

import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


A_	:			Optional[Any]  	=      get_tests_dir('fixtures/test_sentencepiece.model')
A_	:			List[str]  	=      get_tests_dir('fixtures/test_sentencepiece_bpe.model')

A_	:			Optional[Any]  	=      'pt' if is_torch_available() else 'tf'



@require_sentencepiece
@require_tokenizers
class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	CamembertTokenizer
					UpperCAmelCase__:       Union[str, Any]  							=	CamembertTokenizerFast
					UpperCAmelCase__:       Optional[Any]  							=	True
					UpperCAmelCase__:       Dict  							=	True






					def   __A      (			self      ):
						super().setUp()

						# We have a SentencePiece fixture for testing
						A__							:   Tuple					       =		CamembertTokenizer(A__      )
						tokenizer.save_pretrained(self.tmpdirname      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		"""<pad>"""
						A__							:   Optional[Any]					       =		1

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






					def   __A      (			self      ):
						A__							:   Tuple					       =		list(self.get_tokenizer().get_vocab().keys()      )

						self.assertEqual(vocab_keys[0]   ,	"""<s>NOTUSED"""      )
						self.assertEqual(vocab_keys[1]   ,	"""<pad>"""      )
						self.assertEqual(vocab_keys[-1]   ,	"""<mask>"""      )
						self.assertEqual(len(A__      )   ,	1004      )






					def   __A      (			self      ):
						self.assertEqual(self.get_tokenizer().vocab_size   ,	1005      )






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		CamembertTokenizer(A__      )
						tokenizer.save_pretrained(self.tmpdirname      )
						A__							:   Tuple					       =		CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

						A__							:   Optional[int]					       =		"""I was born in 92000, and this is falsé."""

						A__							:   List[Any]					       =		tokenizer.encode(A__      )
						A__							:   Tuple					       =		rust_tokenizer.encode(A__      )
						self.assertListEqual(A__   ,	A__      )

						A__							:   Optional[Any]					       =		tokenizer.encode(A__   ,	add_special_tokens=A__      )
						A__							:   str					       =		rust_tokenizer.encode(A__   ,	add_special_tokens=A__      )
						self.assertListEqual(A__   ,	A__      )

						# <unk> tokens are not the same for `rust` than for `slow`.
						# Because spm gives back raw token instead of `unk` in EncodeAsPieces
						# tokens = tokenizer.tokenize(sequence)
						A__							:   int					       =		tokenizer.convert_ids_to_tokens(A__      )
						A__							:   Dict					       =		rust_tokenizer.tokenize(A__      )
						self.assertListEqual(A__   ,	A__      )






					def   __A      (			self      ):
						if not self.test_rust_tokenizer:
							return

						A__							:   Optional[Any]					       =		self.get_tokenizer()
						A__							:   List[Any]					       =		self.get_rust_tokenizer()

						A__							:   Union[str, Any]					       =		"""I was born in 92000, and this is falsé."""

						A__							:   List[str]					       =		tokenizer.tokenize(A__      )
						A__							:   Optional[Any]					       =		rust_tokenizer.tokenize(A__      )
						self.assertListEqual(A__   ,	A__      )

						A__							:   List[str]					       =		tokenizer.encode(A__   ,	add_special_tokens=A__      )
						A__							:   Tuple					       =		rust_tokenizer.encode(A__   ,	add_special_tokens=A__      )
						self.assertListEqual(A__   ,	A__      )

						A__							:   Optional[Any]					       =		self.get_rust_tokenizer()
						A__							:   Tuple					       =		tokenizer.encode(A__      )
						A__							:   int					       =		rust_tokenizer.encode(A__      )
						self.assertListEqual(A__   ,	A__      )






					@slow
					def   __A      (			self      ):
						# fmt: off
						A__							:   Optional[int]					       =		{"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
						# fmt: on

						# camembert is a french model. So we also use french texts.
						A__							:   Dict					       =		[
						    """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
						    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
						    """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
						    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
						    """telles que la traduction et la synthèse de texte.""",
						]

						self.tokenizer_integration_test_util(
						    expected_encoding=A__   ,	model_name="""camembert-base"""   ,	revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""   ,	sequences=A__   ,	)






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


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

A_	:			str  	=      {
    '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    _a						:


					'''simple docstring'''






					def __init__(			self   ,	A__=None   ,	**A__      ):
						logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""      )
						A__							:   Dict					       =		model
						A__							:   Any					       =		kwargs.get("""model_save_dir"""   ,	A__      )
						A__							:   Optional[int]					       =		kwargs.get("""latest_model_name"""   ,	A__      )






					def __call__(			self   ,	**A__      ):
						A__							:   int					       =		{k: np.array(A__      ) for k, v in kwargs.items()}
						return self.model.run(A__   ,	A__      )






					@staticmethod
					def   __A      (			A__   ,	A__=None   ,	A__=None      ):
						if provider is None:
							logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""      )
							A__							:   List[Any]					       =		"""CPUExecutionProvider"""

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






					def   __A      (			self   ,	A__   ,	A__ = None   ,	**A__      ):
						A__							:   List[Any]					       =		file_name if file_name is not None else ONNX_WEIGHTS_NAME

						A__							:   List[Any]					       =		self.model_save_dir.joinpath(self.latest_model_name      )
						A__							:   Optional[int]					       =		Path(A__      ).joinpath(A__      )
						try:
							shutil.copyfile(A__   ,	A__      )
						except shutil.SameFileError:
							pass

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






					def   __A      (			self   ,	A__   ,	**A__   ,	):
						if os.path.isfile(A__      ):
							logger.error(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
							return

						os.makedirs(A__   ,	exist_ok=A__      )

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






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






					@classmethod
					def   __A      (			cls   ,	A__   ,	A__ = True   ,	A__ = None   ,	A__ = None   ,	**A__   ,	):
						A__							:   Optional[Any]					       =		None
						if len(str(A__      ).split("""@"""      )      ) == 2:
							A__       ,       A__							:   Union[str, Any]					       =		model_id.split("""@"""      )

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



