





'''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		):
 __lowerCamelCase						:							Union[str, Any]         =					ViTImageProcessor if is_vision_available() else None




 @property
 def 							_snake_case   (	self		)    ->							Optional[int]:
     return self.image_processor_tester.prepare_image_processor_dict()




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=	(3, 32, 128)
     _lowerCAmelCase				=	tempfile.mkdtemp()

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

     _lowerCAmelCase				=	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(_lowerCAmelCase		) + "\n"		)

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




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




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							int:
     return ViTImageProcessor.from_pretrained(self.tmpdirname			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Tuple:
     shutil.rmtree(self.tmpdirname		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=	np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta		)

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

     return image_input




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=	self.get_tokenizer()
     _lowerCAmelCase				=	self.get_image_processor()

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

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

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




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=	self.get_tokenizer()
     _lowerCAmelCase				=	self.get_image_processor()

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

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

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

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

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




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=	self.get_image_processor()
     _lowerCAmelCase				=	self.get_tokenizer()

     _lowerCAmelCase				=	MgpstrProcessor(tokenizer=_lowerCAmelCase			,     image_processor=_lowerCAmelCase		)

     _lowerCAmelCase				=	self.prepare_image_inputs()

     _lowerCAmelCase				=	image_processor(_lowerCAmelCase			,     return_tensors="np"		)
     _lowerCAmelCase				=	processor(images=_lowerCAmelCase			,     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 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=	self.get_image_processor()
     _lowerCAmelCase				=	self.get_tokenizer()

     _lowerCAmelCase				=	MgpstrProcessor(tokenizer=_lowerCAmelCase			,     image_processor=_lowerCAmelCase		)

     _lowerCAmelCase				=	"test"

     _lowerCAmelCase				=	processor(text=_lowerCAmelCase		)

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




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=	self.get_image_processor()
     _lowerCAmelCase				=	self.get_tokenizer()

     _lowerCAmelCase				=	MgpstrProcessor(tokenizer=_lowerCAmelCase			,     image_processor=_lowerCAmelCase		)

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

     _lowerCAmelCase				=	processor(text=_lowerCAmelCase			,     images=_lowerCAmelCase		)

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

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




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

     _lowerCAmelCase				=	MgpstrProcessor(tokenizer=_lowerCAmelCase			,     image_processor=_lowerCAmelCase		)

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

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

     self.assertListEqual(_lowerCAmelCase			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=	self.get_image_processor()
     _lowerCAmelCase				=	self.get_tokenizer()

     _lowerCAmelCase				=	MgpstrProcessor(tokenizer=_lowerCAmelCase			,     image_processor=_lowerCAmelCase		)

     _lowerCAmelCase				=	None
     _lowerCAmelCase				=	self.prepare_image_inputs()

     _lowerCAmelCase				=	processor(text=_lowerCAmelCase			,     images=_lowerCAmelCase		)

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





 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=	self.get_image_processor()
     _lowerCAmelCase				=	self.get_tokenizer()

     _lowerCAmelCase				=	MgpstrProcessor(tokenizer=_lowerCAmelCase			,     image_processor=_lowerCAmelCase		)

     _lowerCAmelCase				=	torch.randn(1			,     27			,     38		)
     _lowerCAmelCase				=	torch.randn(1			,     27			,     50257		)
     _lowerCAmelCase				=	torch.randn(1			,     27			,     30522		)

     _lowerCAmelCase				=	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(SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float		,       SCREAMING_SNAKE_CASE_  :					float    ):



    '''simple docstring'''





    if (voltage, current, resistance).count(0    ) != 1:
        raise ValueError("One and only one argument must be 0"    )
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"    )
    if voltage == 0:
        return {"voltage": float(current * resistance    )}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


