






'''simple docstring'''







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A	:			Any         ='''▁'''

_A	:			str         ={'''vocab_file''': '''spiece.model'''}

_A	:			Any         ={
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

_A	:			Union[str, Any]         ={
    '''google/pegasus-xsum''': 512,
}


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

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES

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




							def __init__(							self:				Any			,							UpperCamelCase__:				int			,							UpperCamelCase__:				Union[str, Any]="<pad>"			,							UpperCamelCase__:				Optional[int]="</s>"			,							UpperCamelCase__:				Optional[Any]="<unk>"			,							UpperCamelCase__:				Optional[int]="<mask_2>"			,							UpperCamelCase__:				List[str]="<mask_1>"			,							UpperCamelCase__:				Optional[Any]=None			,							UpperCamelCase__:				Any=103			,							UpperCamelCase__:				Optional[Dict[str, Any]] = None			,							**UpperCamelCase__:				Optional[int]			,							):
														lowerCamelCase__					:	Dict         =       offset
														if additional_special_tokens is not None:
																					if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												raise TypeError(
																												    F'''additional_special_tokens should be of type {type(UpperCamelCase__     )}, but is'''
																												    F''' {type(UpperCamelCase__     )}'''     )

																					lowerCamelCase__					:	Union[str, Any]         =       (
																					    ([mask_token_sent] + additional_special_tokens)
																					    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																					    else additional_special_tokens
																					)
																					# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																					additional_special_tokens_extended += [
																					    F'''<unk_{i}>''' for i in range(len(UpperCamelCase__     )			,							self.offset - 1     )
																					]

																					if len(set(UpperCamelCase__     )     ) != len(UpperCamelCase__     ):
																												raise ValueError(
																												    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																												    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''     )
																					lowerCamelCase__					:	Tuple         =       additional_special_tokens_extended
														else:
																					lowerCamelCase__					:	List[Any]         =       [mask_token_sent] if mask_token_sent is not None else []
																					additional_special_tokens += [F'''<unk_{i}>''' for i in range(2			,							self.offset     )]

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

														super().__init__(
														    eos_token=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							mask_token=UpperCamelCase__			,							pad_token=UpperCamelCase__			,							mask_token_sent=UpperCamelCase__			,							offset=UpperCamelCase__			,							additional_special_tokens=UpperCamelCase__			,							sp_model_kwargs=self.sp_model_kwargs			,							**UpperCamelCase__			,							)
														lowerCamelCase__					:	Optional[int]         =       mask_token_sent
														lowerCamelCase__					:	Optional[Any]         =       vocab_file
														lowerCamelCase__					:	Any         =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
														self.sp_model.Load(UpperCamelCase__     )

														# add special tokens to encoder dict
														lowerCamelCase__					:	Dict[int, str]         =       {
														    0: self.pad_token,
														    1: self.eos_token,
														}

														if self.mask_token_sent is not None:
																					self.encoder.update(
																					    {
																					        2: self.mask_token_sent,
																					        3: self.mask_token,
																					    }     )

														if self.offset > 0:
																					# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																					# mask_token_sent is already added to list -> so start at 1
																					self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,							self.offset - 1     )}     )

														lowerCamelCase__					:	Dict[str, int]         =       {v: k for k, v in self.encoder.items()}




							@property
							def 		lowerCamelCase_				(							self:				Tuple     ):
														return len(self.sp_model     ) + self.offset




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Optional[Any]         =       {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]     ):
														lowerCamelCase__					:	int         =       self.__dict__.copy()
														lowerCamelCase__					:	Tuple         =       None
														return state




							def __setstate__(							self:				Optional[Any]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	int         =       d

														# for backward compatibility
														if not hasattr(self			,							"""sp_model_kwargs"""     ):
																					lowerCamelCase__					:	Dict         =       {}

														lowerCamelCase__					:	Union[str, Any]         =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
														self.sp_model.Load(self.vocab_file     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				str     ):
														return self.sp_model.encode(UpperCamelCase__			,							out_type=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				str     ):
														if token in self.decoder:
																					return self.decoder[token]
														elif token in self.added_tokens_decoder:
																					return self.added_tokens_decoder[token]
														lowerCamelCase__					:	Tuple         =       self.sp_model.piece_to_id(UpperCamelCase__     )
														return sp_id + self.offset




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				int     ):
														if index in self.encoder:
																					return self.encoder[index]
														elif index in self.added_tokens_encoder:
																					return self.added_tokens_encoder[index]
														else:
																					lowerCamelCase__					:	List[str]         =       self.sp_model.IdToPiece(index - self.offset     )
														return token




