
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					):

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   tempfile.mkdtemp()

            # fmt: off
            __snake_case						       =   ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
            # fmt: on
            __snake_case						       =   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]    )    )

            __snake_case						       =   {
                'image_mean': FLAVA_IMAGE_MEAN,
                'image_std': FLAVA_IMAGE_STD,
                'do_normalize': True,
                'do_resize': True,
                'size': 224,
                'do_center_crop': True,
                'crop_size': 224,
                'input_size_patches': 14,
                'total_mask_patches': 75,
                'mask_group_max_patches': None,
                'mask_group_min_patches': 16,
                'mask_group_min_aspect_ratio': 0.3,
                'mask_group_max_aspect_ratio': None,
                'codebook_do_resize': True,
                'codebook_size': 112,
                'codebook_do_center_crop': True,
                'codebook_crop_size': 112,
                'codebook_do_map_pixels': True,
                'codebook_do_normalize': True,
                'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
                'codebook_image_std': FLAVA_CODEBOOK_STD,
            }

            __snake_case						       =   os.path.join(self.tmpdirname							,       SCREAMING_SNAKE_CASE_    )
            with open(self.image_processor_file							,       'w'							,       encoding='utf-8'    ) as fp:
                   json.dump(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       **SCREAMING_SNAKE_CASE_    ):
            return BertTokenizer.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       **SCREAMING_SNAKE_CASE_    ):
            return BertTokenizerFast.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       **SCREAMING_SNAKE_CASE_    ):
            return FlavaImageProcessor.from_pretrained(self.tmpdirname							,       **SCREAMING_SNAKE_CASE_    )

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   [np.random.randint(255							,       size=(3, 30, 400)							,       dtype=np.uinta    )]

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

            return image_inputs

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.get_tokenizer()
            __snake_case						       =   self.get_rust_tokenizer()
            __snake_case						       =   self.get_image_processor()

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )
            processor_slow.save_pretrained(self.tmpdirname    )
            __snake_case						       =   FlavaProcessor.from_pretrained(self.tmpdirname							,       use_fast=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )
            processor_fast.save_pretrained(self.tmpdirname    )
            __snake_case						       =   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							,       SCREAMING_SNAKE_CASE_    )
            self.assertIsInstance(processor_fast.tokenizer							,       SCREAMING_SNAKE_CASE_    )

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,       image_processor=self.get_image_processor()    )
            processor.save_pretrained(self.tmpdirname    )

            __snake_case						       =   self.get_tokenizer(bos_token='(BOS)'							,       eos_token='(EOS)'    )
            __snake_case						       =   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_							,       padding_value=1.0    )

            __snake_case						       =   FlavaProcessor.from_pretrained(
                self.tmpdirname							,       bos_token='(BOS)'							,       eos_token='(EOS)'							,       do_normalize=SCREAMING_SNAKE_CASE_							,       padding_value=1.0    )

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

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.get_image_processor()
            __snake_case						       =   self.get_tokenizer()

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   self.prepare_image_inputs()

            __snake_case						       =   image_processor(SCREAMING_SNAKE_CASE_							,       return_tensors='np'    )
            __snake_case						       =   processor(images=SCREAMING_SNAKE_CASE_							,       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_234    )
            __snake_case						       =   image_processor(
                SCREAMING_SNAKE_CASE_							,       return_image_mask=SCREAMING_SNAKE_CASE_							,       return_codebook_pixels=SCREAMING_SNAKE_CASE_							,       return_tensors='np'    )
            random.seed(1_234    )
            __snake_case						       =   processor(
                images=SCREAMING_SNAKE_CASE_							,       return_image_mask=SCREAMING_SNAKE_CASE_							,       return_codebook_pixels=SCREAMING_SNAKE_CASE_							,       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 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.get_image_processor()
            __snake_case						       =   self.get_tokenizer()

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   'lower newer'

            __snake_case						       =   processor(text=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer(SCREAMING_SNAKE_CASE_    )

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

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.get_image_processor()
            __snake_case						       =   self.get_tokenizer()

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   'lower newer'
            __snake_case						       =   self.prepare_image_inputs()

            __snake_case						       =   processor(text=SCREAMING_SNAKE_CASE_							,       images=SCREAMING_SNAKE_CASE_    )

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

            # add extra args
            __snake_case						       =   processor(text=SCREAMING_SNAKE_CASE_							,       images=SCREAMING_SNAKE_CASE_							,       return_codebook_pixels=SCREAMING_SNAKE_CASE_							,       return_image_mask=SCREAMING_SNAKE_CASE_    )

            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(SCREAMING_SNAKE_CASE_    ):
                   processor()

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.get_image_processor()
            __snake_case						       =   self.get_tokenizer()

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )

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

            __snake_case						       =   processor.batch_decode(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    )

            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.get_image_processor()
            __snake_case						       =   self.get_tokenizer()

            __snake_case						       =   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_							,       image_processor=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   'lower newer'
            __snake_case						       =   self.prepare_image_inputs()

            __snake_case						       =   processor(text=SCREAMING_SNAKE_CASE_							,       images=SCREAMING_SNAKE_CASE_    )

            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_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "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  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

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

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {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    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
