
import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE							:int							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE							:List[str]							 =				{
    'vocab_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'},
    'merges_file': {'ctrl': 'https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'},
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'ctrl': 256,
}

SCREAMING_SNAKE_CASE							:Optional[Any]							 =				{
    'Pregnancy': 16_8629,
    'Christianity': 7675,
    'Explain': 10_6423,
    'Fitness': 6_3440,
    'Saving': 6_3163,
    'Ask': 2_7171,
    'Ass': 9_5985,
    'Joke': 16_3509,
    'Questions': 4_5622,
    'Thoughts': 4_9605,
    'Retail': 5_2342,
    'Feminism': 16_4338,
    'Writing': 1_1992,
    'Atheism': 19_2263,
    'Netflix': 4_8616,
    'Computing': 3_9639,
    'Opinion': 4_3213,
    'Alone': 4_4967,
    'Funny': 5_8917,
    'Gaming': 4_0358,
    'Human': 4088,
    'India': 1331,
    'Joker': 7_7138,
    'Diet': 3_6206,
    'Legal': 1_1859,
    'Norman': 4939,
    'Tip': 7_2689,
    'Weight': 5_2343,
    'Movies': 4_6273,
    'Running': 2_3425,
    'Science': 2090,
    'Horror': 3_7793,
    'Confession': 6_0572,
    'Finance': 1_2250,
    'Politics': 1_6360,
    'Scary': 19_1985,
    'Support': 1_2654,
    'Technologies': 3_2516,
    'Teenage': 6_6160,
    'Event': 3_2769,
    'Learned': 6_7460,
    'Notion': 18_2770,
    'Wikipedia': 3_7583,
    'Books': 6665,
    'Extract': 7_6050,
    'Confessions': 10_2701,
    'Conspiracy': 7_5932,
    'Links': 6_3674,
    'Narcissus': 15_0425,
    'Relationship': 5_4766,
    'Relationships': 13_4796,
    'Reviews': 4_1671,
    'News': 4256,
    'Translation': 2_6820,
    'multilingual': 12_8406,
}


def 						UpperCAmelCase			(	a_						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= set()
				__A   			= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)						)
								__A   			= char

				__A   			= set(a_						)
				return pairs





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     CONTROL_CODES






	def __init__(       self					: Dict							,A					: List[Any]							,A					: Tuple							,A					: Optional[int]="<unk>"							,**A					: str ):
					super().__init__(unk_token=A							,**A )

					with open(A							,encoding="utf-8" ) as vocab_handle:
									__A   			= json.load(A )
					__A   			= {v: k for k, v in self.encoder.items()}
					with open(A							,encoding="utf-8" ) as merges_handle:
									__A   			= merges_handle.read().split("\n" )[1:-1]
					__A   			= [tuple(merge.split() ) for merge in merges]
					__A   			= dict(zip(A							,range(len(A ) ) ) )
					__A   			= {}






	@property
	def 				UpperCamelCase_  (       self					: int ):
					return len(self.encoder )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					return dict(self.encoder							,**self.added_tokens_encoder )






	def 				UpperCamelCase_  (       self					: Any							,A					: Optional[Any] ):
					if token in self.cache:
									return self.cache[token]
					__A   			= tuple(A )
					__A   			= tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
					__A   			= get_pairs(A )

					if not pairs:
									return token

					while True:
									__A   			= min(A							,key=lambda A : self.bpe_ranks.get(A							,float("inf" ) ) )
									if bigram not in self.bpe_ranks:
													break
									__A			,							__A   			= bigram
									__A   			= []
									__A   			= 0
									while i < len(A ):
													try:
																	__A   			= word.index(A							,A )
													except ValueError:
																	new_word.extend(word[i:] )
																	break
													else:
																	new_word.extend(word[i:j] )
																	__A   			= j

													if word[i] == first and i < len(A ) - 1 and word[i + 1] == second:
																	new_word.append(first + second )
																	i += 2
													else:
																	new_word.append(word[i] )
																	i += 1
									__A   			= tuple(A )
									__A   			= new_word
									if len(A ) == 1:
													break
									else:
													__A   			= get_pairs(A )
					__A   			= "@@ ".join(A )
					__A   			= word[:-4]
					__A   			= word
					return word






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Optional[Any] ):
					__A   			= []

					__A   			= re.findall(R"\S+\n?"							,A )

					for token in words:
									split_tokens.extend(list(self.bpe(A ).split(" " ) ) )
					return split_tokens






	def 				UpperCamelCase_  (       self					: int							,A					: Optional[Any] ):
					return self.encoder.get(A							,self.encoder.get(self.unk_token ) )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					return self.decoder.get(A							,self.unk_token )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: Union[str, Any] ):
					__A   			= " ".join(A ).replace("@@ "							,"" ).strip()
					return out_string







	def 				UpperCamelCase_  (       self					: Dict							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

					with open(A							,"w"							,encoding="utf-8" ) as f:
									f.write(json.dumps(self.encoder							,indent=2							,sort_keys=A							,ensure_ascii=A ) + "\n" )

					__A   			= 0
					with open(A							,"w"							,encoding="utf-8" ) as writer:
									writer.write("#version: 0.2\n" )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,key=lambda A : kv[1] ):
													if index != token_index:
																	logger.warning(
																	    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																	    " Please check that the tokenizer is not corrupted!" )
																	__A   			= token_index
													writer.write(" ".join(A ) + "\n" )
													index += 1

					return vocab_file, merge_file

	# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
	#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
	#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
	#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
	#     return ''.join(tokens_generated_so_far)








"""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()




