






"""simple docstring"""


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
       import torch


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	int						 = "char"
   A_						:	str						 = "bpe"
   A_						:	List[str]						 = "wp"





lowercase_				     =     (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	Any						 = ["image_processor", "char_tokenizer"]
   A_						:	Union[str, Any]						 = "ViTImageProcessor"
   A_						:	int						 = "MgpstrTokenizer"


   def __init__(	self :     List[str],       _lowerCamelCase :     Union[str, Any]=None,       _lowerCamelCase :     str=None,       **_lowerCamelCase :     Tuple  ):

     '''simple docstring'''
     __A									= None
     if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.''',       _lowerCamelCase,       )
       __A									= kwargs.pop('''feature_extractor'''  )

     __A									= image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''  )
     if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''  )

     __A									= tokenizer
     __A									= AutoTokenizer.from_pretrained('''gpt2'''  )
     __A									= AutoTokenizer.from_pretrained('''bert-base-uncased'''  )

     super().__init__(_lowerCamelCase,       _lowerCamelCase  )


   def __call__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=None,       _lowerCamelCase :     List[str]=None,       _lowerCamelCase :     Optional[Any]=None,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     if images is None and text is None:
       raise ValueError('''You need to specify either an `images` or `text` input to process.'''  )

     if images is not None:
       __A									= self.image_processor(_lowerCamelCase,       return_tensors=_lowerCamelCase,       **_lowerCamelCase  )
     if text is not None:
       __A									= self.char_tokenizer(_lowerCamelCase,       return_tensors=_lowerCamelCase,       **_lowerCamelCase  )

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       __A									= encodings['''input_ids''']
       return inputs


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     __A			,    __A			,    __A									= sequences
     __A									= char_preds.size(0  )

     __A			,    __A									= self._decode_helper(_lowerCamelCase,       '''char'''  )
     __A			,    __A									= self._decode_helper(_lowerCamelCase,       '''bpe'''  )
     __A			,    __A									= self._decode_helper(_lowerCamelCase,       '''wp'''  )

     __A									= []
     __A									= []
     for i in range(_lowerCamelCase  ):
       __A									= [char_scores[i], bpe_scores[i], wp_scores[i]]
       __A									= [char_strs[i], bpe_strs[i], wp_strs[i]]
       __A									= scores.index(max(_lowerCamelCase  )  )
       final_strs.append(strs[max_score_index]  )
       final_scores.append(scores[max_score_index]  )

     __A									= {}
     __A									= final_strs
     __A									= final_scores
     __A									= char_strs
     __A									= bpe_strs
     __A									= wp_strs
     return out


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     Dict,       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     if format == DecodeType.CHARACTER:
       __A									= self.char_decode
       __A									= 1
       __A									= '''[s]'''
     elif format == DecodeType.BPE:
       __A									= self.bpe_decode
       __A									= 2
       __A									= '''#'''
     elif format == DecodeType.WORDPIECE:
       __A									= self.wp_decode
       __A									= 1_02
       __A									= '''[SEP]'''
     else:
       raise ValueError(f'Format {format} is not supported.'  )

     __A			,    __A									= [], []
     __A									= pred_logits.size(0  )
     __A									= pred_logits.size(1  )
     __A			,    __A									= pred_logits.topk(1,       dim=-1,       largest=_lowerCamelCase,       sorted=_lowerCamelCase  )
     __A									= preds_index.view(-1,       _lowerCamelCase  )[:, 1:]
     __A									= decoder(_lowerCamelCase  )
     __A			,    __A									= torch.nn.functional.softmax(_lowerCamelCase,       dim=2  ).max(dim=2  )
     __A									= preds_max_prob[:, 1:]

     for index in range(_lowerCamelCase  ):
       __A									= preds_str[index].find(_lowerCamelCase  )
       __A									= preds_str[index][:pred_eos]
       __A									= preds_index[index].cpu().tolist()
       __A									= pred_index.index(_lowerCamelCase  ) if eos_token in pred_index else -1
       __A									= preds_max_prob[index][: pred_eos_index + 1]
       __A									= pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
       dec_strs.append(_lowerCamelCase  )
       conf_scores.append(_lowerCamelCase  )

     return dec_strs, conf_scores


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     __A									= [seq.replace(''' ''',       ''''''  ) for seq in self.char_tokenizer.batch_decode(_lowerCamelCase  )]
     return decode_strs


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     Dict  ):

     '''simple docstring'''
     return self.bpe_tokenizer.batch_decode(_lowerCamelCase  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     int  ):

     '''simple docstring'''
     __A									= [seq.replace(''' ''',       ''''''  ) for seq in self.wp_tokenizer.batch_decode(_lowerCamelCase  )]
     return decode_strs












"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

  # Reading the image and converting it to grayscale.
  __A									= imread(__UpperCamelCase		,       0 )

  # Test for get_neighbors_pixel function() return not None
  __A									= 0
  __A									= 0
  __A									= image[x_coordinate][y_coordinate]

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  __A									= np.zeros((image.shape[0], image.shape[1]) )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,       image.shape[0] ):
    for j in range(0		,       image.shape[1] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