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	Any         =       []
														lowerCamelCase__					:	str         =       """"""
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(UpperCamelCase__     ) + token
																												lowerCamelCase__					:	List[Any]         =       []
																					else:
																												current_sub_tokens.append(UpperCamelCase__     )
														out_string += self.sp_model.decode(UpperCamelCase__     )
														return out_string.strip()




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				Optional[int]=False     ):
														return 1




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       set(self.all_special_ids     )  # call it once instead of inside list comp
														all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

														return [1 if x in all_special_ids else 0 for x in seq]




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				List			,							UpperCamelCase__:				Optional[List] = None			,							UpperCamelCase__:				bool = False     ):
														if already_has_special_tokens:
																					return self._special_token_mask(UpperCamelCase__     )
														elif token_ids_a is None:
																					return self._special_token_mask(UpperCamelCase__     ) + [1]
														else:
																					return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				str=None     ):
														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[str] = None     ):
														if not os.path.isdir(UpperCamelCase__     ):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
																					return
														lowerCamelCase__					:	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:
																												lowerCamelCase__					:	List[str]         =       self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__     )

														return (out_vocab_file,)








import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       (			unittest.TestCase     ):
			lowerCamelCase_     :     Dict       			=      [
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    str				=  ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
							with NamedTemporaryFile(mode='''w+t'''    ) as vocab_file:
											vocab_file.write('''\n'''.join(__magic_name__    )    )
											vocab_file.flush()
											snake_case_						:    Optional[Any]				=  BertTokenizerFast(vocab_file.name    )

							with TemporaryDirectory() as bert_save_dir:
											snake_case_						:    str				=  BertModel(BertConfig(vocab_size=len(__magic_name__    )    )    )
											model.save_pretrained(__magic_name__    )
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							__magic_name__    )






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Tuple				=  self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )
											snake_case_						:    List[str]				=  quantize(Path(__magic_name__    )    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Any				=  self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )
											snake_case_						:    Any				=  quantize(__magic_name__    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=None					,							**__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															snake_case_						:    List[str]				=  Path(__magic_name__    ).joinpath('''model.onnx'''    )

											# Remove folder if exists
											if path.parent.exists():
															path.parent.rmdir()

											# Export
											convert(__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )

											return path
							except Exception as e:
											self.fail(__magic_name__    )






			@require_torch
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    Optional[Any]				=  BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    int				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''pt'''    )






			@require_tf
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							from transformers import TFBertModel

							snake_case_						:    Any				=  TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    str				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''tf'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Tuple				=  FeatureExtractionPipeline(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  infer_shapes(__magic_name__					,							__magic_name__    )

							# Assert all variables are present
							self.assertEqual(len(__magic_name__    )					,							len(__magic_name__    )    )
							self.assertTrue(all(var_name in shapes for var_name in variable_names    )    )
							self.assertSequenceEqual(variable_names[:3]					,							__magic_name__    )
							self.assertSequenceEqual(variable_names[3:]					,							__magic_name__    )

							# Assert inputs are {0: batch, 1: sequence}
							for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]					,							{0: '''batch''', 1: '''sequence'''}    )

							# Assert outputs are {0: batch, 1: sequence} and {0: batch}
							self.assertDictEqual(shapes['''output_0''']					,							{0: '''batch''', 1: '''sequence'''}    )
							self.assertDictEqual(shapes['''output_1''']					,							{0: '''batch'''}    )






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  ['''input_ids''', '''attention_mask''', '''token_type_ids''']
							snake_case_						:    List[str]				=  {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
							snake_case_			,							snake_case_						:    Tuple				=  ensure_valid_input(FuncContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the same number of args (all are valid)
							self.assertEqual(len(__magic_name__    )					,							3    )

							# Should have exactly the same input names
							self.assertEqual(set(__magic_name__    )					,							set(__magic_name__    )    )

							# Parameter should be reordered according to their respective place in the function:
							# (input_ids, token_type_ids, attention_mask)
							self.assertEqual(__magic_name__					,							(tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])    )

							# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
							snake_case_			,							snake_case_						:    Dict				=  ensure_valid_input(FuncNonContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(__magic_name__    )					,							1    )
							self.assertEqual(len(__magic_name__    )					,							1    )

							# Should have only "input_ids"
							self.assertEqual(inputs_args[0]					,							tokens['''input_ids''']    )
							self.assertEqual(ordered_input_names[0]					,							'''input_ids'''    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''    )					,							'''-test'''    )
							self.assertEqual('''/home/something/my_fake_model-test.onnx'''					,							generated.as_posix()    )
