


"""simple docstring"""







import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase_   :   str									=  get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class       __A				(       _SCREAMING_SNAKE_CASE,    unittest.TestCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  XLNetTokenizer
 __lowerCAmelCase 					=  XLNetTokenizerFast
 __lowerCAmelCase 					=  True
 __lowerCAmelCase 					=  True


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     super().setUp()

     # We have a SentencePiece fixture for testing
     a 		=XLNetTokenizer(__A ,    keep_accents=__A						)
     tokenizer.sanitize_special_tokens()
     tokenizer.save_pretrained(self.tmpdirname						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		='''<s>'''
     a 		=1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A						) ,    __A						)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A						) ,    __A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=list(self.get_tokenizer().get_vocab().keys()						)

     self.assertEqual(vocab_keys[0] ,    '''<unk>'''						)
     self.assertEqual(vocab_keys[1] ,    '''<s>'''						)
     self.assertEqual(vocab_keys[-1] ,    '''<eod>'''						)
     self.assertEqual(len(__A						) ,    1006						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     self.assertEqual(self.get_tokenizer().vocab_size ,    1000						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=XLNetTokenizer(__A ,    keep_accents=__A						)

     a 		=tokenizer.tokenize('''This is a test'''						)
     self.assertListEqual(__A ,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						) ,    [285, 46, 10, 170, 382]						)

     a 		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
     self.assertListEqual(
         __A ,    [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''é''',
             '''.''',
         ] ,    )
     a 		=tokenizer.convert_tokens_to_ids(__A						)
     self.assertListEqual(__A ,    [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						)

     a 		=tokenizer.convert_ids_to_tokens(__A						)
     self.assertListEqual(
         __A ,    [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''<unk>''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''s''',
             '''<unk>''',
             '''.''',
         ] ,    )


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		=XLNetTokenizer(__A ,    do_lower_case=__A						)
     a 		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
     self.assertListEqual(
         __A ,    [
             SPIECE_UNDERLINE + '''''',
             '''i''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''se''',
             '''.''',
         ] ,    )
     self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''						) ,    ['''▁he''', '''ll''', '''o''']						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=XLNetTokenizer(__A ,    do_lower_case=__A						)
     a 		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
     self.assertListEqual(
         __A ,    [
             SPIECE_UNDERLINE + '''I''',
             SPIECE_UNDERLINE + '''was''',
             SPIECE_UNDERLINE + '''b''',
             '''or''',
             '''n''',
             SPIECE_UNDERLINE + '''in''',
             SPIECE_UNDERLINE + '''''',
             '''9''',
             '''2''',
             '''0''',
             '''0''',
             '''0''',
             ''',''',
             SPIECE_UNDERLINE + '''and''',
             SPIECE_UNDERLINE + '''this''',
             SPIECE_UNDERLINE + '''is''',
             SPIECE_UNDERLINE + '''f''',
             '''al''',
             '''se''',
             '''.''',
         ] ,    )


 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     a 		=XLNetTokenizer.from_pretrained('''xlnet-base-cased'''						)

     a 		=tokenizer.encode('''sequence builders''' ,    add_special_tokens=__A						)
     a 		=tokenizer.encode('''multi-sequence build''' ,    add_special_tokens=__A						)

     a 		=tokenizer.build_inputs_with_special_tokens(__A						)
     a 		=tokenizer.build_inputs_with_special_tokens(__A ,    __A						)

     assert encoded_sentence == text + [4, 3]
     assert encoded_pair == text + [4] + text_a + [4, 3]





 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Union[str, Any]:
     # fmt: off
     a 		={'''input_ids''': [[17, 2_1442, 270, 17, 10, 1_4645, 318, 34, 17, 4546, 3145, 787, 13, 7752, 2_2018, 23, 21, 17, 4546, 3145, 787, 13, 3352, 1_4431, 13, 5500, 11, 1176, 580, 13, 1_6819, 4797, 23, 17, 10, 1_7135, 658, 19, 457, 7932, 13, 184, 19, 3154, 1_7135, 6468, 19, 1404, 1_2269, 19, 4229, 5356, 1_6264, 46, 19, 17, 2_0545, 1_0395, 9, 9, 9, 11, 28, 6421, 9531, 2_0729, 17, 10, 353, 1_7022, 11, 21, 6421, 9531, 1_6949, 17, 10, 1_1509, 753, 11, 33, 95, 2421, 7385, 956, 1_4431, 2626, 25, 842, 7385, 4836, 21, 1429, 2272, 9855, 3120, 161, 2_4738, 19, 1_3203, 658, 218, 787, 21, 430, 1_8482, 847, 2637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 2_2178, 27, 1064, 22, 956, 13, 1_1101, 1429, 5854, 2_4313, 1_8953, 40, 422, 2_4366, 68, 1758, 37, 1_0483, 1_4257, 31, 207, 263, 21, 203, 3773, 25, 71, 9735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2049, 3442, 17, 1_3894, 3380, 23, 95, 18, 1_7634, 2288, 9, 4, 3]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=__A ,    model_name='''xlnet-base-cased''' ,    revision='''c841166438c31ec7ca9a106dee7bb312b73ae511''' ,    )







