



'''simple docstring'''





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 FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							Dict  )      ->   int:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    tempfile.mkdtemp()

       __snake_case :   Union[str, Any]													=    [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''的''',
           '''价''',
           '''格''',
           '''是''',
           '''15''',
           '''便''',
           '''alex''',
           '''##andra''',
           '''，''',
           '''。''',
           '''-''',
           '''t''',
           '''shirt''',
       ]
       __snake_case :   int													=    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]  )  )

       __snake_case :   Any													=    {
           '''do_resize''': True,
           '''size''': {'''height''': 224, '''width''': 224},
           '''do_center_crop''': True,
           '''crop_size''': {'''height''': 18, '''width''': 18},
           '''do_normalize''': True,
           '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
           '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
           '''do_convert_rgb''': True,
       }
       __snake_case :   Tuple													=    os.path.join(self.tmpdirname      ,     _lowerCAmelCase  )
       with open(self.image_processor_file      ,     'w'      ,     encoding='utf-8'  ) as fp:
             json.dump(_lowerCAmelCase      ,     _lowerCAmelCase  )
 def A_   (   self						:							List[str]      ,     **__a						:							Optional[int]  )      ->   Any:







       '''simple docstring'''
       return BertTokenizer.from_pretrained(self.tmpdirname      ,     **_lowerCAmelCase  )
 def A_   (   self						:							Union[str, Any]      ,     **__a						:							str  )      ->   List[str]:







       '''simple docstring'''
       return BertTokenizerFast.from_pretrained(self.tmpdirname      ,     **_lowerCAmelCase  )
 def A_   (   self						:							int      ,     **__a						:							Dict  )      ->   str:







       '''simple docstring'''
       return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,     **_lowerCAmelCase  )
 def A_   (   self						:							str  )      ->   List[str]:







       '''simple docstring'''
       shutil.rmtree(self.tmpdirname  )
 def A_   (   self						:							Tuple  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Optional[int]													=    [np.random.randint(255      ,     size=(3, 30, 400)      ,     dtype=np.uinta  )]

       __snake_case :   str													=    [Image.fromarray(np.moveaxis(_lowerCAmelCase      ,     0      ,     -1  )  ) for x in image_inputs]

       return image_inputs
 def A_   (   self						:							Any  )      ->   Any:







       '''simple docstring'''
       __snake_case :   List[str]													=    self.get_tokenizer()
       __snake_case :   Optional[Any]													=    self.get_rust_tokenizer()
       __snake_case :   Optional[int]													=    self.get_image_processor()

       __snake_case :   Any													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )
       processor_slow.save_pretrained(self.tmpdirname  )
       __snake_case :   Optional[int]													=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,     use_fast=_lowerCAmelCase  )

       __snake_case :   str													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )
       processor_fast.save_pretrained(self.tmpdirname  )
       __snake_case :   List[Any]													=    ChineseCLIPProcessor.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      ,     _lowerCAmelCase  )
       self.assertIsInstance(processor_fast.tokenizer      ,     _lowerCAmelCase  )

       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      ,     _lowerCAmelCase  )
       self.assertIsInstance(processor_fast.image_processor      ,     _lowerCAmelCase  )
 def A_   (   self						:							Any  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Dict													=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,     image_processor=self.get_image_processor()  )
       processor.save_pretrained(self.tmpdirname  )

       __snake_case :   int													=    self.get_tokenizer(cls_token='(CLS)'      ,     sep_token='(SEP)'  )
       __snake_case :   Optional[int]													=    self.get_image_processor(do_normalize=_lowerCAmelCase  )

       __snake_case :   Optional[Any]													=    ChineseCLIPProcessor.from_pretrained(
           self.tmpdirname      ,     cls_token='(CLS)'      ,     sep_token='(SEP)'      ,     do_normalize=_lowerCAmelCase  )

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

       self.assertEqual(processor.image_processor.to_json_string()      ,     image_processor_add_kwargs.to_json_string()  )
       self.assertIsInstance(processor.image_processor      ,     _lowerCAmelCase  )
 def A_   (   self						:							Optional[Any]  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   Union[str, Any]													=    self.get_image_processor()
       __snake_case :   Union[str, Any]													=    self.get_tokenizer()

