import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class       lowercase__		(     unittest.TestCase):


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							tempfile.mkdtemp()

												# fmt: off
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
												# fmt: on
												SCREAMING_SNAKE_CASE							:					List[Any]           =							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							:					int           =							{
												    '''do_resize''': True,
												    '''size''': {'''height''': 18, '''width''': 18},
												    '''do_normalize''': True,
												    '''image_mean''': [0.5, 0.5, 0.5],
												    '''image_std''': [0.5, 0.5, 0.5],
												}
												SCREAMING_SNAKE_CASE							:					List[str]           =							os.path.join(self.tmpdirname					,	UpperCamelCase__		)
												with open(self.image_processor_file					,	'''w'''					,	encoding='''utf-8'''		) as fp:
																		json.dump(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Dict					,	**UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





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


						def  __A ( self			:       Tuple					,	**UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												return ViTImageProcessor.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





												shutil.rmtree(self.tmpdirname		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					int           =							[np.random.randint(255					,	size=(3, 30, 400)					,	dtype=np.uinta		)]

												SCREAMING_SNAKE_CASE							:					List[Any]           =							[Image.fromarray(np.moveaxis(UpperCamelCase__					,	0					,	-1		)		) for x in image_inputs]

												return image_inputs


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Dict           =							VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__					,	image_processor=UpperCamelCase__		)

												processor.save_pretrained(self.tmpdirname		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname		)

												self.assertEqual(processor.tokenizer.get_vocab()					,	tokenizer.get_vocab()		)
												self.assertIsInstance(processor.tokenizer					,	(BertTokenizer, BertTokenizerFast)		)

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


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							VisionTextDualEncoderProcessor(
												    tokenizer=self.get_tokenizer()					,	image_processor=self.get_image_processor()		)
												processor.save_pretrained(self.tmpdirname		)

												SCREAMING_SNAKE_CASE							:					str           =							self.get_tokenizer(bos_token='''(BOS)'''					,	eos_token='''(EOS)'''		)
												SCREAMING_SNAKE_CASE							:					Any           =							self.get_image_processor(do_normalize=UpperCamelCase__					,	padding_value=1.0		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							VisionTextDualEncoderProcessor.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					,	(BertTokenizer, BertTokenizerFast)		)

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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					Tuple           =							VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__					,	image_processor=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							image_processor(UpperCamelCase__					,	return_tensors='''np'''		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							processor(images=UpperCamelCase__					,	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  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:					int           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					str           =							VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__					,	image_processor=UpperCamelCase__		)

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

												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor(text=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							tokenizer(UpperCamelCase__		)

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


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					Tuple           =							VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__					,	image_processor=UpperCamelCase__		)

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

												SCREAMING_SNAKE_CASE							:					List[str]           =							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 self.assertRaises(UpperCamelCase__		):
																		processor()


						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__					,	image_processor=UpperCamelCase__		)

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

												SCREAMING_SNAKE_CASE							:					Dict           =							processor.batch_decode(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.batch_decode(UpperCamelCase__		)

												self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)



						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							self.get_image_processor()
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.get_tokenizer()

												SCREAMING_SNAKE_CASE							:					List[str]           =							VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase__					,	image_processor=UpperCamelCase__		)

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

												SCREAMING_SNAKE_CASE							:					str           =							processor(text=UpperCamelCase__					,	images=UpperCamelCase__		)

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






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''sequence'''}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]		)
												else:
																		return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





