



# 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


__lowerCamelCase         =			logging.get_logger(__name__)


@dataclass
class   snake_case_ :




       """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     ,):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		load_in_abit
             UpperCAmelCase_  :	Tuple				      =		load_in_abit
             UpperCAmelCase_  :	Union[str, Any]				      =		llm_inta_threshold
             UpperCAmelCase_  :	Tuple				      =		llm_inta_skip_modules
             UpperCAmelCase_  :	List[str]				      =		llm_inta_enable_fpaa_cpu_offload
             UpperCAmelCase_  :	Dict				      =		llm_inta_has_fpaa_weight
             UpperCAmelCase_  :	Optional[Any]				      =		bnb_abit_quant_type
             UpperCAmelCase_  :	Tuple				      =		bnb_abit_use_double_quant

             if bnb_abit_compute_dtype is None:
                   UpperCAmelCase_  :	Any				      =		torch.floataa
             elif isinstance(lowercase     ,lowercase):
                   UpperCAmelCase_  :	int				      =		getattr(lowercase     ,lowercase)
             elif isinstance(lowercase     ,torch.dtype):
                   UpperCAmelCase_  :	Optional[Any]				      =		bnb_abit_compute_dtype
             else:
                   raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype")

             self.post_init()



       def  A_   (     self):

             """simple docstring"""





             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  A_   (     self):

             """simple docstring"""





             return self.load_in_abit or self.load_in_abit



       def  A_   (     self):

             """simple docstring"""





             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  A_   (     cls     ,lowercase     ,lowercase     ,**lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		cls(**lowercase)

             UpperCAmelCase_  :	Union[str, 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  A_   (     self     ,lowercase):

             """simple docstring"""





             with open(lowercase     ,"w"     ,encoding="utf-8") as writer:
                   UpperCAmelCase_  :	Any				      =		self.to_dict()
                   UpperCAmelCase_  :	Tuple				      =		json.dumps(lowercase     ,indent=2     ,sort_keys=lowercase) + "\n"

                   writer.write(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		copy.deepcopy(self.__dict__)
             UpperCAmelCase_  :	List[str]				      =		str(output["bnb_4bit_compute_dtype"]).split(".")[1]

             return output



       def __repr__(     self):

             """simple docstring"""





             return F"""{self.__class__.__name__} {self.to_json_string()}"""



       def  A_   (     self     ,lowercase = True):

             """simple docstring"""





             if use_diff is True:
                   UpperCAmelCase_  :	Any				      =		self.to_diff_dict()
             else:
                   UpperCAmelCase_  :	Dict				      =		self.to_dict()
             return json.dumps(lowercase     ,indent=2     ,sort_keys=lowercase) + "\n"



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.to_dict()

             # get the default config dict
             UpperCAmelCase_  :	Tuple				      =		BitsAndBytesConfig().to_dict()

             UpperCAmelCase_  :	Union[str, Any]				      =		{}

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

             return serializable_config_dict





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
   from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase         =			get_tests_dir('''fixtures/test_sentencepiece.model''')

__lowerCamelCase         =			{'''target_lang''': '''fi''', '''source_lang''': '''en'''}
__lowerCamelCase         =			'''>>zh<<'''
__lowerCamelCase         =			'''Helsinki-NLP/'''

if is_torch_available():
   __lowerCamelCase         =			'''pt'''
elif is_tf_available():
   __lowerCamelCase         =			'''tf'''
else:
   __lowerCamelCase         =			'''jax'''


@require_sentencepiece
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				MarianTokenizer
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()
             UpperCAmelCase_  :	Union[str, Any]				      =		["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
             UpperCAmelCase_  :	Union[str, Any]				      =		dict(zip(lowercase     ,range(len(lowercase))))
             UpperCAmelCase_  :	int				      =		Path(self.tmpdirname)
             save_json(lowercase     ,save_dir / VOCAB_FILES_NAMES["vocab"])
             save_json(lowercase     ,save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"])
             if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
                   copyfile(lowercase     ,save_dir / VOCAB_FILES_NAMES["source_spm"])
                   copyfile(lowercase     ,save_dir / VOCAB_FILES_NAMES["target_spm"])

