






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{'vocab_file': 'spiece.model'}

__UpperCAmelCase								=						{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

__UpperCAmelCase								=						{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
__UpperCAmelCase								=						0
__UpperCAmelCase								=						1
__UpperCAmelCase								=						2
__UpperCAmelCase								=						3
__UpperCAmelCase								=						4



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Union[str, Any]       						=  VOCAB_FILES_NAMES
		_snake_case    :    int       						=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case    :    Tuple       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case    :    Union[str, Any]       						=  '''left'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=False				,		_UpperCamelCase=True				,		_UpperCamelCase=False				,		_UpperCamelCase="<s>"				,		_UpperCamelCase="</s>"				,		_UpperCamelCase="<unk>"				,		_UpperCamelCase="<sep>"				,		_UpperCamelCase="<pad>"				,		_UpperCamelCase="<cls>"				,		_UpperCamelCase="<mask>"				,		_UpperCamelCase=["<eop>", "<eod>"]				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							None:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_    :      int            =						AddedToken(_UpperCamelCase				,		lstrip=_UpperCamelCase				,		rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) else mask_token

							UpperCAmelCase_    :      Any            =						{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    do_lower_case=_UpperCamelCase				,		remove_space=_UpperCamelCase				,		keep_accents=_UpperCamelCase				,		bos_token=_UpperCamelCase				,		eos_token=_UpperCamelCase				,		unk_token=_UpperCamelCase				,		sep_token=_UpperCamelCase				,		pad_token=_UpperCamelCase				,		cls_token=_UpperCamelCase				,		mask_token=_UpperCamelCase				,		additional_special_tokens=_UpperCamelCase				,		sp_model_kwargs=self.sp_model_kwargs				,		**_UpperCamelCase				,		)

							UpperCAmelCase_    :      List[str]            =						3

							UpperCAmelCase_    :      int            =						do_lower_case
							UpperCAmelCase_    :      int            =						remove_space
							UpperCAmelCase_    :      Dict            =						keep_accents
							UpperCAmelCase_    :      List[str]            =						vocab_file

							UpperCAmelCase_    :      Any            =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							List[Any]:
							return len(self.sp_model   )



		def 					__UpperCAmelCase			(							self   )	->							Optional[int]:
							UpperCAmelCase_    :      Optional[int]            =						{self.convert_ids_to_tokens(_UpperCamelCase   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab



		def __getstate__(							self   )	->							List[Any]:
							UpperCAmelCase_    :      Tuple            =						self.__dict__.copy()
							UpperCAmelCase_    :      Union[str, Any]            =						None
							return state



		def __setstate__(							self				,		_UpperCamelCase   )	->							Tuple:
							UpperCAmelCase_    :      Tuple            =						d

							# for backward compatibility
							if not hasattr(self				,		'sp_model_kwargs'   ):
												UpperCAmelCase_    :      Union[str, Any]            =						{}

							UpperCAmelCase_    :      Tuple            =						spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(self.vocab_file   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict:
							if self.remove_space:
												UpperCAmelCase_    :      Optional[int]            =						' '.join(inputs.strip().split()   )
							else:
												UpperCAmelCase_    :      Dict            =						inputs
							UpperCAmelCase_    :      str            =						outputs.replace('``'				,		'"'   ).replace('\'\''				,		'"'   )

							if not self.keep_accents:
												UpperCAmelCase_    :      List[str]            =						unicodedata.normalize('NFKD'				,		_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						''.join([c for c in outputs if not unicodedata.combining(_UpperCamelCase   )]   )
							if self.do_lower_case:
												UpperCAmelCase_    :      List[Any]            =						outputs.lower()

							return outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[str]:
							UpperCAmelCase_    :      Union[str, Any]            =						self.preprocess_text(_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						self.sp_model.encode(_UpperCamelCase				,		out_type=_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						[]
							for piece in pieces:
												if len(_UpperCamelCase   ) > 1 and piece[-1] == str(','   ) and piece[-2].isdigit():
																	UpperCAmelCase_    :      Any            =						self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCamelCase				,		''   )   )
																	if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																						if len(cur_pieces[0]   ) == 1:
																											UpperCAmelCase_    :      str            =						cur_pieces[1:]
																						else:
																											UpperCAmelCase_    :      int            =						cur_pieces[0][1:]
																	cur_pieces.append(piece[-1]   )
																	new_pieces.extend(_UpperCamelCase   )
												else:
																	new_pieces.append(_UpperCamelCase   )

							return new_pieces



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							return self.sp_model.PieceToId(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Tuple:
							return self.sp_model.IdToPiece(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[str]:
							UpperCAmelCase_    :      List[str]            =						''.join(_UpperCamelCase   ).replace(_UpperCamelCase				,		' '   ).strip()
							return out_string



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		_UpperCamelCase = True				,		**_UpperCamelCase				,		)	->							str:
							UpperCAmelCase_    :      Optional[int]            =						kwargs.pop('use_source_tokenizer'				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						self.convert_ids_to_tokens(_UpperCamelCase				,		skip_special_tokens=_UpperCamelCase   )