       __snake_case :   Union[str, Any]													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )

       __snake_case :   Optional[Any]													=    self.prepare_image_inputs()

       __snake_case :   Union[str, Any]													=    image_processor(_lowerCAmelCase      ,     return_tensors='np'  )
       __snake_case :   Dict													=    processor(images=_lowerCAmelCase      ,     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						:							Any  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    self.get_image_processor()
       __snake_case :   str													=    self.get_tokenizer()

       __snake_case :   List[Any]													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )

       __snake_case :   Any													=    '''Alexandra，T-shirt的价格是15便士。'''

       __snake_case :   List[Any]													=    processor(text=_lowerCAmelCase  )

       __snake_case :   Union[str, Any]													=    tokenizer(_lowerCAmelCase  )

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,     encoded_processor[key]  )
 def A_   (   self						:							Optional[Any]  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Any													=    self.get_image_processor()
       __snake_case :   Optional[Any]													=    self.get_tokenizer()

       __snake_case :   int													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )

       __snake_case :   List[Any]													=    '''Alexandra，T-shirt的价格是15便士。'''
       __snake_case :   Tuple													=    self.prepare_image_inputs()

       __snake_case :   List[str]													=    processor(text=_lowerCAmelCase      ,     images=_lowerCAmelCase  )

       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(_lowerCAmelCase  ):
             processor()
 def A_   (   self						:							List[str]  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    self.get_image_processor()
       __snake_case :   Any													=    self.get_tokenizer()

       __snake_case :   Optional[int]													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )

       __snake_case :   List[str]													=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       __snake_case :   Any													=    processor.batch_decode(_lowerCAmelCase  )
       __snake_case :   List[str]													=    tokenizer.batch_decode(_lowerCAmelCase  )

       self.assertListEqual(_lowerCAmelCase      ,     _lowerCAmelCase  )





 def A_   (   self						:							str  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Dict													=    self.get_image_processor()
       __snake_case :   Optional[Any]													=    self.get_tokenizer()

       __snake_case :   List[Any]													=    ChineseCLIPProcessor(tokenizer=_lowerCAmelCase      ,     image_processor=_lowerCAmelCase  )

       __snake_case :   Tuple													=    '''Alexandra，T-shirt的价格是15便士。'''
       __snake_case :   Dict													=    self.prepare_image_inputs()

       __snake_case :   Any													=    processor(text=_lowerCAmelCase      ,     images=_lowerCAmelCase  )

       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


lowerCAmelCase							:       Tuple					 =	logging.get_logger(__name__)

lowerCAmelCase							:       List[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 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   str		      =     '''deberta-v2'''






				def __init__(  self			, _lowerCAmelCase=128100			, _lowerCAmelCase=1536			, _lowerCAmelCase=24			, _lowerCAmelCase=24			, _lowerCAmelCase=6144			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=1e-7			, _lowerCAmelCase=False			, _lowerCAmelCase=-1			, _lowerCAmelCase=0			, _lowerCAmelCase=True			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, _lowerCAmelCase="gelu"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)

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

										# Backwards compatibility
										if type(_lowerCAmelCase					) == str:
																__SCREAMING_SNAKE_CASE:				str           =   [x.strip() for x in pos_att_type.lower().split('''|'''					)]

										__SCREAMING_SNAKE_CASE:				int           =   pos_att_type
										__SCREAMING_SNAKE_CASE:				List[Any]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   layer_norm_eps

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   kwargs.get('''pooler_hidden_size'''			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   pooler_dropout
										__SCREAMING_SNAKE_CASE:				int           =   pooler_hidden_act





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				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 			snake_case_    (  self					):

										"""simple docstring"""




										return 12






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, _lowerCAmelCase = 3			, _lowerCAmelCase = 40			, _lowerCAmelCase = 40			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   super().generate_dummy_inputs(preprocessor=_lowerCAmelCase			, framework=_lowerCAmelCase					)
										if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
										return dummy_inputs



