





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   tempfile.mkdtemp()

										__SCREAMING_SNAKE_CASE:				Any           =   BlipImageProcessor()
										__SCREAMING_SNAKE_CASE:				int           =   GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   BlipaProcessor(_lowerCAmelCase			, _lowerCAmelCase					)

										processor.save_pretrained(self.tmpdirname					)






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




										return AutoProcessor.from_pretrained(self.tmpdirname			, **_lowerCAmelCase					).tokenizer






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




										return AutoProcessor.from_pretrained(self.tmpdirname			, **_lowerCAmelCase					).image_processor






				def 			snake_case_    (  self					):

										"""simple docstring"""




										shutil.rmtree(self.tmpdirname					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   [np.random.randint(255			, size=(3, 30, 400)			, dtype=np.uinta					)]

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

										return image_inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   BlipaProcessor(tokenizer=self.get_tokenizer()			, image_processor=self.get_image_processor()					)
										processor.save_pretrained(self.tmpdirname					)

										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_tokenizer(bos_token='''(BOS)'''			, eos_token='''(EOS)'''					)
										__SCREAMING_SNAKE_CASE:				str           =   self.get_image_processor(do_normalize=_lowerCAmelCase			, padding_value=1.0					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   BlipaProcessor.from_pretrained(
										    self.tmpdirname			, bos_token='''(BOS)'''			, eos_token='''(EOS)'''			, do_normalize=_lowerCAmelCase			, padding_value=1.0					)

										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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_image_processor()
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_tokenizer()

										__SCREAMING_SNAKE_CASE:				Tuple           =   BlipaProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   self.prepare_image_inputs()

										__SCREAMING_SNAKE_CASE:				str           =   image_processor(_lowerCAmelCase			, return_tensors='''np'''					)
										__SCREAMING_SNAKE_CASE:				int           =   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 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				Dict           =   BlipaProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				Tuple           =   processor(text=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Any           =   tokenizer(_lowerCAmelCase			, return_token_type_ids=_lowerCAmelCase					)

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				List[Any]           =   BlipaProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   processor(text=_lowerCAmelCase			, images=_lowerCAmelCase					)

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

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.get_image_processor()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.get_tokenizer()

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   BlipaProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				Dict           =   processor.batch_decode(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer.batch_decode(_lowerCAmelCase					)

										self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   BlipaProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

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

										# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
										self.assertListEqual(list(inputs.keys()					)			, ['''pixel_values''', '''input_ids''', '''attention_mask''']					)










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



