
'''simple docstring'''
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


lowercase__		    =	logging.get_logger(__name__)

lowercase__		    =	{
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}






class      snake_case__    (	__SCREAMING_SNAKE_CASE ):







		"""simple docstring"""

		lowerCamelCase							=		"""deberta"""



		def __init__(	self							:       Optional[Any]   ,     UpperCamelCase__							:       Dict=5_0265   ,     UpperCamelCase__							:       Optional[Any]=768   ,     UpperCamelCase__							:       int=12   ,     UpperCamelCase__							:       List[str]=12   ,     UpperCamelCase__							:       Dict=3072   ,     UpperCamelCase__							:       Any="gelu"   ,     UpperCamelCase__							:       Optional[int]=0.1   ,     UpperCamelCase__							:       Union[str, Any]=0.1   ,     UpperCamelCase__							:       Union[str, Any]=512   ,     UpperCamelCase__							:       str=0   ,     UpperCamelCase__							:       Tuple=0.02   ,     UpperCamelCase__							:       str=1e-7   ,     UpperCamelCase__							:       Optional[int]=False   ,     UpperCamelCase__							:       Tuple=-1   ,     UpperCamelCase__							:       List[Any]=0   ,     UpperCamelCase__							:       Dict=True   ,     UpperCamelCase__							:       List[Any]=None   ,     UpperCamelCase__							:       str=0   ,     UpperCamelCase__							:       str="gelu"   ,     **UpperCamelCase__							:       Dict   ,     )		->       int:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__	)

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

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

				snake_case   :    Optional[Any]			       =		pos_att_type
				snake_case   :    Tuple			       =		vocab_size
				snake_case   :    Union[str, Any]			       =		layer_norm_eps

				snake_case   :    Union[str, Any]			       =		kwargs.get('''pooler_hidden_size'''   ,     UpperCamelCase__	)
				snake_case   :    str			       =		pooler_dropout
				snake_case   :    Optional[int]			       =		pooler_hidden_act







class      snake_case__    (	__SCREAMING_SNAKE_CASE ):







		"""simple docstring"""

		@property
		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				if self.task == "multiple-choice":
						snake_case   :    List[Any]			       =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
						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 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				return 12



		def 	lowerCAmelCase							(	self							:       Tuple   ,     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   ,     )		->       Mapping[str, Any]:






				"""simple docstring"""







				snake_case   :    int			       =		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









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 	lowerCAmelCase__(       unittest.TestCase			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		str  			=							tempfile.mkdtemp()

            # fmt: off
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
            # fmt: on
            _SCREAMING_SNAKE_CASE				:		int  			=							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]							)							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							{
                "image_mean": FLAVA_IMAGE_MEAN,
                "image_std": FLAVA_IMAGE_STD,
                "do_normalize": True,
                "do_resize": True,
                "size": 2_2_4,
                "do_center_crop": True,
                "crop_size": 2_2_4,
                "input_size_patches": 1_4,
                "total_mask_patches": 7_5,
                "mask_group_max_patches": None,
                "mask_group_min_patches": 1_6,
                "mask_group_min_aspect_ratio": 0.3,
                "mask_group_max_aspect_ratio": None,
                "codebook_do_resize": True,
                "codebook_size": 1_1_2,
                "codebook_do_center_crop": True,
                "codebook_crop_size": 1_1_2,
                "codebook_do_map_pixels": True,
                "codebook_do_normalize": True,
                "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
                "codebook_image_std": FLAVA_CODEBOOK_STD,
            }

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							os.path.join(self.tmpdirname			,  __lowerCamelCase							)
            with open(self.image_processor_file			,  "w"			,  encoding="utf-8"							) as fp:
                 json.dump(__lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   List[str]:
            return BertTokenizer.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Optional[Any]:
            return BertTokenizerFast.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Tuple:
            return FlavaImageProcessor.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   int:
            shutil.rmtree(self.tmpdirname							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta							)]

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

            return image_inputs




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_rust_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_image_processor()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_slow.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							FlavaProcessor.from_pretrained(self.tmpdirname			,  use_fast=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_fast.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		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			,  __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 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(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				:		List[str]  			=							self.get_image_processor(do_normalize=__lowerCamelCase			,  padding_value=1.0							)

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor.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 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Any  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		int  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processor(__lowerCamelCase			,  return_tensors="np"							)
            _SCREAMING_SNAKE_CASE				:		str  			=							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							)

            # With rest of the args
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_processor(
                __lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_tensors="np"							)
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							processor(
                images=__lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__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 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							processor(text=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tokenizer(__lowerCamelCase							)

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




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

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

            # add extra args
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_image_mask=__lowerCamelCase							)

            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(__lowerCamelCase							):
                 processor()




       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[[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				:		Any  			=							tokenizer.batch_decode(__lowerCamelCase							)

            self.assertListEqual(__lowerCamelCase			,  __lowerCamelCase							)







       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		int  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

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