





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				):

   '''simple docstring'''


   a						:							int   		=		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							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      (3, 32, 128)
       UpperCamelCase__							:				Optional[int]          =      tempfile.mkdtemp()

       # fmt: off
       UpperCamelCase__							:				int          =      ['''[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__							:				Optional[Any]          =      dict(zip(__magic_name__,		range(len(__magic_name__							)							)							)							)

       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(__magic_name__							) + '''\n'''							)

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




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Tuple:






       """simple docstring"""
       return MgpstrTokenizer.from_pretrained(self.tmpdirname,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		**__magic_name__							) ->      Any:






       """simple docstring"""
       return ViTImageProcessor.from_pretrained(self.tmpdirname,		**__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       shutil.rmtree(self.tmpdirname							)




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta							)

       UpperCamelCase__							:				Any          =      Image.fromarray(np.moveaxis(__magic_name__,		0,		-1							)							)

       return image_input




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.get_tokenizer()
       UpperCamelCase__							:				int          =      self.get_image_processor()

       UpperCamelCase__							:				str          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)
       processor.save_pretrained(self.tmpdirname							)
       UpperCamelCase__							:				int          =      MgpstrProcessor.from_pretrained(self.tmpdirname,		use_fast=__magic_name__							)

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

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




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






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.get_tokenizer()
       UpperCamelCase__							:				Union[str, Any]          =      self.get_image_processor()

       UpperCamelCase__							:				str          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)
       processor.save_pretrained(self.tmpdirname							)

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

       UpperCamelCase__							:				List[str]          =      MgpstrProcessor.from_pretrained(
           self.tmpdirname,		bos_token='''(BOS)''',		eos_token='''(EOS)''',		do_normalize=__magic_name__,		padding_value=1.0							)

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

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




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      self.get_image_processor()
       UpperCamelCase__							:				Any          =      self.get_tokenizer()

       UpperCamelCase__							:				Union[str, Any]          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				int          =      self.prepare_image_inputs()

       UpperCamelCase__							:				Optional[int]          =      image_processor(__magic_name__,		return_tensors='''np'''							)
       UpperCamelCase__							:				Optional[Any]          =      processor(images=__magic_name__,		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							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self.get_image_processor()
       UpperCamelCase__							:				Optional[Any]          =      self.get_tokenizer()

       UpperCamelCase__							:				Optional[Any]          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				Optional[int]          =      '''test'''

       UpperCamelCase__							:				List[Any]          =      processor(text=__magic_name__							)

       UpperCamelCase__							:				Tuple          =      tokenizer(__magic_name__							)
       for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key],		encoded_processor[key]							)




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.get_image_processor()
       UpperCamelCase__							:				Any          =      self.get_tokenizer()

       UpperCamelCase__							:				Optional[Any]          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				Tuple          =      '''test'''
       UpperCamelCase__							:				Optional[Any]          =      self.prepare_image_inputs()

       UpperCamelCase__							:				Optional[int]          =      processor(text=__magic_name__,		images=__magic_name__							)

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

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




   def       UpperCamelCase__  (    self							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self.get_image_processor()
       UpperCamelCase__							:				List[str]          =      self.get_tokenizer()

       UpperCamelCase__							:				str          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				Dict          =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__							:				List[Any]          =      processor.char_decode(__magic_name__							)
       UpperCamelCase__							:				List[str]          =      tokenizer.batch_decode(__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      [seq.replace(''' ''',		''''''							) for seq in decoded_tok]

       self.assertListEqual(__magic_name__,		__magic_name__							)




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






       """simple docstring"""
       UpperCamelCase__							:				Any          =      self.get_image_processor()
       UpperCamelCase__							:				List[str]          =      self.get_tokenizer()

       UpperCamelCase__							:				Union[str, Any]          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

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

       UpperCamelCase__							:				Optional[int]          =      processor(text=__magic_name__,		images=__magic_name__							)

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




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.get_image_processor()
       UpperCamelCase__							:				List[Any]          =      self.get_tokenizer()

       UpperCamelCase__							:				str          =      MgpstrProcessor(tokenizer=__magic_name__,		image_processor=__magic_name__							)

       UpperCamelCase__							:				int          =      torch.randn(1,		27,		38							)
       UpperCamelCase__							:				Optional[Any]          =      torch.randn(1,		27,		50257							)
       UpperCamelCase__							:				Optional[int]          =      torch.randn(1,		27,		30522							)

       UpperCamelCase__							:				Tuple          =      processor.batch_decode([char_input, bpe_input, wp_input]							)

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








from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	list[list[int]]:
    UpperCamelCase__							:				list[list[int]]          =      []
    create_all_state(1		,   __UpperCAmelCase		,   __UpperCAmelCase		,   []		,   __UpperCAmelCase      )
    return result




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			list[list[int]]		,   )      ->	None:
    if level == 0:
        total_list.append(current_list[:]      )
        return

    for i in range(__UpperCAmelCase		,   total_number - level + 2      ):
        current_list.append(__UpperCAmelCase      )
        create_all_state(i + 1		,   __UpperCAmelCase		,   level - 1		,   __UpperCAmelCase		,   __UpperCAmelCase      )
        current_list.pop()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[list[int]]      )      ->	None:
    for i in total_list:
        print(*__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 4
  UpperCAmelCase_     							= 2
  UpperCAmelCase_     							= generate_all_combinations(n, k)
  print_all_state(total_list)

