
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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= tempfile.mkdtemp()

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

					__A   			= {
					    "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],
					}
					__A   			= os.path.join(self.tmpdirname							,A )
					with open(self.image_processor_file							,"w"							,encoding="utf-8" ) as fp:
									json.dump(A							,A )






	def 				UpperCamelCase_  (       self					: int							,**A					: List[Any] ):
					return BertTokenizer.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: int							,**A					: Optional[Any] ):
					return ViTImageProcessor.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					shutil.rmtree(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= [np.random.randint(2_55							,size=(3, 30, 4_00)							,dtype=np.uinta )]

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

					return image_inputs






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.get_tokenizer()
					__A   			= self.get_image_processor()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					processor.save_pretrained(self.tmpdirname )
					__A   			= 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							,A )






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= VisionTextDualEncoderProcessor(
					    tokenizer=self.get_tokenizer()							,image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

					__A   			= self.get_tokenizer(bos_token="(BOS)"							,eos_token="(EOS)" )
					__A   			= self.get_image_processor(do_normalize=A							,padding_value=1.0 )

					__A   			= VisionTextDualEncoderProcessor.from_pretrained(
					    self.tmpdirname							,bos_token="(BOS)"							,eos_token="(EOS)"							,do_normalize=A							,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							,A )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= self.prepare_image_inputs()

					__A   			= image_processor(A							,return_tensors="np" )
					__A   			= processor(images=A							,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					: Optional[int] ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"

					__A   			= processor(text=A )

					__A   			= tokenizer(A )

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






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

					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(A ):
									processor()






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

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

					__A   			= processor.batch_decode(A )
					__A   			= tokenizer.batch_decode(A )

					self.assertListEqual(A							,A )







	def 				UpperCamelCase_  (       self					: int ):
					__A   			= self.get_image_processor()
					__A   			= self.get_tokenizer()

					__A   			= VisionTextDualEncoderProcessor(tokenizer=A							,image_processor=A )

					__A   			= "lower newer"
					__A   			= self.prepare_image_inputs()

					__A   			= processor(text=A							,images=A )

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





import argparse
import json

from tqdm import tqdm
def 		UpperCamelCase	(   )	->     Optional[int]:
   UpperCamelCase    :  List[Any]							=	argparse.ArgumentParser()

   # Required parameters
   parser.add_argument(
       '--src_path'       ,     type=snake_case__       ,     default='biencoder-nq-dev.json'       ,     help='Path to raw DPR training data'       ,     )
   parser.add_argument(
       '--evaluation_set'       ,     type=snake_case__       ,     help='where to store parsed evaluation_set file'       ,     )
   parser.add_argument(
       '--gold_data_path'       ,     type=snake_case__       ,     help='where to store parsed gold_data_path file'       ,     )
   UpperCamelCase    :  int							=	parser.parse_args()

   with open(args.src_path       ,     'r'	) as src_file, open(args.evaluation_set       ,     'w'	) as eval_file, open(
       args.gold_data_path       ,     'w'	) as gold_file:
      UpperCamelCase    :  int							=	json.load(snake_case__	)
      for dpr_record in tqdm(snake_case__	):
         UpperCamelCase    :  Union[str, Any]							=	dpr_record['question']
         UpperCamelCase    :  Dict							=	[context['title'] for context in dpr_record['positive_ctxs']]
         eval_file.write(question + '\n'	)
         gold_file.write('\t'.join(snake_case__	) + '\n'	)


if __name__ == "__main__":
    main()



