





"""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  lowerCamelCase__ (	unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      ViTImageProcessor if is_vision_available() else None






    @property
    def     _UpperCamelCase							(			self ):
          return self.image_processor_tester.prepare_image_processor_dict()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	(3, 32, 128)
          UpperCAmelCase          =	tempfile.mkdtemp()

          # fmt: off
          UpperCAmelCase          =	["""[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          =	dict(zip(A     ,range(len(A ) ) ) )

          UpperCAmelCase          =	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""" )

          UpperCAmelCase          =	{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 32, """width""": 128},
          }
          UpperCAmelCase          =	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 ):
          return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,**A )






    def     _UpperCamelCase							(			self     ,**A ):
          return ViTImageProcessor.from_pretrained(self.tmpdirname     ,**A )






    def     _UpperCamelCase							(			self ):
          shutil.rmtree(self.tmpdirname )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	np.random.randint(255     ,size=(3, 30, 400)     ,dtype=np.uinta )

          UpperCAmelCase          =	Image.fromarray(np.moveaxis(A     ,0     ,-1 ) )

          return image_input






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.get_tokenizer()
          UpperCAmelCase          =	self.get_image_processor()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )
          processor.save_pretrained(self.tmpdirname )
          UpperCAmelCase          =	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 ):
          UpperCAmelCase          =	self.get_tokenizer()
          UpperCAmelCase          =	self.get_image_processor()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )
          processor.save_pretrained(self.tmpdirname )

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

          UpperCAmelCase          =	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 ):
          UpperCAmelCase          =	self.get_image_processor()
          UpperCAmelCase          =	self.get_tokenizer()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )

          UpperCAmelCase          =	self.prepare_image_inputs()

          UpperCAmelCase          =	image_processor(A     ,return_tensors="""np""" )
          UpperCAmelCase          =	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 ):
          UpperCAmelCase          =	self.get_image_processor()
          UpperCAmelCase          =	self.get_tokenizer()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )

          UpperCAmelCase          =	"""test"""

          UpperCAmelCase          =	processor(text=A )

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.get_image_processor()
          UpperCAmelCase          =	self.get_tokenizer()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )

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

          UpperCAmelCase          =	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 ):
          UpperCAmelCase          =	self.get_image_processor()
          UpperCAmelCase          =	self.get_tokenizer()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )

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

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

          self.assertListEqual(A     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.get_image_processor()
          UpperCAmelCase          =	self.get_tokenizer()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )

          UpperCAmelCase          =	None
          UpperCAmelCase          =	self.prepare_image_inputs()

          UpperCAmelCase          =	processor(text=A     ,images=A )

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






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.get_image_processor()
          UpperCAmelCase          =	self.get_tokenizer()

          UpperCAmelCase          =	MgpstrProcessor(tokenizer=A     ,image_processor=A )

          UpperCAmelCase          =	torch.randn(1     ,27     ,38 )
          UpperCAmelCase          =	torch.randn(1     ,27     ,50_257 )
          UpperCAmelCase          =	torch.randn(1     ,27     ,30_522 )

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

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











"""simple docstring"""







from __future__ import annotations

def _a			(		_snake_case				,	_snake_case = None				,	_snake_case = None							):







      """simple docstring"""






      if start is None:
            UpperCAmelCase          =	0

      if end is None:
            UpperCAmelCase          =	len(_snake_case							) - 1

      if start >= end:
            return

      UpperCAmelCase          =	(start + end) // 2

      slowsort(_snake_case				,	_snake_case				,	_snake_case							)
      slowsort(_snake_case				,	mid + 1				,	_snake_case							)

      if sequence[end] < sequence[mid]:
            UpperCAmelCase      , UpperCAmelCase          =	sequence[mid], sequence[end]

      slowsort(_snake_case				,	_snake_case				,	end - 1							)


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

  testmod()



