




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"""





    lowerCAmelCase         = ViTImageProcessor if is_vision_available() else None





    @property
    def 				a__ ( self    )					->						Tuple:
         return self.image_processor_tester.prepare_image_processor_dict()





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

         # fmt: off
         UpperCAmelCase_     :				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
         UpperCAmelCase_     :				Any  =     dict(zip(_SCREAMING_SNAKE_CASE  ,range(len(_SCREAMING_SNAKE_CASE    )    )    )    )

         UpperCAmelCase_     :				List[str]  =     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_     :				List[str]  =     {
             '''do_normalize''': False,
             '''do_resize''': True,
             '''image_processor_type''': '''ViTImageProcessor''',
             '''resample''': 3,
             '''size''': {'''height''': 32, '''width''': 128},
         }
         UpperCAmelCase_     :				Union[str, 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 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						Any:
         return MgpstrTokenizer.from_pretrained(self.tmpdirname  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,**_SCREAMING_SNAKE_CASE    )					->						str:
         return ViTImageProcessor.from_pretrained(self.tmpdirname  ,**_SCREAMING_SNAKE_CASE    )





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





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

         UpperCAmelCase_     :				Optional[int]  =     Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE  ,0  ,-1    )    )

         return image_input





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     self.get_tokenizer()
         UpperCAmelCase_     :				Tuple  =     self.get_image_processor()

         UpperCAmelCase_     :				Tuple  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )
         processor.save_pretrained(self.tmpdirname    )
         UpperCAmelCase_     :				Union[str, 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 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				int  =     self.get_tokenizer()
         UpperCAmelCase_     :				Optional[Any]  =     self.get_image_processor()

         UpperCAmelCase_     :				str  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )
         processor.save_pretrained(self.tmpdirname    )

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

         UpperCAmelCase_     :				int  =     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 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				Tuple  =     self.get_image_processor()
         UpperCAmelCase_     :				List[str]  =     self.get_tokenizer()

         UpperCAmelCase_     :				List[Any]  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[int]  =     self.prepare_image_inputs()

         UpperCAmelCase_     :				Dict  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''np'''    )
         UpperCAmelCase_     :				Dict  =     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 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				str  =     self.get_image_processor()
         UpperCAmelCase_     :				Dict  =     self.get_tokenizer()

         UpperCAmelCase_     :				Dict  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Union[str, Any]  =     '''test'''

         UpperCAmelCase_     :				Optional[Any]  =     processor(text=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				str  =     tokenizer(_SCREAMING_SNAKE_CASE    )
         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]    )





    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				int  =     self.get_image_processor()
         UpperCAmelCase_     :				List[str]  =     self.get_tokenizer()

         UpperCAmelCase_     :				Optional[int]  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )

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

         UpperCAmelCase_     :				List[str]  =     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 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Optional[Any]  =     self.get_image_processor()
         UpperCAmelCase_     :				List[str]  =     self.get_tokenizer()

         UpperCAmelCase_     :				Dict  =     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_     :				List[str]  =     processor.char_decode(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				str  =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     [seq.replace(''' '''  ,''''''    ) for seq in decoded_tok]

         self.assertListEqual(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_     :				Union[str, Any]  =     self.get_image_processor()
         UpperCAmelCase_     :				Tuple  =     self.get_tokenizer()

         UpperCAmelCase_     :				str  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     None
         UpperCAmelCase_     :				Union[str, Any]  =     self.prepare_image_inputs()

         UpperCAmelCase_     :				Optional[int]  =     processor(text=_SCREAMING_SNAKE_CASE  ,images=_SCREAMING_SNAKE_CASE    )

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





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

         UpperCAmelCase_     :				List[Any]  =     MgpstrProcessor(tokenizer=_SCREAMING_SNAKE_CASE  ,image_processor=_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[str]  =     torch.randn(1  ,27  ,38    )
         UpperCAmelCase_     :				Any  =     torch.randn(1  ,27  ,50_257    )
         UpperCAmelCase_     :				List[Any]  =     torch.randn(1  ,27  ,30_522    )

         UpperCAmelCase_     :				List[str]  =     processor.batch_decode([char_input, bpe_input, wp_input]    )

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





def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for i in range(1					,		1001 ):
          total += i**i
     return str(_lowercase )[-10:]


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