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							UpperCAmelCase_    :      List[Any]            =						[]
							UpperCAmelCase_    :      Any            =						[]
							for token in filtered_tokens:
												if skip_special_tokens and token in self.all_special_ids:
																	continue
												if token in self.added_tokens_encoder:
																	if current_sub_text:
																						sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase   )   )
																						UpperCAmelCase_    :      List[str]            =						[]
																	sub_texts.append(_UpperCamelCase   )
												else:
																	current_sub_text.append(_UpperCamelCase   )
							if current_sub_text:
												sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase   )   )

							# Mimic the behavior of the Rust tokenizer:
							# By default, there are no spaces between special tokens
							UpperCAmelCase_    :      Dict            =						''.join(_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
												UpperCAmelCase_    :      int            =						self.clean_up_tokenization(_UpperCamelCase   )
												return clean_text
							else:
												return text



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							List[int]:
							UpperCAmelCase_    :      List[Any]            =						[self.sep_token_id]
							UpperCAmelCase_    :      Tuple            =						[self.cls_token_id]
							if token_ids_a is None:
												return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = False   )	->							List[int]:

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=_UpperCamelCase				,		token_ids_a=_UpperCamelCase				,		already_has_special_tokens=_UpperCamelCase   )

							if token_ids_a is not None:
												return ([0] * len(_UpperCamelCase   )) + [1] + ([0] * len(_UpperCamelCase   )) + [1, 1]
							return ([0] * len(_UpperCamelCase   )) + [1, 1]



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							List[int]:
							UpperCAmelCase_    :      Any            =						[self.sep_token_id]
							UpperCAmelCase_    :      Any            =						[2]

							if token_ids_a is None:
												return len(token_ids_a + sep   ) * [0] + cls_segment_id
							return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1] + cls_segment_id



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

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.vocab_file   ):
												copyfile(self.vocab_file				,		_UpperCamelCase   )
							elif not os.path.isfile(self.vocab_file   ):
												with open(_UpperCamelCase				,		'wb'   ) as fi:
																	UpperCAmelCase_    :      Optional[Any]            =						self.sp_model.serialized_model_proto()
																	fi.write(_UpperCamelCase   )

							return (out_vocab_file,)








import os

import numpy
import onnx







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     int       	=      a.name
       lowercase							:     Any       	=      b.name

       lowercase							:     Optional[Any]       	=      """"""
       lowercase							:     Dict       	=      """"""

       lowercase							:     int       	=      a == b

       lowercase							:     int       	=      name_a
       lowercase							:     List[str]       	=      name_b

       return res







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       for i, input_name in enumerate(node_proto.input				):
              if input_name == name:
                     node_proto.input.insert(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     node_proto.input.pop(i + 1				)

       if node_proto.op_type == "If":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              _graph_replace_input_with(node_proto.attribute[1].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if node_proto.op_type == "Loop":
              _graph_replace_input_with(node_proto.attribute[0].g					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       for n in graph_proto.node:
              _node_replace_input_with(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Any       	=      list(model.graph.initializer				)
       lowercase							:     Dict       	=      list(model_without_ext.graph.initializer				)
       for i, ref_i in ind_to_replace:
              assert inits_with_data[i].name == inits[i].name
              assert inits_with_data[ref_i].name == inits[ref_i].name
              assert i > ref_i

              lowercase							:     Union[str, Any]       	=      inits[i].name
              lowercase							:     Dict       	=      inits[ref_i].name

              model_without_ext.graph.initializer.remove(inits[i]				)

              # for n in model.graph.node:
              _graph_replace_input_with(model_without_ext.graph					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     Union[str, Any]       	=      os.path.dirname(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      os.path.basename(SCREAMING_SNAKE_CASE__				)

       lowercase							:     str       	=      onnx.load(os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)				)

       lowercase							:     List[str]       	=      list(model.graph.initializer				)

       lowercase							:     Tuple       	=      set()
       lowercase							:     int       	=      {}
       lowercase							:     Optional[Any]       	=      []

       lowercase							:     Dict       	=      0

       for i in range(len(SCREAMING_SNAKE_CASE__				)				):
              if i in dup_set:
                     continue

              for j in range(i + 1					,	len(SCREAMING_SNAKE_CASE__				)				):
                     if j in dup_set:
                            continue
                     if _is_equal_tensor_proto(inits[i]					,	inits[j]				):
                            dup_set.add(SCREAMING_SNAKE_CASE__				)
                            dup_set.add(SCREAMING_SNAKE_CASE__				)

                            lowercase							:     int       	=      inits[j].data_type
                            lowercase							:     Optional[int]       	=      numpy.prod(inits[j].dims				)
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 11:
                                   mem_size *= 8
                            else:
                                   print("""unexpected data type: """					,	SCREAMING_SNAKE_CASE__				)
                            total_reduced_size += mem_size

                            lowercase							:     Tuple       	=      inits[i].name
                            lowercase							:     int       	=      inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(SCREAMING_SNAKE_CASE__				)
                            else:
                                   lowercase							:     List[str]       	=      [name_j]
                            ind_to_replace.append((j, i)				)

       print("""total reduced size: """					,	total_reduced_size / 1_024 / 1_024 / 1_024					,	"""GB"""				)

       lowercase							:     str       	=      sorted(SCREAMING_SNAKE_CASE__				)
       _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      """optimized_""" + model_file_name
       lowercase							:     Dict       	=      os.path.join(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       onnx.save(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return new_model
