
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   :					Union[str, Any]    =			logging.get_logger(__name__)

_UpperCAmelCase   :					str    =			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

_UpperCAmelCase   :					List[str]    =			{
    "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   :					Union[str, Any]    =			{
    "openai-gpt": 512,
}


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		UpperCamelCase            =	text.strip()
		if not text:
				return []
		UpperCamelCase            =	text.split()
		return tokens


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):



		def __init__(					self							, A_=True							, A_=None							, A_=True							, A_=None							, A_=True							, )    ->   Optional[int]:
				"""simple docstring"""
				if never_split is None:
						UpperCamelCase            =	[]
				UpperCamelCase            =	do_lower_case
				UpperCamelCase            =	set(A_			)
				UpperCamelCase            =	tokenize_chinese_chars
				UpperCamelCase            =	strip_accents
				UpperCamelCase            =	do_split_on_punc



		def __UpperCamelCase					(					self							, A_							, A_=None			)    ->   Optional[int]:
				"""simple docstring"""
				UpperCamelCase            =	self.never_split.union(set(A_			)			) if never_split else self.never_split
				UpperCamelCase            =	self._clean_text(A_			)

				# 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(A_			)
				# prevents treating the same character with different unicode codepoints as different characters
				UpperCamelCase            =	unicodedata.normalize('NFC'							, A_			)
				UpperCamelCase            =	whitespace_tokenize(A_			)
				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(A_			)
								elif self.strip_accents:
										UpperCamelCase            =	self._run_strip_accents(A_			)
						split_tokens.extend(self._run_split_on_punc(A_							, A_			)			)

				UpperCamelCase            =	whitespace_tokenize(' '.join(A_			)			)
				return output_tokens



		def __UpperCamelCase					(					self							, A_			)    ->   Dict:
				"""simple docstring"""
				UpperCamelCase            =	unicodedata.normalize('NFD'							, A_			)
				UpperCamelCase            =	[]
				for char in text:
						UpperCamelCase            =	unicodedata.category(A_			)
						if cat == "Mn":
								continue
						output.append(A_			)
				return "".join(A_			)



		def __UpperCamelCase					(					self							, A_							, A_=None			)    ->   int:
				"""simple docstring"""
				if not self.do_split_on_punc or (never_split is not None and text in never_split):
						return [text]
				UpperCamelCase            =	list(A_			)
				UpperCamelCase            =	0
				UpperCamelCase            =	True
				UpperCamelCase            =	[]
				while i < len(A_			):
						UpperCamelCase            =	chars[i]
						if _is_punctuation(A_			):
								output.append([char]			)
								UpperCamelCase            =	True
						else:
								if start_new_word:
										output.append([]			)
								UpperCamelCase            =	False
								output[-1].append(A_			)
						i += 1

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



		def __UpperCamelCase					(					self							, A_			)    ->   str:
				"""simple docstring"""
				UpperCamelCase            =	[]
				for char in text:
						UpperCamelCase            =	ord(A_			)
						if self._is_chinese_char(A_			):
								output.append(' '			)
								output.append(A_			)
								output.append(' '			)
						else:
								output.append(A_			)
				return "".join(A_			)



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				# This defines a "chinese character" as anything in the CJK Unicode block:
				#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
				#
				# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
				# despite its name. The modern Korean Hangul alphabet is a different block,
				# as is Japanese Hiragana and Katakana. Those alphabets are used to write
				# space-separated words, so they are not treated specially and handled
				# like the all of the other languages.
				if (
				    (cp >= 0X4_E00 and cp <= 0X9_FFF)
				    or (cp >= 0X3_400 and cp <= 0X4_DBF)  #
				    or (cp >= 0X20_000 and cp <= 0X2A_6DF)  #
				    or (cp >= 0X2A_700 and cp <= 0X2B_73F)  #
				    or (cp >= 0X2B_740 and cp <= 0X2B_81F)  #
				    or (cp >= 0X2B_820 and cp <= 0X2C_EAF)  #
				    or (cp >= 0XF_900 and cp <= 0XF_AFF)
				    or (cp >= 0X2F_800 and cp <= 0X2F_A1F)  #
				):  #
						return True

