






'''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 	SCREAMING_SNAKE_CASE					(					unittest.TestCase       ):
			snake_case__  =				ViTImageProcessor if is_vision_available() else None






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Dict:
							return self.image_processor_tester.prepare_image_processor_dict()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			int:
							a_		:  List[Any]											=      (3, 32, 128)
							a_		:  Optional[int]											=      tempfile.mkdtemp()

							# fmt: off
							a_		:  Any											=      ['''[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
							a_		:  int											=      dict(zip(__SCREAMING_SNAKE_CASE  ,						range(len(__SCREAMING_SNAKE_CASE					)					)					)					)

							a_		:  Union[str, Any]											=      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'''					)

							a_		:  Any											=      {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:  int											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						**__SCREAMING_SNAKE_CASE      :			Dict					)			->			Optional[Any]:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			Dict					)			->			Optional[int]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname  ,						**__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Any:
							shutil.rmtree(self.tmpdirname					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Dict:
							a_		:  List[str]											=      np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta					)

							a_		:  List[str]											=      Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE  ,						0  ,						-1					)					)

							return image_input






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

							a_		:  List[Any]											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)
							processor.save_pretrained(self.tmpdirname					)
							a_		:  Any											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Dict:
							a_		:  Dict											=      self.get_tokenizer()
							a_		:  Optional[int]											=      self.get_image_processor()

							a_		:  Union[str, Any]											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)
							processor.save_pretrained(self.tmpdirname					)

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

							a_		:  str											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Any:
							a_		:  Optional[int]											=      self.get_image_processor()
							a_		:  Optional[Any]											=      self.get_tokenizer()

							a_		:  str											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)

							a_		:  int											=      self.prepare_image_inputs()

							a_		:  List[Any]											=      image_processor(__SCREAMING_SNAKE_CASE  ,						return_tensors='''np'''					)
							a_		:  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 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Any:
							a_		:  List[str]											=      self.get_image_processor()
							a_		:  str											=      self.get_tokenizer()

							a_		:  Optional[Any]											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      '''test'''

							a_		:  List[str]											=      processor(text=__SCREAMING_SNAKE_CASE					)

							a_		:  int											=      tokenizer(__SCREAMING_SNAKE_CASE					)
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]					)






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

							a_		:  Dict											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)

							a_		:  List[Any]											=      '''test'''
							a_		:  str											=      self.prepare_image_inputs()

							a_		:  Optional[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			str:
							a_		:  Optional[int]											=      self.get_image_processor()
							a_		:  Union[str, Any]											=      self.get_tokenizer()

							a_		:  Dict											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)

							a_		:  List[str]											=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:  Optional[int]											=      processor.char_decode(__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      [seq.replace(''' '''  ,						''''''					) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Optional[int]:
							a_		:  Any											=      self.get_image_processor()
							a_		:  Dict											=      self.get_tokenizer()

							a_		:  List[str]											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)

							a_		:  Any											=      None
							a_		:  Optional[int]											=      self.prepare_image_inputs()

							a_		:  List[Any]											=      processor(text=__SCREAMING_SNAKE_CASE  ,						images=__SCREAMING_SNAKE_CASE					)

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







			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]					)			->			Union[str, Any]:
							a_		:  Dict											=      self.get_image_processor()
							a_		:  int											=      self.get_tokenizer()

							a_		:  List[Any]											=      MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE  ,						image_processor=__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[int]											=      torch.randn(1  ,						27  ,						38					)
							a_		:  Tuple											=      torch.randn(1  ,						27  ,						5_0257					)
							a_		:  List[str]											=      torch.randn(1  ,						27  ,						3_0522					)

							a_		:  Optional[int]											=      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'''







def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):

				def get_matched_characters(__A							:   str						,		__A							:   str      ) -> str:
								a_		:  Union[str, Any]											=      []
								a_		:  int											=      min(len(_stra      )						,		len(_stra      )      ) // 2
								for i, l in enumerate(_stra      ):
												a_		:  Any											=      int(max(0						,		i - limit      )      )
												a_		:  Union[str, Any]											=      int(min(i + limit + 1						,		len(_stra      )      )      )
												if l in _stra[left:right]:
																matched.append(__A      )
																a_		:  Any											=      f'{_stra[0:_stra.index(__A      )]} {_stra[_stra.index(__A      ) + 1:]}'

								return "".join(__A      )

				# matching characters
				a_		:  Optional[Any]											=      get_matched_characters(__A						,		__A      )
				a_		:  int											=      get_matched_characters(__A						,		__A      )
				a_		:  Any											=      len(__A      )

				# transposition
				a_		:  List[Any]											=      (
				    len([(ca, ca) for ca, ca in zip(__A						,		__A      ) if ca != ca]      ) // 2
				)

				if not match_count:
								a_		:  Dict											=      0.0
				else:
								a_		:  Optional[int]											=      (
								    1
								    / 3
								    * (
								        match_count / len(__A      )
								        + match_count / len(__A      )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				a_		:  List[str]											=      0
				for ca, ca in zip(stra[:4]						,		stra[:4]      ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))




