





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__ :			int       	=			logging.get_logger(__name__)

UpperCamelCase__ :			List[str]       	=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

UpperCamelCase__ :			Optional[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__ :			int       	=			{
    """openai-gpt""": 5_12,
}




def  A_(      A ):
	UpperCAmelCase_		    =  text.strip()
	if not text:
		return []
	UpperCAmelCase_		    =  text.split()
	return tokens







class 	_UpperCamelCase	(							A_	):







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	List[str]=True			,       __lowercase     :	str=None			,       __lowercase     :	Dict=True			,       __lowercase     :	Union[str, Any]=None			,       __lowercase     :	int=True			,       ):






						'''simple docstring'''







						if never_split is None:
							UpperCAmelCase_		    =  []
						UpperCAmelCase_		    =  do_lower_case
						UpperCAmelCase_		    =  set(__lowercase		)
						UpperCAmelCase_		    =  tokenize_chinese_chars
						UpperCAmelCase_		    =  strip_accents
						UpperCAmelCase_		    =  do_split_on_punc





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	Union[str, Any]			,       __lowercase     :	Any=None		):






						'''simple docstring'''







						UpperCAmelCase_		    =  self.never_split.union(set(__lowercase		)		) if never_split else self.never_split
						UpperCAmelCase_		    =  self._clean_text(__lowercase		)

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

						UpperCAmelCase_		    =  whitespace_tokenize(""" """.join(__lowercase		)		)
						return output_tokens





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	int		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Dict			,       __lowercase     :	List[str]			,       __lowercase     :	int=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(__lowercase		)
						UpperCAmelCase_		    =  0
						UpperCAmelCase_		    =  True
						UpperCAmelCase_		    =  []
						while i < len(__lowercase		):
							UpperCAmelCase_		    =  chars[i]
							if _is_punctuation(__lowercase		):
								output.append([char]		)
								UpperCAmelCase_		    =  True
							else:
								if start_new_word:
									output.append([]		)
								UpperCAmelCase_		    =  False
								output[-1].append(__lowercase		)
							i += 1

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





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	Optional[int]		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	Dict		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	Optional[Any]		):






						'''simple docstring'''







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




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




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







class 	_UpperCamelCase	(							A_	):







					'''simple docstring'''

					lowerCamelCase  :  Dict           =							VOCAB_FILES_NAMES
					lowerCamelCase  :  Optional[int]           =							PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase  :  List[str]           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase  :  List[str]           =							['input_ids', 'attention_mask']





					def __init__(    self     :	Tuple			,       __lowercase     :	List[Any]			,       __lowercase     :	Optional[Any]			,       __lowercase     :	List[Any]="<unk>"			,       **__lowercase     :	Optional[Any]		):






						'''simple docstring'''







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

						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=__lowercase		)
							UpperCAmelCase_		    =  None

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





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple		):






						'''simple docstring'''







						return True





					@property
					def 							SCREAMING_SNAKE_CASE				(    self     :	int		):






						'''simple docstring'''







						return len(self.encoder		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Any		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	Dict		):






						'''simple docstring'''







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

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

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

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





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	Any		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	Optional[int]		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[str]			,       __lowercase     :	Union[str, Any]		):






						'''simple docstring'''







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





					def 							SCREAMING_SNAKE_CASE				(    self     :	str			,       __lowercase     :	str		):






						'''simple docstring'''







						UpperCAmelCase_		    =  """""".join(__lowercase		).replace("""</w>"""			,       """ """		).strip()
						return out_string





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]			,       __lowercase     :	str			,       __lowercase     :	Optional[str] = None		):






						'''simple docstring'''







