code: 
  
  '''simple docstring'''
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _lowerCAmelCase     :			Optional[int]    =logging.get_logger(__name__)
  
  _lowerCAmelCase     :			List[Any]    ={
      """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
  }
  
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      "mpnet"
  
  
  
  
  
  					def __init__(							self							,			lowerCamelCase__=3_0_5_2_7							,			lowerCamelCase__=7_6_8							,			lowerCamelCase__=1_2							,			lowerCamelCase__=1_2							,			lowerCamelCase__=3_0_7_2							,			lowerCamelCase__="gelu"							,			lowerCamelCase__=0.1							,			lowerCamelCase__=0.1							,			lowerCamelCase__=5_1_2							,			lowerCamelCase__=0.02							,			lowerCamelCase__=1e-12							,			lowerCamelCase__=3_2							,			lowerCamelCase__=1							,			lowerCamelCase__=0							,			lowerCamelCase__=2							,			**lowerCamelCase__							,			):
  						super().__init__(pad_token_id=lowerCamelCase__							,			bos_token_id=lowerCamelCase__							,			eos_token_id=lowerCamelCase__							,			**lowerCamelCase__	)
  
  						UpperCAmelCase__:  Tuple		       =   vocab_size
  						UpperCAmelCase__:  Union[str, Any]		       =   hidden_size
  						UpperCAmelCase__:  int		       =   num_hidden_layers
  						UpperCAmelCase__:  List[str]		       =   num_attention_heads
  						UpperCAmelCase__:  Optional[int]		       =   hidden_act
  						UpperCAmelCase__:  Any		       =   intermediate_size
  						UpperCAmelCase__:  Optional[int]		       =   hidden_dropout_prob
  						UpperCAmelCase__:  Union[str, Any]		       =   attention_probs_dropout_prob
  						UpperCAmelCase__:  Any		       =   max_position_embeddings
  						UpperCAmelCase__:  str		       =   initializer_range
  						UpperCAmelCase__:  Union[str, Any]		       =   layer_norm_eps
  						UpperCAmelCase__:  Any		       =   relative_attention_num_buckets
style_context: 
  
  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 __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[int]		       =   tempfile.mkdtemp()
  
  						# fmt: off
  						UpperCAmelCase__:  Optional[Any]		       =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
  						# fmt: on
  						UpperCAmelCase__:  str		       =   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]	)	)
  
  						UpperCAmelCase__:  Tuple		       =   {
  						    "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,
  						}
  
  						UpperCAmelCase__:  Tuple		       =   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__	):
  						return BertTokenizer.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return BertTokenizerFast.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return FlavaImageProcessor.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						shutil.rmtree(self.tmpdirname	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   [np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta	)]
  
  						UpperCAmelCase__:  Any		       =   [Image.fromarray(np.moveaxis(lowerCamelCase__							,			0							,			-1	)	) for x in image_inputs]
  
  						return image_inputs
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  						UpperCAmelCase__:  Any		       =   self.get_rust_tokenizer()
  						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
  
  						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  						processor_slow.save_pretrained(self.tmpdirname	)
  						UpperCAmelCase__:  Dict		       =   FlavaProcessor.from_pretrained(self.tmpdirname							,			use_fast=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  						processor_fast.save_pretrained(self.tmpdirname	)
  						UpperCAmelCase__:  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	):
  						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()	)
  						processor.save_pretrained(self.tmpdirname	)
  
  						UpperCAmelCase__:  Any		       =   self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"	)
  						UpperCAmelCase__:  str		       =   self.get_image_processor(do_normalize=lowerCamelCase__							,			padding_value=1.0	)
  
  						UpperCAmelCase__:  str		       =   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	):
  						UpperCAmelCase__:  int		       =   self.get_image_processor()
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Optional[Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[int]		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  Tuple		       =   image_processor(lowerCamelCase__							,			return_tensors="np"	)
  						UpperCAmelCase__:  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	)
  						UpperCAmelCase__:  Optional[int]		       =   image_processor(
  						    lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
  						random.seed(1_2_3_4	)
  						UpperCAmelCase__:  Tuple		       =   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	):
  						UpperCAmelCase__:  Dict		       =   self.get_image_processor()
  						UpperCAmelCase__:  str		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  List[str]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   "lower newer"
  
  						UpperCAmelCase__:  Optional[Any]		       =   processor(text=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__	)
  
  						for key in encoded_tok.keys():
  							self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  str		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Any		       =   "lower newer"
  						UpperCAmelCase__:  Any		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  Optional[int]		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)
  
  						self.assertListEqual(list(inputs.keys()	)							,			["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)
  
  						# add extra args
  						UpperCAmelCase__:  str		       =   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	):
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_image_processor()
  						UpperCAmelCase__:  List[Any]		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  						UpperCAmelCase__:  str		       =   processor.batch_decode(lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__	)
  
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.get_image_processor()
  						UpperCAmelCase__:  Dict		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   "lower newer"
  						UpperCAmelCase__:  Dict		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  int		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)
  
  						self.assertListEqual(list(inputs.keys()	)							,			processor.model_input_names	)