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


__lowercase										= get_tests_dir('''fixtures/test_sentencepiece.model''')
__lowercase										= get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')

__lowercase										= '''pt''' if is_torch_available() else '''tf'''


@require_sentencepiece
@require_tokenizers
class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						List[str]      =				CamembertTokenizer
    a__     :						int      =				CamembertTokenizerFast
    a__     :						Optional[int]      =				True
    a__     :						str      =				True







    def       UpperCamelCase__       (     self)	->     Dict:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __UpperCamelCase					:Any        =		CamembertTokenizer(__lowercase)
        tokenizer.save_pretrained(self.tmpdirname)







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[str]        =		'''<pad>'''
        __UpperCamelCase					:Optional[int]        =		1

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







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:List[str]        =		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(__lowercase)			,			1_004)







    def       UpperCamelCase__       (     self)	->     List[str]:
        self.assertEqual(self.get_tokenizer().vocab_size			,			1_005)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:Dict        =		CamembertTokenizer(__lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        __UpperCamelCase					:Tuple        =		CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        __UpperCamelCase					:Tuple        =		'''I was born in 92000, and this is falsé.'''

        __UpperCamelCase					:Optional[Any]        =		tokenizer.encode(__lowercase)
        __UpperCamelCase					:List[str]        =		rust_tokenizer.encode(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		tokenizer.encode(__lowercase			,			add_special_tokens=__lowercase)
        __UpperCamelCase					:str        =		rust_tokenizer.encode(__lowercase			,			add_special_tokens=__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        # <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)
        __UpperCamelCase					:List[Any]        =		tokenizer.convert_ids_to_tokens(__lowercase)
        __UpperCamelCase					:Dict        =		rust_tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     str:
        if not self.test_rust_tokenizer:
            return

        __UpperCamelCase					:List[Any]        =		self.get_tokenizer()
        __UpperCamelCase					:str        =		self.get_rust_tokenizer()

        __UpperCamelCase					:Dict        =		'''I was born in 92000, and this is falsé.'''

        __UpperCamelCase					:Any        =		tokenizer.tokenize(__lowercase)
        __UpperCamelCase					:Tuple        =		rust_tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:int        =		tokenizer.encode(__lowercase			,			add_special_tokens=__lowercase)
        __UpperCamelCase					:List[Any]        =		rust_tokenizer.encode(__lowercase			,			add_special_tokens=__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:List[Any]        =		self.get_rust_tokenizer()
        __UpperCamelCase					:Optional[int]        =		tokenizer.encode(__lowercase)
        __UpperCamelCase					:Any        =		rust_tokenizer.encode(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)







    @slow
    def       UpperCamelCase__       (     self)	->     Tuple:
        # fmt: off
        __UpperCamelCase					:List[str]        =		{'''input_ids''': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
        __UpperCamelCase					:Any        =		[
            '''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=__lowercase			,			model_name='''camembert-base'''			,			revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''			,			sequences=__lowercase			,			)






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
    import torch


__lowercase										= logging.get_logger(__name__)


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase=False			,			__lowercase=False			,			__lowercase=6.0			,			__lowercase=None			,			__lowercase=False			,			__lowercase=False			,			__lowercase=None			,			__lowercase="fp4"			,			__lowercase=False			,			**__lowercase			,			)	->     Tuple:
        __UpperCamelCase					:List[str]        =		load_in_abit
        __UpperCamelCase					:Union[str, Any]        =		load_in_abit
        __UpperCamelCase					:str        =		llm_inta_threshold
        __UpperCamelCase					:List[str]        =		llm_inta_skip_modules
        __UpperCamelCase					:Any        =		llm_inta_enable_fpaa_cpu_offload
        __UpperCamelCase					:List[Any]        =		llm_inta_has_fpaa_weight
        __UpperCamelCase					:str        =		bnb_abit_quant_type
        __UpperCamelCase					:Optional[int]        =		bnb_abit_use_double_quant

        if bnb_abit_compute_dtype is None:
            __UpperCamelCase					:Tuple        =		torch.floataa
        elif isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:Union[str, Any]        =		getattr(__lowercase			,			__lowercase)
        elif isinstance(__lowercase			,			torch.dtype):
            __UpperCamelCase					:int        =		bnb_abit_compute_dtype
        else:
            raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype''')

        self.post_init()







