
"""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 				snake_case    (			unittest.TestCase						):
   a_ :						Any			 =			ViTImageProcessor if is_vision_available() else None







   @property
   def 					UpperCAmelCase__     (      self)					->int:
      return self.image_processor_tester.prepare_image_processor_dict()







   def 					UpperCAmelCase__     (      self)					->str:
      a_							=			(3, 32, 1_28)
      a_							=			tempfile.mkdtemp()

      # fmt: off
      a_							=			['[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_							=			dict(zip(__UpperCAmelCase    ,				range(len(__UpperCAmelCase))))

      a_							=			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(__UpperCAmelCase) + "\n")

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







   def 					UpperCAmelCase__     (      self    ,				**__UpperCAmelCase)					->List[str]:
      return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,				**__UpperCAmelCase)







   def 					UpperCAmelCase__     (      self    ,				**__UpperCAmelCase)					->int:
      return ViTImageProcessor.from_pretrained(self.tmpdirname    ,				**__UpperCAmelCase)







   def 					UpperCAmelCase__     (      self)					->int:
      shutil.rmtree(self.tmpdirname)







   def 					UpperCAmelCase__     (      self)					->Optional[int]:
      a_							=			np.random.randint(2_55    ,				size=(3, 30, 4_00)    ,				dtype=np.uinta)

      a_							=			Image.fromarray(np.moveaxis(__UpperCAmelCase    ,				0    ,				-1))

      return image_input







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

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)
      processor.save_pretrained(self.tmpdirname)
      a_							=			MgpstrProcessor.from_pretrained(self.tmpdirname    ,				use_fast=__UpperCAmelCase)

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

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







   def 					UpperCAmelCase__     (      self)					->Union[str, Any]:
      a_							=			self.get_tokenizer()
      a_							=			self.get_image_processor()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)
      processor.save_pretrained(self.tmpdirname)

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

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

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

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







   def 					UpperCAmelCase__     (      self)					->Optional[Any]:
      a_							=			self.get_image_processor()
      a_							=			self.get_tokenizer()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)

      a_							=			self.prepare_image_inputs()

      a_							=			image_processor(__UpperCAmelCase    ,				return_tensors="np")
      a_							=			processor(images=__UpperCAmelCase    ,				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)					->List[Any]:
      a_							=			self.get_image_processor()
      a_							=			self.get_tokenizer()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)

      a_							=			'test'

      a_							=			processor(text=__UpperCAmelCase)

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







   def 					UpperCAmelCase__     (      self)					->List[Any]:
      a_							=			self.get_image_processor()
      a_							=			self.get_tokenizer()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)

      a_							=			'test'
      a_							=			self.prepare_image_inputs()

      a_							=			processor(text=__UpperCAmelCase    ,				images=__UpperCAmelCase)

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

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







   def 					UpperCAmelCase__     (      self)					->Optional[Any]:
      a_							=			self.get_image_processor()
      a_							=			self.get_tokenizer()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)

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

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

      self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase)







   def 					UpperCAmelCase__     (      self)					->str:
      a_							=			self.get_image_processor()
      a_							=			self.get_tokenizer()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)

      a_							=			None
      a_							=			self.prepare_image_inputs()

      a_							=			processor(text=__UpperCAmelCase    ,				images=__UpperCAmelCase)

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







   def 					UpperCAmelCase__     (      self)					->Tuple:
      a_							=			self.get_image_processor()
      a_							=			self.get_tokenizer()

      a_							=			MgpstrProcessor(tokenizer=__UpperCAmelCase    ,				image_processor=__UpperCAmelCase)

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

      a_							=			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__			(  SCREAMING_SNAKE_CASE_     )				->		list:

			if len(SCREAMING_SNAKE_CASE_     ) <= 1:
						return [tuple(SCREAMING_SNAKE_CASE_     )]

			lowerCAmelCase__      :      Optional[Any]         =		[]

			def generate(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						if k == 1:
									res.append(tuple(arr[:]     )     )
									return

						generate(k - 1       ,							SCREAMING_SNAKE_CASE_     )

						for i in range(k - 1     ):
									if k % 2 == 0:  # k is even
												lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		arr[k - 1], arr[i]
									else:  # k is odd
												lowerCAmelCase__      ,				lowerCAmelCase__      :      Union[str, Any]         =		arr[k - 1], arr[0]
									generate(k - 1       ,							SCREAMING_SNAKE_CASE_     )

			generate(len(SCREAMING_SNAKE_CASE_     )       ,							SCREAMING_SNAKE_CASE_     )
			return res


if __name__ == "__main__":
					lowerCamelCase__          =		input("""Enter numbers separated by a comma:\n""").strip()
					lowerCamelCase__          =		[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))