             UpperCAmelCase_  :	str				      =		MarianTokenizer.from_pretrained(self.tmpdirname)
             tokenizer.save_pretrained(self.tmpdirname)



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             return MarianTokenizer.from_pretrained(self.tmpdirname     ,**lowercase)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return (
                 "This is a test",
                 "This is a test",
             )



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		"</s>"
             UpperCAmelCase_  :	List[str]				      =		0

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		list(self.get_tokenizer().get_vocab().keys())

             self.assertEqual(vocab_keys[0]     ,"</s>")
             self.assertEqual(vocab_keys[1]     ,"<unk>")
             self.assertEqual(vocab_keys[-1]     ,"<pad>")
             self.assertEqual(len(lowercase)     ,9)



       def  A_   (     self):

             """simple docstring"""





             self.assertEqual(self.get_tokenizer().vocab_size     ,9)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de""")
             UpperCAmelCase_  :	Optional[int]				      =		en_de_tokenizer(["I am a small frog"]     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		[38, 121, 14, 697, 38848, 0]
             self.assertListEqual(lowercase     ,batch.input_ids[0])

             UpperCAmelCase_  :	Optional[int]				      =		tempfile.mkdtemp()
             en_de_tokenizer.save_pretrained(lowercase)
             UpperCAmelCase_  :	Any				      =		[x.name for x in Path(lowercase).glob("*")]
             self.assertIn("source.spm"     ,lowercase)
             MarianTokenizer.from_pretrained(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		self.get_tokenizer()

             UpperCAmelCase_  :	List[Any]				      =		tok(
                 ["I am a small frog" * 1000, "I am a small frog"]     ,padding=lowercase     ,truncation=lowercase     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             self.assertEqual(batch.input_ids.shape     ,(2, 512))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		self.get_tokenizer()
             UpperCAmelCase_  :	Optional[int]				      =		tok(["I am a tiny frog", "I am a small frog"]     ,padding=lowercase     ,return_tensors=lowercase)
             self.assertIsInstance(lowercase     ,lowercase)
             self.assertEqual(batch_smaller.input_ids.shape     ,(2, 10))



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		{"input_ids": [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11501, 23, 8460, 4073, 34009, 20, 435, 11439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 37867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 26453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10767, 6, 316, 304, 4239, 3, 0], [148, 15722, 19, 1839, 12, 1350, 13, 22327, 5082, 5418, 47567, 35938, 59, 318, 19552, 108, 2183, 54, 14976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 19088, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100], [36, 6395, 12570, 39147, 11597, 6, 266, 4, 45405, 7296, 3, 0, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100, 58100]], "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, 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], [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]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase     ,model_name="Helsinki-NLP/opus-mt-en-de"     ,revision="1a8c2263da11e68e50938f97e10cd57820bd504c"     ,decode_kwargs={"use_source_tokenizer": True}     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs")

             UpperCAmelCase_  :	Any				      =		"Tämä on testi"
             UpperCAmelCase_  :	List[str]				      =		"This is a test"

             UpperCAmelCase_  :	int				      =		[76, 7, 2047, 2]
             UpperCAmelCase_  :	Any				      =		[69, 12, 11, 940, 2]

             UpperCAmelCase_  :	Any				      =		tokenizer(lowercase).input_ids
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	Any				      =		tokenizer(text_target=lowercase).input_ids
             self.assertListEqual(lowercase     ,lowercase)

             UpperCAmelCase_  :	List[Any]				      =		tokenizer.decode(lowercase     ,skip_special_tokens=lowercase)
             self.assertEqual(lowercase     ,lowercase)
