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 __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							ViTImageProcessor if is_vision_available() else None




   @property
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (3, 32, 1_28)
     SCREAMING_SNAKE_CASE__    							=   tempfile.mkdtemp()

     # fmt: off
     SCREAMING_SNAKE_CASE__    							=   ["""[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
     SCREAMING_SNAKE_CASE__    							=   dict(zip(_lowercase ,			range(len(_lowercase   )   )   )   )

     SCREAMING_SNAKE_CASE__    							=   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(_lowercase   ) + """\n"""   )

     SCREAMING_SNAKE_CASE__    							=   {
         """do_normalize""": False,
         """do_resize""": True,
         """image_processor_type""": """ViTImageProcessor""",
         """resample""": 3,
         """size""": {"""height""": 32, """width""": 1_28},
     }
     SCREAMING_SNAKE_CASE__    							=   os.path.join(self.tmpdirname ,			_lowercase   )
     with open(self.image_processor_file ,			"""w""" ,			encoding="""utf-8"""   ) as fp:
       json.dump(_lowercase ,			_lowercase   )




   def 			__a		(					self    :       List[str] ,			**_lowercase    :       str   ):



     """simple docstring"""






     return MgpstrTokenizer.from_pretrained(self.tmpdirname ,			**_lowercase   )




   def 			__a		(					self    :       Tuple ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     return ViTImageProcessor.from_pretrained(self.tmpdirname ,			**_lowercase   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname   )




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   np.random.randint(2_55 ,			size=(3, 30, 4_00) ,			dtype=np.uinta   )

     SCREAMING_SNAKE_CASE__    							=   Image.fromarray(np.moveaxis(_lowercase ,			0 ,			-1   )   )

     return image_input




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()
     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )
     processor.save_pretrained(self.tmpdirname   )
     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor.from_pretrained(self.tmpdirname ,			use_fast=_lowercase   )

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

     self.assertEqual(processor.image_processor.to_json_string() ,			image_processor.to_json_string()   )
     self.assertIsInstance(processor.image_processor ,			_lowercase   )




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()
     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )
     processor.save_pretrained(self.tmpdirname   )

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

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor.from_pretrained(
         self.tmpdirname ,			bos_token="""(BOS)""" ,			eos_token="""(EOS)""" ,			do_normalize=_lowercase ,			padding_value=1.0   )

     self.assertEqual(processor.char_tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()   )
     self.assertIsInstance(processor.char_tokenizer ,			_lowercase   )

     self.assertEqual(processor.image_processor.to_json_string() ,			image_processor_add_kwargs.to_json_string()   )
     self.assertIsInstance(processor.image_processor ,			_lowercase   )




   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()
     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__    							=   image_processor(_lowercase ,			return_tensors="""np"""   )
     SCREAMING_SNAKE_CASE__    							=   processor(images=_lowercase ,			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[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()
     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   """test"""

     SCREAMING_SNAKE_CASE__    							=   processor(text=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokenizer(_lowercase   )
     for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key] ,			encoded_processor[key]   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()
     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   """test"""
     SCREAMING_SNAKE_CASE__    							=   self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__    							=   processor(text=_lowercase ,			images=_lowercase   )

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

     # test if it raises when no input is passed
     with pytest.raises(_lowercase   ):
       processor()




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()
     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   processor.char_decode(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tokenizer.batch_decode(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   [seq.replace(""" """ ,			""""""   ) for seq in decoded_tok]

     self.assertListEqual(_lowercase ,			_lowercase   )




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()
     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   None
     SCREAMING_SNAKE_CASE__    							=   self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE__    							=   processor(text=_lowercase ,			images=_lowercase   )

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




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_image_processor()
     SCREAMING_SNAKE_CASE__    							=   self.get_tokenizer()

     SCREAMING_SNAKE_CASE__    							=   MgpstrProcessor(tokenizer=_lowercase ,			image_processor=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.randn(1 ,			27 ,			38   )
     SCREAMING_SNAKE_CASE__    							=   torch.randn(1 ,			27 ,			5_02_57   )
     SCREAMING_SNAKE_CASE__    							=   torch.randn(1 ,			27 ,			3_05_22   )

     SCREAMING_SNAKE_CASE__    							=   processor.batch_decode([char_input, bpe_input, wp_input]   )

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

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [1]

  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   0, 0, 0
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
  SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5

  for _ in range(1				,  __UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   min(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase			)
    ugly_nums.append(__UpperCamelCase			)
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 2
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 3
    if next_num == next_a:
      ia += 1
      SCREAMING_SNAKE_CASE__    							=   ugly_nums[ia] * 5
  return ugly_nums[-1]


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

   testmod(verbose=True)
   print(F"""{ugly_numbers(200) = }""")