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        if not isinstance(self.llm_inta_threshold			,			__lowercase):
            raise ValueError('''llm_int8_threshold must be a float''')

        if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules			,			__lowercase):
            raise ValueError('''llm_int8_skip_modules must be a list of strings''')
        if not isinstance(self.llm_inta_enable_fpaa_cpu_offload			,			__lowercase):
            raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean''')

        if not isinstance(self.llm_inta_has_fpaa_weight			,			__lowercase):
            raise ValueError('''llm_int8_has_fp16_weight must be a boolean''')

        if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype			,			torch.dtype):
            raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype''')

        if not isinstance(self.bnb_abit_quant_type			,			__lowercase):
            raise ValueError('''bnb_4bit_quant_type must be a string''')

        if not isinstance(self.bnb_abit_use_double_quant			,			__lowercase):
            raise ValueError('''bnb_4bit_use_double_quant must be a boolean''')

        if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes''')) >= version.parse(
            '''0.39.0'''):
            raise ValueError(
                '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version''')







    def       UpperCamelCase__       (     self)	->     Any:
        return self.load_in_abit or self.load_in_abit







    def       UpperCamelCase__       (     self)	->     List[Any]:
        if self.load_in_abit:
            return "llm_int8"
        elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
            return "fp4"
        elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
            return "nf4"
        else:
            return None







    @classmethod
    def       UpperCamelCase__       (     cls			,			__lowercase			,			__lowercase			,			**__lowercase)	->     List[str]:
        __UpperCamelCase					:Optional[int]        =		cls(**__lowercase)

        __UpperCamelCase					:Optional[Any]        =		[]
        for key, value in kwargs.items():
            if hasattr(__lowercase			,			__lowercase):
                setattr(__lowercase			,			__lowercase			,			__lowercase)
                to_remove.append(__lowercase)
        for key in to_remove:
            kwargs.pop(__lowercase			,			__lowercase)

        if return_unused_kwargs:
            return config, kwargs
        else:
            return config







    def       UpperCamelCase__       (     self			,			__lowercase)	->     Union[str, Any]:
        with open(__lowercase			,			'''w'''			,			encoding='''utf-8''') as writer:
            __UpperCamelCase					:Optional[int]        =		self.to_dict()
            __UpperCamelCase					:Optional[int]        =		json.dumps(__lowercase			,			indent=2			,			sort_keys=__lowercase) + '''\n'''

            writer.write(__lowercase)







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:Optional[Any]        =		copy.deepcopy(self.__dict__)
        __UpperCamelCase					:Optional[int]        =		str(output['''bnb_4bit_compute_dtype''']).split('''.''')[1]

        return output







    def __repr__(     self)	->     Dict:
        return f"""{self.__class__.__name__} {self.to_json_string()}"""







    def       UpperCamelCase__       (     self			,			__lowercase = True)	->     str:
        if use_diff is True:
            __UpperCamelCase					:Union[str, Any]        =		self.to_diff_dict()
        else:
            __UpperCamelCase					:Dict        =		self.to_dict()
        return json.dumps(__lowercase			,			indent=2			,			sort_keys=__lowercase) + "\n"







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:Union[str, Any]        =		self.to_dict()

        # get the default config dict
        __UpperCamelCase					:Optional[Any]        =		BitsAndBytesConfig().to_dict()

        __UpperCamelCase					:str        =		{}

        # only serialize values that differ from the default config
        for key, value in config_dict.items():
            if value != default_config_dict[key]:
                __UpperCamelCase					:str        =		value

        return serializable_config_dict





