import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
 if is_torch_available():
  import torch
 if is_tf_available():
  import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


UpperCAmelCase      		=					logging.get_logger(__name__)

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

UpperCAmelCase      		=					{
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

UpperCAmelCase      		=					{
    '''Salesforce/codegen-350M-mono''': 20_48,
}


@lru_cache()
def SCREAMING_SNAKE_CASE					(  ):
       _UpperCamelCase  :							Union[str, Any]	    =	(
           list(range(ord('!'					)		,    ord('~'					) + 1					)					) + list(range(ord('¡'					)		,    ord('¬'					) + 1					)					) + list(range(ord('®'					)		,    ord('ÿ'					) + 1					)					)
       )
       _UpperCamelCase  :							List[Any]	    =	bs[:]
       _UpperCamelCase  :							Optional[Any]	    =	0
       for b in range(2**8					):
              if b not in bs:
                     bs.append(lowercase_					)
                     cs.append(2**8 + n					)
                     n += 1
       _UpperCamelCase  :							int	    =	[chr(lowercase_					) for n in cs]
       return dict(zip(lowercase_		,    lowercase_					)					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[int]					):
       _UpperCamelCase  :							Any	    =	set()
       _UpperCamelCase  :							str	    =	word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)					)
              _UpperCamelCase  :							Optional[int]	    =	char
       return pairs





class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						int					     =			VOCAB_FILES_NAMES
 _lowerCamelCase    :						str					     =			PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase    :						str					     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase    :						Dict					     =			['input_ids', 'attention_mask']

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Tuple							,      lowerCamelCase							:		Union[str, Any]="replace"							,      lowerCamelCase							:		List[str]="<|endoftext|>"							,      lowerCamelCase							:		Union[str, Any]="<|endoftext|>"							,      lowerCamelCase							:		int="<|endoftext|>"							,      lowerCamelCase							:		str=None							,      lowerCamelCase							:		int=False							,      lowerCamelCase							:		Optional[int]=False							,      **lowerCamelCase							:		List[str]							,      )			->				Any:
        _UpperCamelCase  :							List[Any]	    =	AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else bos_token
        _UpperCamelCase  :							Union[str, Any]	    =	AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else eos_token
        _UpperCamelCase  :							str	    =	AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else unk_token
        _UpperCamelCase  :							List[str]	    =	AddedToken(lowerCamelCase							,      lstrip=lowerCamelCase							,      rstrip=lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else pad_token
        super().__init__(
            errors=lowerCamelCase							,      unk_token=lowerCamelCase							,      bos_token=lowerCamelCase							,      eos_token=lowerCamelCase							,      pad_token=lowerCamelCase							,      add_prefix_space=lowerCamelCase							,      add_bos_token=lowerCamelCase							,      **lowerCamelCase							,      )
        _UpperCamelCase  :							str	    =	add_bos_token

        with open(lowerCamelCase							,      encoding='utf-8'	) as vocab_handle:
               _UpperCamelCase  :							Tuple	    =	json.load(lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	{v: k for k, v in self.encoder.items()}
        _UpperCamelCase  :							Any	    =	errors  # how to handle errors in decoding
        _UpperCamelCase  :							int	    =	bytes_to_unicode()
        _UpperCamelCase  :							Union[str, Any]	    =	{v: k for k, v in self.byte_encoder.items()}
        with open(lowerCamelCase							,      encoding='utf-8'	) as merges_handle:
               _UpperCamelCase  :							Optional[Any]	    =	merges_handle.read().split('\n'	)[1:-1]
        _UpperCamelCase  :							Optional[Any]	    =	[tuple(merge.split()	) for merge in bpe_merges]
        _UpperCamelCase  :							List[str]	    =	dict(zip(lowerCamelCase							,      range(len(lowerCamelCase	)	)	)	)
        _UpperCamelCase  :							Tuple	    =	{}
        _UpperCamelCase  :							Union[str, Any]	    =	add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _UpperCamelCase  :							Union[str, Any]	    =	re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)

 @property
 def        __snake_case       (      self							:		Union[str, Any]	)			->				Any:
        return len(self.encoder	)

 def        __snake_case       (      self							:		Dict	)			->				int:
        return dict(self.encoder							,      **self.added_tokens_encoder	)