				return False



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	[]
				for char in text:
						UpperCamelCase            =	ord(A_			)
						if cp == 0 or cp == 0XF_FFD or _is_control(A_			):
								continue
						if _is_whitespace(A_			):
								output.append(' '			)
						else:
								output.append(A_			)
				return "".join(A_			)






def  A			(   lowercase					)  ->    List[str]:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Optional[int]      	=							VOCAB_FILES_NAMES
		__lowercase			:			str      	=							PRETRAINED_VOCAB_FILES_MAP
		__lowercase			:			Any      	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase			:			Optional[Any]      	=							["input_ids", "attention_mask"]



		def __init__(					self							, A_							, A_							, A_="<unk>"							, **A_			)    ->   Tuple:
				"""simple docstring"""
				super().__init__(unk_token=A_							, **A_			)

				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=A_			)
						UpperCamelCase            =	None

				with open(A_							, encoding='utf-8'			) as vocab_handle:
						UpperCamelCase            =	json.load(A_			)
				UpperCamelCase            =	{v: k for k, v in self.encoder.items()}
				with open(A_							, 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(A_							, range(len(A_			)			)			)			)
				UpperCamelCase            =	{}



		@property
		def __UpperCamelCase					(					self			)    ->   Any:
				"""simple docstring"""
				return True



		@property
		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				return len(self.encoder			)



		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				return dict(self.encoder							, **self.added_tokens_encoder			)



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	tuple(token[:-1]			) + (token[-1] + '</w>',)
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase            =	get_pairs(A_			)

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

				while True:
						UpperCamelCase            =	min(A_							, key=lambda A_			: self.bpe_ranks.get(A_							, float('inf'			)			)			)
						if bigram not in self.bpe_ranks:
								break
						UpperCamelCase      ,    UpperCamelCase            =	bigram
						UpperCamelCase            =	[]
						UpperCamelCase            =	0
						while i < len(A_			):
								try:
										UpperCamelCase            =	word.index(A_							, A_			)
								except ValueError:
										new_word.extend(word[i:]			)
										break
								else:
										new_word.extend(word[i:j]			)
										UpperCamelCase            =	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
						UpperCamelCase            =	tuple(A_			)
						UpperCamelCase            =	new_word
						if len(A_			) == 1:
								break
						else:
								UpperCamelCase            =	get_pairs(A_			)
				UpperCamelCase            =	' '.join(A_			)
				if word == "\n  </w>":
						UpperCamelCase            =	'\n</w>'
				UpperCamelCase            =	word
				return word



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				UpperCamelCase            =	[]
				if self.fix_text is None:
						# Using BERT's BasicTokenizer
						UpperCamelCase            =	self.nlp.tokenize(A_			)
						for token in text:
								split_tokens.extend(list(self.bpe(A_			).split(' '			)			)			)
				else:
						# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
						UpperCamelCase            =	self.nlp(text_standardize(self.fix_text(A_			)			)			)
						for token in text:
								split_tokens.extend(list(self.bpe(token.text.lower()			).split(' '			)			)			)
				return split_tokens



		def __UpperCamelCase					(					self							, A_			)    ->   str:
				"""simple docstring"""
				return self.encoder.get(A_							, self.encoder.get(self.unk_token			)			)



		def __UpperCamelCase					(					self							, A_			)    ->   List[Any]:
				"""simple docstring"""
				return self.decoder.get(A_							, self.unk_token			)



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	''.join(A_			).replace('</w>'							, ' '			).strip()
				return out_string



		def __UpperCamelCase					(					self							, A_							, A_ = None			)    ->   Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_			):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
						return
				UpperCamelCase            =	os.path.join(
				    A_							, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
				UpperCamelCase            =	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'			)

				UpperCamelCase            =	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!'			)
										UpperCamelCase            =	token_index
								writer.write(' '.join(A_			) + '\n'			)
								index += 1

				return vocab_file, merge_file


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
