



import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


UpperCAmelCase :				Tuple							=logging.get_logger(__name__)

UpperCAmelCase :				Optional[int]							={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

UpperCAmelCase :				List[Any]							={
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

UpperCAmelCase :				str							={
    """openai-gpt""": 512,
}



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= text.strip()
					if not text:
										return []
					UpperCamelCase_			= text.split()
					return tokens


class    _lowercase (a_   ):
				'''simple docstring'''







				def __init__(			self  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__=None  ,			snake_case__=True  ,			):

									'''simple docstring'''


									if never_split is None:
														UpperCamelCase_			= []
									UpperCamelCase_			= do_lower_case
									UpperCamelCase_			= set(snake_case__     )
									UpperCamelCase_			= tokenize_chinese_chars
									UpperCamelCase_			= strip_accents
									UpperCamelCase_			= do_split_on_punc

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=None     ):

									'''simple docstring'''


									UpperCamelCase_			= self.never_split.union(set(snake_case__     )     ) if never_split else self.never_split
									UpperCamelCase_			= self._clean_text(snake_case__     )

									# This was added on November 1st, 2018 for the multilingual and Chinese
									# models. This is also applied to the English models now, but it doesn't
									# matter since the English models were not trained on any Chinese data
									# and generally don't have any Chinese data in them (there are Chinese
									# characters in the vocabulary because Wikipedia does have some Chinese
									# words in the English Wikipedia.).
									if self.tokenize_chinese_chars:
														UpperCamelCase_			= self._tokenize_chinese_chars(snake_case__     )
									# prevents treating the same character with different unicode codepoints as different characters
									UpperCamelCase_			= unicodedata.normalize("NFC"  ,			snake_case__     )
									UpperCamelCase_			= whitespace_tokenize(snake_case__     )
									UpperCamelCase_			= []
									for token in orig_tokens:
														if token not in never_split:
																			if self.do_lower_case:
																								UpperCamelCase_			= token.lower()
																								if self.strip_accents is not False:
																													UpperCamelCase_			= self._run_strip_accents(snake_case__     )
																			elif self.strip_accents:
																								UpperCamelCase_			= self._run_strip_accents(snake_case__     )
														split_tokens.extend(self._run_split_on_punc(snake_case__  ,			snake_case__     )     )

									UpperCamelCase_			= whitespace_tokenize(" ".join(snake_case__     )     )
									return output_tokens

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= unicodedata.normalize("NFD"  ,			snake_case__     )
									UpperCamelCase_			= []
									for char in text:
														UpperCamelCase_			= unicodedata.category(snake_case__     )
														if cat == "Mn":
																			continue
														output.append(snake_case__     )
									return "".join(snake_case__     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=None     ):

									'''simple docstring'''


									if not self.do_split_on_punc or (never_split is not None and text in never_split):
														return [text]
									UpperCamelCase_			= list(snake_case__     )
									UpperCamelCase_			= 0
									UpperCamelCase_			= True
									UpperCamelCase_			= []
									while i < len(snake_case__     ):
														UpperCamelCase_			= chars[i]
														if _is_punctuation(snake_case__     ):
																			output.append([char]     )
																			UpperCamelCase_			= True
														else:
																			if start_new_word:
																								output.append([]     )
																			UpperCamelCase_			= False
																			output[-1].append(snake_case__     )
														i += 1

									return ["".join(snake_case__     ) for x in output]

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= []
									for char in text:
														UpperCamelCase_			= ord(snake_case__     )
														if self._is_chinese_char(snake_case__     ):
																			output.append(" "     )
																			output.append(snake_case__     )
																			output.append(" "     )
														else:
																			output.append(snake_case__     )
									return "".join(snake_case__     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									if (
									    (cp >= 0x4e00 and cp <= 0x9fff)
									    or (cp >= 0x3400 and cp <= 0x4dbf)  #
									    or (cp >= 0x20000 and cp <= 0x2a6df)  #
									    or (cp >= 0x2a700 and cp <= 0x2b73f)  #
									    or (cp >= 0x2b740 and cp <= 0x2b81f)  #
									    or (cp >= 0x2b820 and cp <= 0x2ceaf)  #
									    or (cp >= 0xf900 and cp <= 0xfaff)
									    or (cp >= 0x2f800 and cp <= 0x2fa1f)  #
									):  #
														return True

									return False

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= []
									for char in text:
														UpperCamelCase_			= ord(snake_case__     )
														if cp == 0 or cp == 0xfffd or _is_control(snake_case__     ):
																			continue
														if _is_whitespace(snake_case__     ):
																			output.append(" "     )
														else:
																			output.append(snake_case__     )
									return "".join(snake_case__     )