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		List[str]	)			->				Dict:
        if token in self.cache:
               return self.cache[token]
        _UpperCamelCase  :							List[str]	    =	tuple(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	get_pairs(lowerCamelCase	)

        if not pairs:
               return token

        while True:
               _UpperCamelCase  :							Optional[int]	    =	min(lowerCamelCase							,      key=lambda lowerCamelCase	: self.bpe_ranks.get(lowerCamelCase							,      float('inf'	)	)	)
               if bigram not in self.bpe_ranks:
                      break
               _UpperCamelCase    ,   _UpperCamelCase  :							List[Any]	    =	bigram
               _UpperCamelCase  :							Optional[Any]	    =	[]
               _UpperCamelCase  :							str	    =	0
               while i < len(lowerCamelCase	):
                      try:
                             _UpperCamelCase  :							Optional[int]	    =	word.index(lowerCamelCase							,      lowerCamelCase	)
                      except ValueError:
                             new_word.extend(word[i:]	)
                             break
                      else:
                             new_word.extend(word[i:j]	)
                             _UpperCamelCase  :							Tuple	    =	j

                      if word[i] == first and i < len(lowerCamelCase	) - 1 and word[i + 1] == second:
                             new_word.append(first + second	)
                             i += 2
                      else:
                             new_word.append(word[i]	)
                             i += 1
               _UpperCamelCase  :							int	    =	tuple(lowerCamelCase	)
               _UpperCamelCase  :							List[Any]	    =	new_word
               if len(lowerCamelCase	) == 1:
                      break
               else:
                      _UpperCamelCase  :							Any	    =	get_pairs(lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	' '.join(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	word
        return word

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		Tuple=None	)			->				Dict:
        if self.add_bos_token:
               _UpperCamelCase  :							Dict	    =	[self.bos_token_id]
        else:
               _UpperCamelCase  :							Union[str, Any]	    =	[]

        _UpperCamelCase  :							str	    =	bos_token_ids + token_ids_a

        if token_ids_a is None:
               return output

        return output + bos_token_ids + token_ids_a

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		int	)			->				Optional[Any]:
        _UpperCamelCase  :							List[str]	    =	[]
        for token in re.findall(self.pat							,      lowerCamelCase	):
               _UpperCamelCase  :							Optional[int]	    =	''.join(
                   self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase	).split(' '	)	)
        return bpe_tokens

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Union[str, Any]	)			->				Any:
        return self.encoder.get(lowerCamelCase							,      self.encoder.get(self.unk_token	)	)

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		List[Any]	)			->				Union[str, Any]:
        return self.decoder.get(lowerCamelCase	)

 def        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		Any	)			->				Optional[Any]:
        _UpperCamelCase  :							int	    =	''.join(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'							,      errors=self.errors	)
        return text

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

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

        _UpperCamelCase  :							Optional[Any]	    =	0
        with open(lowerCamelCase							,      '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 lowerCamelCase	: 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  :							str	    =	token_index
                      writer.write(' '.join(lowerCamelCase	) + '\n'	)
                      index += 1

        return vocab_file, merge_file

 def        __snake_case       (      self							:		List[str]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		int=False							,      **lowerCamelCase							:		int	)			->				List[Any]:
        _UpperCamelCase  :							Dict	    =	kwargs.pop('add_prefix_space'							,      self.add_prefix_space	)
        if is_split_into_words or add_prefix_space:
               _UpperCamelCase  :							Any	    =	' ' + text
        return (text, kwargs)

 def        __snake_case       (      self							:		str							,      lowerCamelCase							:		Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		bool = None							,      lowerCamelCase							:		Optional[List[str]] = None							,      **lowerCamelCase							:		List[str]							,      )			->				str:
        _UpperCamelCase  :							Optional[int]	    =	super()._decode(
            token_ids=lowerCamelCase							,      skip_special_tokens=lowerCamelCase							,      clean_up_tokenization_spaces=lowerCamelCase							,      **lowerCamelCase							,      )

        if truncate_before_pattern is not None and len(lowerCamelCase	) > 0:
               _UpperCamelCase  :							Optional[Any]	    =	self.truncate(lowerCamelCase							,      lowerCamelCase	)

        return decoded_text

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Any	)			->				Optional[int]:
        def find_re(lowerCamelCase							:		Any							,      lowerCamelCase							:		List[Any]							,      lowerCamelCase							:		str	):
               _UpperCamelCase  :							Optional[int]	    =	pattern.search(lowerCamelCase							,      lowerCamelCase	)
               return m.start() if m else -1

        _UpperCamelCase  :							Dict	    =	[re.compile(lowerCamelCase							,      re.MULTILINE	) for pattern in truncate_before_pattern]

        _UpperCamelCase  :							Union[str, Any]	    =	list(re.finditer('^print'							,      lowerCamelCase							,      re.MULTILINE	)	)

        if len(lowerCamelCase	) > 1:
               _UpperCamelCase  :							Tuple	    =	completion[: prints[1].start()]

        _UpperCamelCase  :							Union[str, Any]	    =	list(re.finditer('^def'							,      lowerCamelCase							,      re.MULTILINE	)	)

        if len(lowerCamelCase	) > 1:
               _UpperCamelCase  :							List[str]	    =	completion[: defs[1].start()]

        _UpperCamelCase  :							Tuple	    =	0

        _UpperCamelCase  :							List[str]	    =	[
            pos for pos in [find_re(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	) for terminal in terminals] if pos != -1
        ]

        if len(lowerCamelCase	) > 0:
               return completion[: min(lowerCamelCase	)]
        else:
               return completion










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


