





'''simple docstring'''


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       lowerCAmelCase_						(						unittest.TestCase						):




 def 							_snake_case      (		self						)			->							Optional[Any]:
     _lowerCAmelCase			=					tempfile.mkdtemp()

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

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




 def 							_snake_case      (		self	,   **_lowerCAmelCase						)			->							int:
     return BertTokenizer.from_pretrained(self.tmpdirname	,   **_a						)




 def 							_snake_case      (		self	,   **_lowerCAmelCase						)			->							Optional[Any]:
     return ViTImageProcessor.from_pretrained(self.tmpdirname	,   **_a						)




 def 							_snake_case      (		self						)			->							Any:
     shutil.rmtree(self.tmpdirname						)




 def 							_snake_case      (		self						)			->							Tuple:
     _lowerCAmelCase			=					[np.random.randint(255	,   size=(3, 30, 400)	,   dtype=np.uinta						)]

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

     return image_inputs




 def 							_snake_case      (		self						)			->							Optional[Any]:
     _lowerCAmelCase			=					self.get_tokenizer()
     _lowerCAmelCase			=					self.get_image_processor()

     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(tokenizer=_a	,   image_processor=_a						)

     processor.save_pretrained(self.tmpdirname						)
     _lowerCAmelCase			=					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 							_snake_case      (		self						)			->							Dict:
     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(
         tokenizer=self.get_tokenizer()	,   image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

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

     _lowerCAmelCase			=					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 							_snake_case      (		self						)			->							str:
     _lowerCAmelCase			=					self.get_image_processor()
     _lowerCAmelCase			=					self.get_tokenizer()

     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(tokenizer=_a	,   image_processor=_a						)

     _lowerCAmelCase			=					self.prepare_image_inputs()

     _lowerCAmelCase			=					image_processor(_a	,   return_tensors="np"						)
     _lowerCAmelCase			=					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 							_snake_case      (		self						)			->							Any:
     _lowerCAmelCase			=					self.get_image_processor()
     _lowerCAmelCase			=					self.get_tokenizer()

     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(tokenizer=_a	,   image_processor=_a						)

     _lowerCAmelCase			=					"lower newer"

     _lowerCAmelCase			=					processor(text=_a						)

     _lowerCAmelCase			=					tokenizer(_a						)

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




 def 							_snake_case      (		self						)			->							List[str]:
     _lowerCAmelCase			=					self.get_image_processor()
     _lowerCAmelCase			=					self.get_tokenizer()

     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(tokenizer=_a	,   image_processor=_a						)

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

     _lowerCAmelCase			=					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 							_snake_case      (		self						)			->							Dict:
     _lowerCAmelCase			=					self.get_image_processor()
     _lowerCAmelCase			=					self.get_tokenizer()

     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(tokenizer=_a	,   image_processor=_a						)

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

     _lowerCAmelCase			=					processor.batch_decode(_a						)
     _lowerCAmelCase			=					tokenizer.batch_decode(_a						)

     self.assertListEqual(_a	,   _a						)





 def 							_snake_case      (		self						)			->							Any:
     _lowerCAmelCase			=					self.get_image_processor()
     _lowerCAmelCase			=					self.get_tokenizer()

     _lowerCAmelCase			=					VisionTextDualEncoderProcessor(tokenizer=_a	,   image_processor=_a						)

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

     _lowerCAmelCase			=					processor(text=_a	,   images=_a						)

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








"""simple docstring"""




def     a__ (				snake_case__       , snake_case__ = False				)						->					str:

			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected string as input, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)
			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected boolean as use_pascal parameter, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)

			lowerCamelCase			      =					input_str.split("""_"""				)

			lowerCamelCase			      =					0 if use_pascal else 1

			lowerCamelCase			      =					words[start_index:]

			lowerCamelCase			      =					[word[0].upper() + word[1:] for word in words_to_capitalize]

			lowerCamelCase			      =					"""""" if use_pascal else words[0]

			return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()



