from math import factorial




def    lowerCAmelCase_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


      if n < k or k < 0:
            raise ValueError("Please enter positive integers for n and k where n >= k"							)
      return factorial(__A							) // (factorial(__A							) * factorial(n - k							))


if __name__ == "__main__":
      print(
          'The number of five-card hands possible from a standard',
          f'''fifty-two card deck is: {combinations(5_2, 5)}\n''',
      )

      print(
          'If a class of 40 students must be arranged into groups of',
          f'''4 for group projects, there are {combinations(4_0, 4)} ways''',
          'to arrange them.\n',
      )

      print(
          'If 10 teams are competing in a Formula One race, there',
          f'''are {combinations(1_0, 3)} ways that first, second and''',
          'third place can be awarded.',
      )






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   __lowercase      (unittest.TestCase ):
 _UpperCamelCase			  =  ViTImageProcessor if is_vision_available() else None
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					(3, 32, 128)
       __lowerCAmelCase					:						List[str]					=					tempfile.mkdtemp()

       # fmt: off
       __lowerCAmelCase					:						List[str]					=					['''[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
       __lowerCAmelCase					:						Optional[int]					=					dict(zip(A_      ,							range(len(A_					)					)					)					)

       __lowerCAmelCase					:						Tuple					=					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(A_					) + '''\n'''					)

       __lowerCAmelCase					:						Union[str, Any]					=					{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __lowerCAmelCase					:						Optional[Any]					=					os.path.join(self.tmpdirname      ,							A_					)
       with open(self.image_processor_file      ,							'''w'''      ,							encoding='''utf-8'''					) as fp:
             json.dump(A_      ,							A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							**A_					)					->Tuple:



       '''simple docstring'''





       return ViTImageProcessor.from_pretrained(self.tmpdirname      ,							**A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





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



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)

       __lowerCAmelCase					:						str					=					Image.fromarray(np.moveaxis(A_      ,							0      ,							-1					)					)

       return image_input
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





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

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)
       __lowerCAmelCase					:						Union[str, Any]					=					MgpstrProcessor.from_pretrained(self.tmpdirname      ,							use_fast=A_					)

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

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						List[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)
       processor.save_pretrained(self.tmpdirname					)

       __lowerCAmelCase					:						List[Any]					=					self.get_tokenizer(bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''					)
       __lowerCAmelCase					:						int					=					self.get_image_processor(do_normalize=A_      ,							padding_value=1.0					)

       __lowerCAmelCase					:						int					=					MgpstrProcessor.from_pretrained(
           self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=A_      ,							padding_value=1.0					)

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

       self.assertEqual(processor.image_processor.to_json_string()      ,							image_processor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.image_processor      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

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

       __lowerCAmelCase					:						Optional[Any]					=					image_processor(A_      ,							return_tensors='''np'''					)
       __lowerCAmelCase					:						Tuple					=					processor(images=A_      ,							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					)					->str:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Optional[Any]					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Any					=					'''test'''

       __lowerCAmelCase					:						Dict					=					processor(text=A_					)

       __lowerCAmelCase					:						str					=					tokenizer(A_					)
       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]					)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						str					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

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

       __lowerCAmelCase					:						int					=					processor(text=A_      ,							images=A_					)

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

       # test if it raises when no input is passed
       with pytest.raises(A_					):
             processor()
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

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

       __lowerCAmelCase					:						Optional[int]					=					processor.char_decode(A_					)
       __lowerCAmelCase					:						Tuple					=					tokenizer.batch_decode(A_					)
       __lowerCAmelCase					:						Any					=					[seq.replace(''' '''      ,							''''''					) for seq in decoded_tok]

       self.assertListEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.get_image_processor()
       __lowerCAmelCase					:						Any					=					self.get_tokenizer()

       __lowerCAmelCase					:						int					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

       __lowerCAmelCase					:						Union[str, Any]					=					None
       __lowerCAmelCase					:						Optional[Any]					=					self.prepare_image_inputs()

       __lowerCAmelCase					:						List[Any]					=					processor(text=A_      ,							images=A_					)

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





 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





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

       __lowerCAmelCase					:						Any					=					MgpstrProcessor(tokenizer=A_      ,							image_processor=A_					)

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

       __lowerCAmelCase					:						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''']					)