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

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

						UpperCAmelCase_		    =  0
						with open(__lowercase			,       """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 __lowercase		: 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(__lowercase		) + """\n"""		)
								index += 1

						return vocab_file, merge_file









from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
				import pyximport

				pyximport.install(setup_args={"""include_dirs""": np.get_include()})
				from . import algos_graphormer  # noqa E402




def  A_(      A					,   A = 512 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""cython"""] )

	if keep_features and "edge_attr" in item.keys():  # edge_attr
		UpperCAmelCase_		    =  np.asarray(item["""edge_attr"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((len(item["""edge_index"""][0] ), 1)					,   dtype=np.intaa )  # same embedding for all

	if keep_features and "node_feat" in item.keys():  # input_nodes
		UpperCAmelCase_		    =  np.asarray(item["""node_feat"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((item["""num_nodes"""], 1)					,   dtype=np.intaa )  # same embedding for all

	UpperCAmelCase_		    =  np.asarray(item["""edge_index"""]					,   dtype=np.intaa )

	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1
	UpperCAmelCase_		    =  item["""num_nodes"""]

	if len(edge_attr.shape ) == 1:
		UpperCAmelCase_		    =  edge_attr[:, None]
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,   dtype=np.intaa )
	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes]					,   dtype=A )
	UpperCAmelCase_		    =  True

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

	UpperCAmelCase_		    =  algos_graphormer.gen_edge_input(A					,   A					,   A )
	UpperCAmelCase_		    =  np.zeros([num_nodes + 1, num_nodes + 1]					,   dtype=np.single )  # with graph token

	# combine
	UpperCAmelCase_		    =  input_nodes + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  attn_bias
	UpperCAmelCase_		    =  attn_edge_type
	UpperCAmelCase_		    =  shortest_path_result.astype(np.intaa ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  np.sum(A					,   axis=1 ).reshape(-1 ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  item["""in_degree"""]  # for undirected graph
	UpperCAmelCase_		    =  input_edges + 1  # we shift all indices by one for padding
	if "labels" not in item:
		UpperCAmelCase_		    =  item["""y"""]

	return item







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __lowercase     :	Union[str, Any]=False		):






						'''simple docstring'''







						if not is_cython_available():
							raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""		)

						UpperCAmelCase_		    =  spatial_pos_max
						UpperCAmelCase_		    =  on_the_fly_processing





					def __call__(    self     :	List[str]			,       __lowercase     :	List[dict]		):






						'''simple docstring'''







						if self.on_the_fly_processing:
							UpperCAmelCase_		    =  [preprocess_item(__lowercase		) for i in features]

						if not isinstance(features[0]			,       __lowercase		):
							UpperCAmelCase_		    =  [vars(__lowercase		) for f in features]
						UpperCAmelCase_		    =  {}

						UpperCAmelCase_		    =  max(len(i["""input_nodes"""]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_nodes"""][0]		)
						UpperCAmelCase_		    =  len(features[0]["""attn_edge_type"""][0][0]		)
						UpperCAmelCase_		    =  max(len(i["""input_edges"""][0][0]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_edges"""][0][0][0]		)
						UpperCAmelCase_		    =  len(__lowercase		)

						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       max_node_num + 1			,       max_node_num + 1			,       dtype=torch.float		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(
						    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)

						for ix, f in enumerate(__lowercase		):
							for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
								UpperCAmelCase_		    =  torch.tensor(f[k]		)

							if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]		) > 0:
								UpperCAmelCase_		    =  float("""-inf"""		)

							UpperCAmelCase_		    =  f["""attn_bias"""]
							UpperCAmelCase_		    =  f[
							    """attn_edge_type"""
							]
							UpperCAmelCase_		    =  f["""spatial_pos"""]
							UpperCAmelCase_		    =  f["""in_degree"""]
							UpperCAmelCase_		    =  f["""input_nodes"""]
							UpperCAmelCase_		    =  f["""input_edges"""]

						UpperCAmelCase_		    =  batch["""in_degree"""]

						UpperCAmelCase_		    =  features[0]["""labels"""]
						if len(__lowercase		) == 1:  # one task
							if isinstance(sample[0]			,       __lowercase		):  # regression
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
							else:  # binary classification
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
						else:  # multi task classification, left to float to keep the NaNs
							UpperCAmelCase_		    =  torch.from_numpy(np.stack([i["""labels"""] for i in features]			,       axis=0		)		)

						return batch


