





'''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 					SCREAMING_SNAKE_CASE       (unittest.TestCase				):
     lowerCAmelCase						     =			ViTImageProcessor if is_vision_available() else None


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return self.image_processor_tester.prepare_image_processor_dict()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Dict						=			(3, 32, 128)
          __A		:					List[str]						=			tempfile.mkdtemp()

          # fmt: off
          __A		:					Any						=			['[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		:					Tuple						=			dict(zip(_UpperCAmelCase		,      range(len(_UpperCAmelCase))))

          __A		:					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(_UpperCAmelCase) + '\n')

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


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          return MgpstrTokenizer.from_pretrained(self.tmpdirname		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          return ViTImageProcessor.from_pretrained(self.tmpdirname		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			np.random.randint(255		,      size=(3, 30, 400)		,      dtype=np.uinta)

          __A		:					Optional[int]						=			Image.fromarray(np.moveaxis(_UpperCAmelCase		,      0		,      -1))

          return image_input


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					List[Any]						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)
          processor.save_pretrained(self.tmpdirname)
          __A		:					Any						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					int						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)
          processor.save_pretrained(self.tmpdirname)

          __A		:					int						=			self.get_tokenizer(bos_token='(BOS)'		,      eos_token='(EOS)')
          __A		:					Optional[Any]						=			self.get_image_processor(do_normalize=_UpperCAmelCase		,      padding_value=1.0)

          __A		:					Dict						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.get_image_processor()
          __A		:					Any						=			self.get_tokenizer()

          __A		:					Any						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)

          __A		:					int						=			self.prepare_image_inputs()

          __A		:					Tuple						=			image_processor(_UpperCAmelCase		,      return_tensors='np')
          __A		:					Optional[Any]						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					Union[str, Any]						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)

          __A		:					Any						=			'test'

          __A		:					Union[str, Any]						=			processor(text=_UpperCAmelCase)

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.get_image_processor()
          __A		:					Tuple						=			self.get_tokenizer()

          __A		:					List[str]						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)

          __A		:					str						=			'test'
          __A		:					Dict						=			self.prepare_image_inputs()

          __A		:					List[Any]						=			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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.get_image_processor()
          __A		:					int						=			self.get_tokenizer()

          __A		:					Optional[int]						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)

          __A		:					Optional[Any]						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          __A		:					str						=			processor.char_decode(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			tokenizer.batch_decode(_UpperCAmelCase)
          __A		:					Optional[int]						=			[seq.replace(' '		,      '') for seq in decoded_tok]

          self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			self.get_image_processor()
          __A		:					Optional[Any]						=			self.get_tokenizer()

          __A		:					Dict						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)

          __A		:					int						=			None
          __A		:					Any						=			self.prepare_image_inputs()

          __A		:					Union[str, Any]						=			processor(text=_UpperCAmelCase		,      images=_UpperCAmelCase)

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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			self.get_image_processor()
          __A		:					Optional[int]						=			self.get_tokenizer()

          __A		:					Optional[int]						=			MgpstrProcessor(tokenizer=_UpperCAmelCase		,      image_processor=_UpperCAmelCase)

          __A		:					Tuple						=			torch.randn(1		,      27		,      38)
          __A		:					Any						=			torch.randn(1		,      27		,      5_0257)
          __A		:					List[Any]						=			torch.randn(1		,      27		,      3_0522)

          __A		:					List[Any]						=			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 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()