


import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor

@require_tokenizers
@require_vision
class     a		(unittest.TestCase				):




							"""simple docstring"""


							def    __snake_case (							self	:  List[Any]    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       tempfile.mkdtemp()

														# fmt: off
														__snake_case		:      int										=       ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
														# fmt: on
														__snake_case		:      Dict										=       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		:      Optional[Any]										=       {
														    "do_resize": True,
														    "size": {"height": 18, "width": 18},
														    "do_normalize": True,
														    "image_mean": [0.5, 0.5, 0.5],
														    "image_std": [0.5, 0.5, 0.5],
														}
														__snake_case		:      str										=       os.path.join(self.tmpdirname   ,     lowerCamelCase    )
														with open(self.image_processor_file   ,     "w"   ,     encoding="utf-8"    ) as fp:
																					json.dump(lowerCamelCase   ,     lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     **lowerCamelCase	:  List[Any]    )     ->   List[str]:
														return BertTokenizer.from_pretrained(self.tmpdirname   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     **lowerCamelCase	:  int    )     ->   Optional[int]:
														return ViTImageProcessor.from_pretrained(self.tmpdirname   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														shutil.rmtree(self.tmpdirname    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       [np.random.randint(255   ,     size=(3, 30, 400)   ,     dtype=np.uinta    )]

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

														return image_inputs

							def    __snake_case (							self	:  Any    )     ->   str:
														__snake_case		:      Optional[Any]										=       self.get_tokenizer()
														__snake_case		:      str										=       self.get_image_processor()

														__snake_case		:      Any										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														processor.save_pretrained(self.tmpdirname    )
														__snake_case		:      List[Any]										=       VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

														self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()    )
														self.assertIsInstance(processor.tokenizer   ,     (BertTokenizer, BertTokenizerFast)    )

														self.assertEqual(processor.image_processor.to_json_string()   ,     image_processor.to_json_string()    )
														self.assertIsInstance(processor.image_processor   ,     lowerCamelCase    )

							def    __snake_case (							self	:  Optional[int]    )     ->   List[Any]:
														__snake_case		:      Optional[int]										=       VisionTextDualEncoderProcessor(
														    tokenizer=self.get_tokenizer()   ,     image_processor=self.get_image_processor()    )
														processor.save_pretrained(self.tmpdirname    )

														__snake_case		:      Tuple										=       self.get_tokenizer(bos_token="(BOS)"   ,     eos_token="(EOS)"    )
														__snake_case		:      Tuple										=       self.get_image_processor(do_normalize=lowerCamelCase   ,     padding_value=1.0    )

														__snake_case		:      List[Any]										=       VisionTextDualEncoderProcessor.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   ,     (BertTokenizer, BertTokenizerFast)    )

														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	:  Dict    )     ->   List[Any]:
														__snake_case		:      Dict										=       self.get_image_processor()
														__snake_case		:      List[Any]										=       self.get_tokenizer()

														__snake_case		:      Union[str, Any]										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      List[str]										=       self.prepare_image_inputs()

														__snake_case		:      Dict										=       image_processor(lowerCamelCase   ,     return_tensors="np"    )
														__snake_case		:      Any										=       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	:  Any    )     ->   Any:
														__snake_case		:      Tuple										=       self.get_image_processor()
														__snake_case		:      str										=       self.get_tokenizer()

														__snake_case		:      int										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      int										=       "lower newer"

														__snake_case		:      str										=       processor(text=lowerCamelCase    )

														__snake_case		:      Tuple										=       tokenizer(lowerCamelCase    )

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

							def    __snake_case (							self	:  Tuple    )     ->   Optional[int]:
														__snake_case		:      str										=       self.get_image_processor()
														__snake_case		:      List[str]										=       self.get_tokenizer()

														__snake_case		:      str										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      Dict										=       "lower newer"
														__snake_case		:      Dict										=       self.prepare_image_inputs()

														__snake_case		:      int										=       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 self.assertRaises(lowerCamelCase    ):
																					processor()

							def    __snake_case (							self	:  Any    )     ->   Optional[Any]:
														__snake_case		:      List[Any]										=       self.get_image_processor()
														__snake_case		:      int										=       self.get_tokenizer()

														__snake_case		:      Optional[Any]										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

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

														__snake_case		:      str										=       processor.batch_decode(lowerCamelCase    )
														__snake_case		:      Dict										=       tokenizer.batch_decode(lowerCamelCase    )

														self.assertListEqual(lowerCamelCase   ,     lowerCamelCase    )






							def    __snake_case (							self	:  List[str]    )     ->   List[str]:
														__snake_case		:      Dict										=       self.get_image_processor()
														__snake_case		:      Any										=       self.get_tokenizer()

														__snake_case		:      Tuple										=       VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase   ,     image_processor=lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       "lower newer"
														__snake_case		:      List[str]										=       self.prepare_image_inputs()

														__snake_case		:      Dict										=       processor(text=lowerCamelCase   ,     images=lowerCamelCase    )

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





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

