							[
				    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ErnieMForMultipleChoice',
				    'ErnieMForQuestionAnswering',
				    'ErnieMForSequenceClassification',
				    'ErnieMForTokenClassification',
				    'ErnieMModel',
				    'ErnieMPreTrainedModel',
				    'ErnieMForInformationExtraction',
				]


if TYPE_CHECKING:
				from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ernie_m import ErnieMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ernie_m import (
								    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ErnieMForInformationExtraction,
								    ErnieMForMultipleChoice,
								    ErnieMForQuestionAnswering,
								    ErnieMForSequenceClassification,
								    ErnieMForTokenClassification,
								    ErnieMModel,
								    ErnieMPreTrainedModel,
								)


else:
				import sys

				_lowerCamelCase					:						int					     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

