


"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
					import torch


if is_vision_available():
					from PIL import Image

					from transformers import MgpstrProcessor, ViTImageProcessor






@require_torch
@require_vision
class      A(  unittest.TestCase		):

		"""simple docstring"""





		A    =							ViTImageProcessor if is_vision_available() else None
		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return self.image_processor_tester.prepare_image_processor_dict()
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	(3, 32, 1_28)
					_UpperCamelCase					:str					     =	tempfile.mkdtemp()

					# fmt: off
					_UpperCamelCase					:Dict					     =	['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
					# fmt: on
					_UpperCamelCase					:List[str]					     =	dict(zip(SCREAMING_SNAKE_CASE__					,				range(len(SCREAMING_SNAKE_CASE__   )   )   )   )

					_UpperCamelCase					:Dict					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(SCREAMING_SNAKE_CASE__   ) + '''\n'''   )

					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''do_normalize''': False,
					    '''do_resize''': True,
					    '''image_processor_type''': '''ViTImageProcessor''',
					    '''resample''': 3,
					    '''size''': {'''height''': 32, '''width''': 1_28},
					}
					_UpperCamelCase					:Optional[Any]					     =	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   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					return MgpstrTokenizer.from_pretrained(self.tmpdirname					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					return ViTImageProcessor.from_pretrained(self.tmpdirname					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					shutil.rmtree(self.tmpdirname   )
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[Any]					     =	np.random.randint(2_55					,				size=(3, 30, 4_00)					,				dtype=np.uinta   )

					_UpperCamelCase					:Any					     =	Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__					,				0					,				-1   )   )

					return image_input
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.get_tokenizer()
					_UpperCamelCase					:Any					     =	self.get_image_processor()

					_UpperCamelCase					:Optional[Any]					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )
					processor.save_pretrained(self.tmpdirname   )
					_UpperCamelCase					:Optional[int]					     =	MgpstrProcessor.from_pretrained(self.tmpdirname					,				use_fast=SCREAMING_SNAKE_CASE__   )

					self.assertEqual(processor.char_tokenizer.get_vocab()					,				tokenizer.get_vocab()   )
					self.assertIsInstance(processor.char_tokenizer					,				SCREAMING_SNAKE_CASE__   )

					self.assertEqual(processor.image_processor.to_json_string()					,				image_processor.to_json_string()   )
					self.assertIsInstance(processor.image_processor					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.get_tokenizer()
					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor()

					_UpperCamelCase					:Dict					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )
					processor.save_pretrained(self.tmpdirname   )

					_UpperCamelCase					:List[str]					     =	self.get_tokenizer(bos_token='''(BOS)'''					,				eos_token='''(EOS)'''   )
					_UpperCamelCase					:str					     =	self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__					,				padding_value=1.0   )

					_UpperCamelCase					:Tuple					     =	MgpstrProcessor.from_pretrained(
					    self.tmpdirname					,				bos_token='''(BOS)'''					,				eos_token='''(EOS)'''					,				do_normalize=SCREAMING_SNAKE_CASE__					,				padding_value=1.0   )

					self.assertEqual(processor.char_tokenizer.get_vocab()					,				tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.char_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   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.get_image_processor()
					_UpperCamelCase					:int					     =	self.get_tokenizer()

					_UpperCamelCase					:Optional[Any]					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	self.prepare_image_inputs()

					_UpperCamelCase					:int					     =	image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors='''np'''   )
					_UpperCamelCase					:List[Any]					     =	processor(images=SCREAMING_SNAKE_CASE__					,				return_tensors='''np'''   )

					for key in input_image_proc.keys():
								self.assertAlmostEqual(input_image_proc[key].sum()					,				input_processor[key].sum()					,				delta=1E-2   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.get_image_processor()
					_UpperCamelCase					:Union[str, Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:Optional[Any]					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	'''test'''

					_UpperCamelCase					:Union[str, Any]					     =	processor(text=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Tuple					     =	tokenizer(SCREAMING_SNAKE_CASE__   )
					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]					,				encoded_processor[key]   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	self.get_image_processor()
					_UpperCamelCase					:List[Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:Optional[int]					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	'''test'''
					_UpperCamelCase					:Tuple					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Optional[int]					     =	processor(text=SCREAMING_SNAKE_CASE__					,				images=SCREAMING_SNAKE_CASE__   )

					self.assertListEqual(list(inputs.keys()   )					,				['''pixel_values''', '''labels''']   )

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__   ):
								processor()
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.get_image_processor()
					_UpperCamelCase					:Tuple					     =	self.get_tokenizer()

					_UpperCamelCase					:str					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

					_UpperCamelCase					:Any					     =	processor.char_decode(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Union[str, Any]					     =	tokenizer.batch_decode(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	[seq.replace(''' '''					,				''''''   ) for seq in decoded_tok]

					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.get_image_processor()
					_UpperCamelCase					:Any					     =	self.get_tokenizer()

					_UpperCamelCase					:str					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:int					     =	None
					_UpperCamelCase					:Dict					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Dict					     =	processor(text=SCREAMING_SNAKE_CASE__					,				images=SCREAMING_SNAKE_CASE__   )

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





		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.get_image_processor()
					_UpperCamelCase					:Union[str, Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:Optional[int]					     =	MgpstrProcessor(tokenizer=SCREAMING_SNAKE_CASE__					,				image_processor=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Union[str, Any]					     =	torch.randn(1					,				27					,				38   )
					_UpperCamelCase					:str					     =	torch.randn(1					,				27					,				5_02_57   )
					_UpperCamelCase					:List[Any]					     =	torch.randn(1					,				27					,				3_05_22   )

					_UpperCamelCase					:Optional[Any]					     =	processor.batch_decode([char_input, bpe_input, wp_input]   )

					self.assertListEqual(list(results.keys()   )					,				['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']   )





"""simple docstring"""






import os





def 				A_       ( )					->  Any:
			with open(os.path.dirname(snake_case__		) + '''/p022_names.txt'''		) as file:
						_UpperCamelCase					:Optional[Any]					     =	str(file.readlines()[0]		)
						_UpperCamelCase					:Dict					     =	names.replace('''"'''       ,					''''''		).split(''','''		)

			names.sort()

			_UpperCamelCase					:str					     =	0
			_UpperCamelCase					:Union[str, Any]					     =	0

			for i, name in enumerate(snake_case__		):
						for letter in name:
									name_score += ord(snake_case__		) - 64

						total_score += (i + 1) * name_score
						_UpperCamelCase					:List[Any]					     =	0
			return total_score


if __name__ == "__main__":
					print(solution())