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}



class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase=None				,		**_UpperCamelCase   )	->							Dict:
							logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'   )
							UpperCAmelCase_    :      Any            =						model
							UpperCAmelCase_    :      int            =						kwargs.get('model_save_dir'				,		_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						kwargs.get('latest_model_name'				,		_UpperCamelCase   )



		def __call__(							self				,		**_UpperCamelCase   )	->							str:
							UpperCAmelCase_    :      Optional[int]            =						{k: np.array(_UpperCamelCase   ) for k, v in kwargs.items()}
							return self.model.run(_UpperCamelCase				,		_UpperCamelCase   )



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase=None				,		_UpperCamelCase=None   )	->							List[Any]:
							if provider is None:
												logger.info('No onnxruntime provider specified, using CPUExecutionProvider'   )
												UpperCAmelCase_    :      List[str]            =						'CPUExecutionProvider'

							return ort.InferenceSession(_UpperCamelCase				,		providers=[provider]				,		sess_options=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      Any            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

							UpperCAmelCase_    :      Optional[Any]            =						self.model_save_dir.joinpath(self.latest_model_name   )
							UpperCAmelCase_    :      str            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
							try:
												shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
							except shutil.SameFileError:
												pass

							# copy external weights (for models >2GB)
							UpperCAmelCase_    :      Optional[Any]            =						self.model_save_dir.joinpath(_UpperCamelCase   )
							if src_path.exists():
												UpperCAmelCase_    :      List[Any]            =						Path(_UpperCamelCase   ).joinpath(_UpperCamelCase   )
												try:
																	shutil.copyfile(_UpperCamelCase				,		_UpperCamelCase   )
												except shutil.SameFileError:
																	pass



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		**_UpperCamelCase				,		)	->							List[str]:
							if os.path.isfile(_UpperCamelCase   ):
												logger.error(f"Provided path ({save_directory}) should be a directory, not a file"   )
												return

							os.makedirs(_UpperCamelCase				,		exist_ok=_UpperCamelCase   )

							# saving model weights/files
							self._save_pretrained(_UpperCamelCase				,		**_UpperCamelCase   )



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = False				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							List[str]:
							UpperCAmelCase_    :      List[str]            =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
							# load model from local directory
							if os.path.isdir(_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						OnnxRuntimeModel.load_model(
												    os.path.join(_UpperCamelCase				,		_UpperCamelCase   )				,		provider=_UpperCamelCase				,		sess_options=_UpperCamelCase   )
												UpperCAmelCase_    :      Tuple            =						Path(_UpperCamelCase   )
							# load model from hub
							else:
												# download model
												UpperCAmelCase_    :      List[str]            =						hf_hub_download(
												    repo_id=_UpperCamelCase				,		filename=_UpperCamelCase				,		use_auth_token=_UpperCamelCase				,		revision=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		force_download=_UpperCamelCase				,		)
												UpperCAmelCase_    :      Union[str, Any]            =						Path(_UpperCamelCase   ).parent
												UpperCAmelCase_    :      List[str]            =						Path(_UpperCamelCase   ).name
												UpperCAmelCase_    :      Union[str, Any]            =						OnnxRuntimeModel.load_model(_UpperCamelCase				,		provider=_UpperCamelCase				,		sess_options=_UpperCamelCase   )
							return cls(model=_UpperCamelCase				,		**_UpperCamelCase   )



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						None
							if len(str(_UpperCamelCase   ).split('@'   )   ) == 2:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						model_id.split('@'   )

							return cls._from_pretrained(
							    model_id=_UpperCamelCase				,		revision=_UpperCamelCase				,		cache_dir=_UpperCamelCase				,		force_download=_UpperCamelCase				,		use_auth_token=_UpperCamelCase				,		**_UpperCamelCase				,		)