def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= set()
					UpperCamelCase_			= word[0]
					for char in word[1:]:
										pairs.add((prev_char, char))
										UpperCamelCase_			= char
					return pairs



def  _lowerCAmelCase     (_lowerCAmelCase):
					UpperCamelCase_			= text.replace("—" ,						"-")
					UpperCamelCase_			= text.replace("–" ,						"-")
					UpperCamelCase_			= text.replace("―" ,						"-")
					UpperCamelCase_			= text.replace("…" ,						"...")
					UpperCamelCase_			= text.replace("´" ,						"'")
					UpperCamelCase_			= re.sub(r"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)" ,						r" \1 " ,						_lowerCAmelCase)
					UpperCamelCase_			= re.sub(r"\s*\n\s*" ,						" \n " ,						_lowerCAmelCase)
					UpperCamelCase_			= re.sub(r"[^\S\n]+" ,						" " ,						_lowerCAmelCase)
					return text.strip()






class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    VOCAB_FILES_NAMES
				lowercase__     =    PRETRAINED_VOCAB_FILES_MAP
				lowercase__     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__     =    ["""input_ids""", """attention_mask"""]

				def __init__(			self  ,			snake_case__  ,			snake_case__  ,			snake_case__="<unk>"  ,			**snake_case__     ):

									'''simple docstring'''


									super().__init__(unk_token=snake_case__  ,			**snake_case__     )

									try:
														import ftfy
														from spacy.lang.en import English

														UpperCamelCase_			= English()
														UpperCamelCase_			= _nlp.tokenizer
														UpperCamelCase_			= ftfy.fix_text
									except ImportError:
														logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."     )
														UpperCamelCase_			= BasicTokenizer(do_lower_case=snake_case__     )
														UpperCamelCase_			= None

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

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return True

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return len(self.encoder     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return dict(self.encoder  ,			**self.added_tokens_encoder     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= tuple(token[:-1]     ) + (token[-1] + "</w>",)
									if token in self.cache:
														return self.cache[token]
									UpperCamelCase_			= get_pairs(snake_case__     )

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

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

																			if word[i] == first and i < len(snake_case__     ) - 1 and word[i + 1] == second:
																								new_word.append(first + second     )
																								i += 2
																			else:
																								new_word.append(word[i]     )
																								i += 1
														UpperCamelCase_			= tuple(snake_case__     )
														UpperCamelCase_			= new_word
														if len(snake_case__     ) == 1:
																			break
														else:
																			UpperCamelCase_			= get_pairs(snake_case__     )
									UpperCamelCase_			= " ".join(snake_case__     )
									if word == "\n  </w>":
														UpperCamelCase_			= "\n</w>"
									UpperCamelCase_			= word
									return word

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= []
									if self.fix_text is None:
														# Using BERT's BasicTokenizer
														UpperCamelCase_			= self.nlp.tokenize(snake_case__     )
														for token in text:
																			split_tokens.extend(list(self.bpe(snake_case__     ).split(" "     )     )     )
									else:
														# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
														UpperCamelCase_			= self.nlp(text_standardize(self.fix_text(snake_case__     )     )     )
														for token in text:
																			split_tokens.extend(list(self.bpe(token.text.lower()     ).split(" "     )     )     )
									return split_tokens

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									return self.encoder.get(snake_case__  ,			self.encoder.get(self.unk_token     )     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									return self.decoder.get(snake_case__  ,			self.unk_token     )

				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= "".join(snake_case__     ).replace("</w>"  ,			" "     ).strip()
									return out_string

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__ = None     ):

									'''simple docstring'''


									if not os.path.isdir(snake_case__     ):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
														return
									UpperCamelCase_			= os.path.join(
									    snake_case__  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
									UpperCamelCase_			= os.path.join(
									    snake_case__  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

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

									UpperCamelCase_			= 0
									with open(snake_case__  ,			"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 snake_case__     : 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!"     )
																								UpperCamelCase_			= token_index
																			writer.write(" ".join(snake_case__     ) + "\n"     )
																			index += 1

									return vocab_file, merge_file


from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__UpperCAmelCase   						=     datasets.utils.logging.get_logger(__name__)





class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilderConfig	):






       """simple docstring"""


       UpperCAmelCase_				       =None
       UpperCAmelCase_				       =None







class 				UpperCamelCase__		(	folder_based_builder.FolderBasedBuilder	):






       """simple docstring"""


       UpperCAmelCase_				       =datasets.Image()
       UpperCAmelCase_				       ="image"
       UpperCAmelCase_				       =ImageFolderConfig
       UpperCAmelCase_				       =42  # definition at the bottom of the script
       UpperCAmelCase_				       =ImageClassification(image_column="image"       ,    label_column="label"	)







__UpperCAmelCase   						=     [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
__UpperCAmelCase   						=     IMAGE_EXTENSIONS


