


import json
import os
from typing import List, Optional, Tuple

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


__magic_name__       					=      logging.get_logger(__name__)

__magic_name__       					=      {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__magic_name__       					=      {
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

__magic_name__       					=      {
    '''microsoft/biogpt''': 1_0_2_4,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Dict      =       set()
	lowerCamelCase_			:							Tuple      =       word[0]
	for char in word[1:]:
		pairs.add((prev_char, char))
		lowerCamelCase_			:							Tuple      =       char
	return pairs





class    lowerCAmelCase__				(			__lowerCamelCase       ):






						"""simple docstring"""
						__UpperCAmelCase   :		Dict  					=	VOCAB_FILES_NAMES
						__UpperCAmelCase   :		Tuple  					=	PRETRAINED_VOCAB_FILES_MAP
						__UpperCAmelCase   :		List[str]  					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCAmelCase   :		Dict  					=	['''input_ids''', '''attention_mask''']




						def __init__(   self				,							a_				,							a_				,							a_="<unk>"				,							a_="<s>"				,							a_="</s>"				,							a_="</s>"				,							a_="<pad>"				,							**a_				,							):
							super().__init__(
							    bos_token=a_				,							eos_token=a_				,							sep_token=a_				,							unk_token=a_				,							pad_token=a_				,							**a_				,							)

							try:
								import sacremoses
							except ImportError:
								raise ImportError(
								    "You need to install sacremoses to use BioGptTokenizer. "
								    "See https://pypi.org/project/sacremoses/ for installation."     )

							lowerCamelCase_			:							Optional[int]      =       "en"
							lowerCamelCase_			:							int      =       sacremoses
							# cache of sm.MosesTokenizer instance
							lowerCamelCase_			:							int      =       {}
							lowerCamelCase_			:							Any      =       {}
							with open(a_				,							encoding="utf-8"     ) as vocab_handle:
								lowerCamelCase_			:							List[str]      =       json.load(a_     )
							lowerCamelCase_			:							int      =       {v: k for k, v in self.encoder.items()}
							with open(a_				,							encoding="utf-8"     ) as merges_handle:
								lowerCamelCase_			:							Dict      =       merges_handle.read().split("\n"     )[:-1]
							lowerCamelCase_			:							int      =       [tuple(merge.split()[:2]     ) for merge in merges]
							lowerCamelCase_			:							Dict      =       dict(zip(a_				,							range(len(a_     )     )     )     )
							lowerCamelCase_			:							int      =       {}




						@property
						def 					_UpperCamelCase			(   self     ):
							return len(self.encoder     )




						def 					_UpperCamelCase			(   self     ):
							return dict(self.encoder				,							**self.added_tokens_encoder     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if lang not in self.cache_moses_tokenizer:
								lowerCamelCase_			:							Union[str, Any]      =       self.sm.MosesTokenizer(lang=a_     )
								lowerCamelCase_			:							Union[str, Any]      =       moses_tokenizer
							return self.cache_moses_tokenizer[lang].tokenize(
							    a_				,							aggressive_dash_splits=a_				,							return_str=a_				,							escape=a_     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							if lang not in self.cache_moses_detokenizer:
								lowerCamelCase_			:							List[Any]      =       self.sm.MosesDetokenizer(lang=a_     )
								lowerCamelCase_			:							List[Any]      =       moses_detokenizer
							return self.cache_moses_detokenizer[lang].detokenize(a_     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Optional[Any]      =       tuple(token[:-1]     ) + (token[-1] + "</w>",)
							if token in self.cache:
								return self.cache[token]
							lowerCamelCase_			:							Optional[int]      =       get_pairs(a_     )

							if not pairs:
								return token + "</w>"

							while True:
								lowerCamelCase_			:							List[Any]      =       min(a_				,							key=lambda a_     : self.bpe_ranks.get(a_				,							float("inf"     )     )     )
								if bigram not in self.bpe_ranks:
									break
								lowerCamelCase_   ,lowerCamelCase_			:							int      =       bigram
								lowerCamelCase_			:							Optional[int]      =       []
								lowerCamelCase_			:							Optional[int]      =       0
								while i < len(a_     ):
									try:
										lowerCamelCase_			:							Tuple      =       word.index(a_				,							a_     )
									except ValueError:
										new_word.extend(word[i:]     )
										break
									else:
										new_word.extend(word[i:j]     )
										lowerCamelCase_			:							List[str]      =       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
								lowerCamelCase_			:							int      =       tuple(a_     )
								lowerCamelCase_			:							List[Any]      =       new_word
								if len(a_     ) == 1:
									break
								else:
									lowerCamelCase_			:							Optional[Any]      =       get_pairs(a_     )
							lowerCamelCase_			:							Union[str, Any]      =       " ".join(a_     )
							if word == "\n  </w>":
								lowerCamelCase_			:							Union[str, Any]      =       "\n</w>"
							lowerCamelCase_			:							List[Any]      =       word
							return word




