






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A        =			logging.get_logger(__name__)

A        =			{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						"""lilt"""


		def __init__(						self ,    UpperCamelCase__=30522 ,    UpperCamelCase__=768 ,    UpperCamelCase__=12 ,    UpperCamelCase__=12 ,    UpperCamelCase__=3072 ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=512 ,    UpperCamelCase__=2 ,    UpperCamelCase__=0.02 ,    UpperCamelCase__=1E-12 ,    UpperCamelCase__=0 ,    UpperCamelCase__="absolute" ,    UpperCamelCase__=None ,    UpperCamelCase__=4 ,    UpperCamelCase__=1024 ,    **UpperCamelCase__ ,    ):
						super().__init__(pad_token_id=UpperCamelCase__ ,    **UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   vocab_size
						SCREAMING_SNAKE_CASE_   :	Dict													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	int													=   num_attention_heads
						SCREAMING_SNAKE_CASE_   :	str													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   intermediate_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_dropout_prob
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   max_position_embeddings
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   type_vocab_size
						SCREAMING_SNAKE_CASE_   :	int													=   initializer_range
						SCREAMING_SNAKE_CASE_   :	int													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   position_embedding_type
						SCREAMING_SNAKE_CASE_   :	Dict													=   classifier_dropout
						SCREAMING_SNAKE_CASE_   :	str													=   channel_shrink_ratio
						SCREAMING_SNAKE_CASE_   :	Any													=   max_ad_position_embeddings







import json
import os
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 AlignProcessor, EfficientNetImageProcessor


@require_vision
class 							__a							(  unittest.TestCase						):





		'''simple docstring'''



		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   tempfile.mkdtemp()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   [
						    '[UNK]',
						    '[CLS]',
						    '[SEP]',
						    '[PAD]',
						    '[MASK]',
						    'want',
						    '##want',
						    '##ed',
						    'wa',
						    'un',
						    'runn',
						    '##ing',
						    ',',
						    'low',
						    'lowest',
						]
						SCREAMING_SNAKE_CASE_   :	Dict													=   os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['vocab_file']       )
						with open(self.vocab_file ,    'w' ,    encoding='utf-8'       ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   {
						    'do_resize': True,
						    'size': 20,
						    'do_center_crop': True,
						    'crop_size': 18,
						    'do_normalize': True,
						    'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
						    'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
						}
						SCREAMING_SNAKE_CASE_   :	str													=   os.path.join(self.tmpdirname ,    UpperCamelCase__       )
						with open(self.image_processor_file ,    'w' ,    encoding='utf-8'       ) as fp:
										json.dump(UpperCamelCase__ ,    UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizer.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return BertTokenizerFast.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    **UpperCamelCase__       ):
						return EfficientNetImageProcessor.from_pretrained(self.tmpdirname ,    **UpperCamelCase__       )


		def   __snake_case			(						self       ):
						shutil.rmtree(self.tmpdirname       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [np.random.randint(255 ,    size=(3, 30, 400) ,    dtype=np.uinta       )]
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [Image.fromarray(np.moveaxis(UpperCamelCase__ ,    0 ,    -1       )       ) for x in image_inputs]
						return image_inputs


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.get_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_rust_tokenizer()
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_slow.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.from_pretrained(self.tmpdirname ,    use_fast=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )
						processor_fast.save_pretrained(self.tmpdirname       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor.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 ,    UpperCamelCase__       )
						self.assertIsInstance(processor_fast.tokenizer ,    UpperCamelCase__       )

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=self.get_tokenizer() ,    image_processor=self.get_image_processor()       )
						processor.save_pretrained(self.tmpdirname       )

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   AlignProcessor.from_pretrained(
						    self.tmpdirname ,    bos_token='(BOS)' ,    eos_token='(EOS)' ,    do_normalize=UpperCamelCase__ ,    padding_value=1.0       )

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

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	str													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.prepare_image_inputs()

						SCREAMING_SNAKE_CASE_   :	int													=   image_processor(UpperCamelCase__ ,    return_tensors='np'       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   processor(images=UpperCamelCase__ ,    return_tensors='np'       )

						for key in input_image_proc.keys():
										self.assertAlmostEqual(input_image_proc[key].sum() ,    input_processor[key].sum() ,    delta=1E-2       )


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	List[str]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   'lower newer'

						SCREAMING_SNAKE_CASE_   :	Tuple													=   processor(text=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	int													=   tokenizer(UpperCamelCase__ ,    padding='max_length' ,    max_length=64       )

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


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	str													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

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

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

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

						# test if it raises when no input is passed
						with pytest.raises(UpperCamelCase__       ):
										processor()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Tuple													=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						SCREAMING_SNAKE_CASE_   :	List[str]													=   processor.batch_decode(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   tokenizer.batch_decode(UpperCamelCase__       )

						self.assertListEqual(UpperCamelCase__ ,    UpperCamelCase__       )






		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	Dict													=   self.get_image_processor()
						SCREAMING_SNAKE_CASE_   :	int													=   self.get_tokenizer()

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   AlignProcessor(tokenizer=UpperCamelCase__ ,    image_processor=UpperCamelCase__       )

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

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   processor(text=UpperCamelCase__ ,    images=UpperCamelCase__       )

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