


"""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 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	snake_case__    :  Optional[Any]    =      ViTImageProcessor if is_vision_available() else None




	@property
	def   UpperCAmelCase_				(			self      :    str       )	->     Dict:
						return self.image_processor_tester.prepare_image_processor_dict()




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     Optional[Any]:
						__SCREAMING_SNAKE_CASE						       =    (3, 3_2, 1_2_8)
						__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(UpperCAmelCase__      ,      range(len(UpperCAmelCase__       )       )       )       )

						__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(UpperCAmelCase__       ) + "\n"       )

						__SCREAMING_SNAKE_CASE						       =    {
						    "do_normalize": False,
						    "do_resize": True,
						    "image_processor_type": "ViTImageProcessor",
						    "resample": 3,
						    "size": {"height": 3_2, "width": 1_2_8},
						}
						__SCREAMING_SNAKE_CASE						       =    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      :    Tuple      ,      **UpperCAmelCase__      :    Union[str, Any]       )	->     str:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      **UpperCAmelCase__      :    List[Any]       )	->     Optional[Any]:
						return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Dict       )	->     str:
						shutil.rmtree(self.tmpdirname       )




	def   UpperCAmelCase_				(			self      :    List[str]       )	->     int:
						__SCREAMING_SNAKE_CASE						       =    np.random.randint(2_5_5      ,      size=(3, 3_0, 4_0_0)      ,      dtype=np.uinta       )

						__SCREAMING_SNAKE_CASE						       =    Image.fromarray(np.moveaxis(UpperCAmelCase__      ,      0      ,      -1       )       )

						return image_input




	def   UpperCAmelCase_				(			self      :    Any       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )
						processor.save_pretrained(self.tmpdirname       )
						__SCREAMING_SNAKE_CASE						       =    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      :    Tuple       )	->     Optional[int]:
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )
						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=UpperCAmelCase__      ,      padding_value=1.0       )

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

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    self.prepare_image_inputs()

						__SCREAMING_SNAKE_CASE						       =    image_processor(UpperCAmelCase__      ,      return_tensors="np"       )
						__SCREAMING_SNAKE_CASE						       =    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      :    Any       )	->     Union[str, Any]:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    "test"

						__SCREAMING_SNAKE_CASE						       =    processor(text=UpperCAmelCase__       )

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




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

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

						__SCREAMING_SNAKE_CASE						       =    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      :    Dict       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

						__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(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    tokenizer.batch_decode(UpperCAmelCase__       )
						__SCREAMING_SNAKE_CASE						       =    [seq.replace(" "      ,      ""       ) for seq in decoded_tok]

						self.assertListEqual(UpperCAmelCase__      ,      UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    None
						__SCREAMING_SNAKE_CASE						       =    self.prepare_image_inputs()

						__SCREAMING_SNAKE_CASE						       =    processor(text=UpperCAmelCase__      ,      images=UpperCAmelCase__       )

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




	def   UpperCAmelCase_				(			self      :    Optional[Any]       )	->     List[str]:
						__SCREAMING_SNAKE_CASE						       =    self.get_image_processor()
						__SCREAMING_SNAKE_CASE						       =    self.get_tokenizer()

						__SCREAMING_SNAKE_CASE						       =    MgpstrProcessor(tokenizer=UpperCAmelCase__      ,      image_processor=UpperCAmelCase__       )

						__SCREAMING_SNAKE_CASE						       =    torch.randn(1      ,      2_7      ,      3_8       )
						__SCREAMING_SNAKE_CASE						       =    torch.randn(1      ,      2_7      ,      5_0_2_5_7       )
						__SCREAMING_SNAKE_CASE						       =    torch.randn(1      ,      2_7      ,      3_0_5_2_2       )

						__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"]       )




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return " ".join(
					    "".join(word[::-1]       ) if len(lowerCAmelCase_       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words('''Hey wollef sroirraw'''))