						def 					_UpperCamelCase			(   self				,							a_				,							a_=False     ):
							if bypass_tokenizer:
								lowerCamelCase_			:							Tuple      =       text.split()
							else:
								lowerCamelCase_			:							Tuple      =       self.moses_tokenize(a_				,							self.lang     )

							lowerCamelCase_			:							Union[str, Any]      =       []
							for token in text:
								if token:
									split_tokens.extend(list(self.bpe(a_     ).split(" "     )     )     )

							return split_tokens




						def 					_UpperCamelCase			(   self				,							a_     ):
							return self.encoder.get(a_				,							self.encoder.get(self.unk_token     )     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							return self.decoder.get(a_				,							self.unk_token     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Tuple      =       [t.replace(" "				,							""     ).replace("</w>"				,							" "     ) for t in tokens]
							lowerCamelCase_			:							Any      =       "".join(a_     ).split()
							# detokenize
							lowerCamelCase_			:							Union[str, Any]      =       self.moses_detokenize(a_				,							self.lang     )
							return text




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							if token_ids_a is None:
								return [self.sep_token_id] + token_ids_a
							lowerCamelCase_			:							Tuple      =       [self.sep_token_id]
							return sep + token_ids_a + sep + token_ids_a




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None				,							a_ = False     ):
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=a_				,							token_ids_a=a_				,							already_has_special_tokens=a_     )
							# no bos used in fairseq
							if token_ids_a is not None:
								return [1] + ([0] * len(a_     )) + [1] + ([0] * len(a_     ))
							return [1] + ([0] * len(a_     ))




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							lowerCamelCase_			:							Optional[int]      =       [self.sep_token_id]

							# no bos used in fairseq
							if token_ids_a is None:
								return len(token_ids_a + sep     ) * [0]
							return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




						def 					_UpperCamelCase			(   self				,							a_				,							a_ = None     ):
							if not os.path.isdir(a_     ):
								logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
								return
							lowerCamelCase_			:							Optional[Any]      =       os.path.join(
							    a_				,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCamelCase_			:							Dict      =       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"     )

							lowerCamelCase_			:							Tuple      =       0
							with open(a_				,							"w"				,							encoding="utf-8"     ) as writer:
								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!"     )
										lowerCamelCase_			:							List[str]      =       token_index
									writer.write(" ".join(a_     ) + "\n"     )
									index += 1

							return vocab_file, merge_file




						def __getstate__(   self     ):
							lowerCamelCase_			:							str      =       self.__dict__.copy()
							lowerCamelCase_			:							Tuple      =       None
							return state




						def __setstate__(   self				,							a_     ):
							lowerCamelCase_			:							Any      =       d

							try:
								import sacremoses
							except ImportError:
								raise ImportError(
								    "You need to install sacremoses to use XLMTokenizer. "
								    "See https://pypi.org/project/sacremoses/ for installation."     )

							lowerCamelCase_			:							int      =       sacremoses





import math
import os

import cva
import numpy as np

__magic_name__       					=      2_55.0







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							List[Any]      =       np.mean((original - contrast) ** 2)
	if mse == 0:
		return 100

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(lowerCAmelCase_))







def     __magic_name__  (      ):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       os.path.dirname(os.path.realpath(lowerCAmelCase_))
	# Loading images (original image and compressed image)
	lowerCamelCase_			:							Optional[int]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/original_image.png"))
	lowerCamelCase_			:							List[str]      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/compressed_image.png")      ,					1)

	lowerCamelCase_			:							str      =       cva.imread(os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-base.png"))
	lowerCamelCase_			:							int      =       cva.imread(
	    os.path.join(lowerCAmelCase_      ,					"image_data/PSNR-example-comp-10.jpg")      ,					1)

	# Value expected: 29.73dB
	print("-- First Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")

	# # Value expected: 31.53dB (Wikipedia Example)
	print("\n-- Second Test --")
	print(F"""PSNR value is {peak_signal_to_noise_ratio(lowerCAmelCase_      ,					lowerCAmelCase_)} dB""")


if __name__ == "__main__":
							main()

