
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"""







     lowerCAmelCase_							   =			ViTImageProcessor if is_vision_available() else None



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return self.image_processor_tester.prepare_image_processor_dict()



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		(3, 32, 128)
       __SCREAMING_SNAKE_CASE      :	int					=		tempfile.mkdtemp()

       # fmt: off
       __SCREAMING_SNAKE_CASE      :	List[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
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		dict(zip(_A     ,						range(len(_A ) ) ) )

       __SCREAMING_SNAKE_CASE      :	int					=		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''' )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		{
           '''do_normalize''': False,
           '''do_resize''': True,
           '''image_processor_type''': '''ViTImageProcessor''',
           '''resample''': 3,
           '''size''': {'''height''': 32, '''width''': 128},
       }
       __SCREAMING_SNAKE_CASE      :	Dict					=		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							:							Optional[Any]     ,						**_A							:							Optional[Any] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							int     ,						**_A							:							List[str] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Union[str, Any] ):







       """simple docstring"""




       shutil.rmtree(self.tmpdirname )



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Dict					=		Image.fromarray(np.moveaxis(_A     ,						0     ,						-1 ) )

       return image_input



     def  UpperCAmelCase__							(				self							:							Dict ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		self.get_tokenizer()
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.get_image_processor()

       __SCREAMING_SNAKE_CASE      :	Tuple					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )
       processor.save_pretrained(self.tmpdirname )
       __SCREAMING_SNAKE_CASE      :	Any					=		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							:							Tuple ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	str					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )
       processor.save_pretrained(self.tmpdirname )

       __SCREAMING_SNAKE_CASE      :	int					=		self.get_tokenizer(bos_token='''(BOS)'''     ,						eos_token='''(EOS)''' )
       __SCREAMING_SNAKE_CASE      :	str					=		self.get_image_processor(do_normalize=_A     ,						padding_value=1.0 )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		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							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.get_image_processor()
       __SCREAMING_SNAKE_CASE      :	List[str]					=		self.get_tokenizer()

       __SCREAMING_SNAKE_CASE      :	str					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )

       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE      :	Any					=		image_processor(_A     ,						return_tensors='''np''' )
       __SCREAMING_SNAKE_CASE      :	int					=		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							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.get_image_processor()
       __SCREAMING_SNAKE_CASE      :	int					=		self.get_tokenizer()

       __SCREAMING_SNAKE_CASE      :	Any					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )

       __SCREAMING_SNAKE_CASE      :	str					=		'''test'''

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		processor(text=_A )

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



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		'''test'''
       __SCREAMING_SNAKE_CASE      :	str					=		self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE      :	str					=		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							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	str					=		self.get_image_processor()
       __SCREAMING_SNAKE_CASE      :	Any					=		self.get_tokenizer()

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )

       __SCREAMING_SNAKE_CASE      :	Dict					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

       __SCREAMING_SNAKE_CASE      :	Dict					=		processor.char_decode(_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		tokenizer.batch_decode(_A )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		[seq.replace(''' '''     ,						'''''' ) for seq in decoded_tok]

       self.assertListEqual(_A     ,						_A )



     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		None
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE      :	List[str]					=		processor(text=_A     ,						images=_A )

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




     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Tuple					=		MgpstrProcessor(tokenizer=_A     ,						image_processor=_A )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		torch.randn(1     ,						27     ,						38 )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		torch.randn(1     ,						27     ,						5_0257 )
       __SCREAMING_SNAKE_CASE      :	str					=		torch.randn(1     ,						27     ,						3_0522 )

       __SCREAMING_SNAKE_CASE      :	str					=		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'''



import math
import sys

import cva
import numpy as np







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = math.sqrt(a__		)
     __SCREAMING_SNAKE_CASE      = 1 / (sigma * math.sqrt(2 * math.pi		))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros((kernel_size, kernel_size)		)
     for i in range(0 ,       a__		):
          for j in range(0 ,       a__		):
               __SCREAMING_SNAKE_CASE      = math.sqrt(
                   abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
     return vec_gaussian(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros(img.shape		)
     __SCREAMING_SNAKE_CASE      = get_gauss_kernel(a__ ,       a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = img.shape
     for i in range(kernel_size // 2 ,       size_x - kernel_size // 2		):
          for j in range(kernel_size // 2 ,       size_y - kernel_size // 2		):
               __SCREAMING_SNAKE_CASE      = get_slice(a__ ,       a__ ,       a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = img_s - img_s[kernel_size // 2, kernel_size // 2]
               __SCREAMING_SNAKE_CASE      = vec_gaussian(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.sum(a__		) / np.sum(a__		)
               __SCREAMING_SNAKE_CASE      = val
     return imga







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = args[1] if args[1:] else """../image_data/lena.jpg"""
     __SCREAMING_SNAKE_CASE      = float(args[2]		) if args[2:] else 1.0
     __SCREAMING_SNAKE_CASE      = float(args[3]		) if args[3:] else 1.0
     if args[4:]:
          __SCREAMING_SNAKE_CASE      = int(args[4]		)
          __SCREAMING_SNAKE_CASE      = kernel_size + abs(kernel_size % 2 - 1		)
     else:
          __SCREAMING_SNAKE_CASE      = 5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase					:			Any       =	parse_args(sys.argv)
  UpperCAmelCase					:			Union[str, Any]       =	cva.imread(filename, 0)
  cva.imshow('input image', img)

  UpperCAmelCase					:			Optional[int]       =	img / 2_5_5
  UpperCAmelCase					:			Optional[Any]       =	out.astype('float32')
  UpperCAmelCase					:			Tuple       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  UpperCAmelCase					:			List[Any]       =	out * 2_5_5
  UpperCAmelCase					:			Any       =	np.uinta(out)
  cva.imshow('output image', out)
  cva.waitKey(0)
  cva.destroyAllWindows()






