





'''simple docstring'''




import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowercase__       : List[Any]         =       logging.getLogger()






def 			_lowerCAmelCase     (    __snake_case    :					Path					,				__snake_case    :					list     )	->	List[str]:
     __A		:					Tuple						=			'\n'.join(__snake_case     )
     Path(__snake_case     ).open('w'     ).writelines(__snake_case     )


lowercase__       : Optional[Any]         =       '''patrickvonplaten/t5-tiny-random'''
lowercase__       : List[Any]         =       '''sshleifer/bart-tiny-random'''
lowercase__       : Optional[Any]         =       '''sshleifer/tiny-mbart'''

lowercase__       : Any         =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 					SCREAMING_SNAKE_CASE       (a__				):


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
          __A		:					Optional[Any]						=			input_file_name.parent / 'utest_output.txt'
          assert not output_file_name.exists()
          __A		:					Optional[int]						=			[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
          _dump_articles(_UpperCAmelCase		,      _UpperCAmelCase)

          __A		:					Optional[int]						=			str(Path(self.get_auto_remove_tmp_dir()) / 'scores.json')
          __A		:					Dict						=			'translation_en_to_de' if model == T5_TINY else 'summarization'
          __A		:					Any						=			F'\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            '.split()

          with patch.object(_UpperCAmelCase		,      'argv'		,      _UpperCAmelCase):
               run_generate()
               assert Path(_UpperCAmelCase).exists()
               # os.remove(Path(output_file_name))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.run_eval_tester(_UpperCAmelCase)


     @parameterized.expand([BART_TINY, MBART_TINY])
     @slow
     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          self.run_eval_tester(_UpperCAmelCase)


     @parameterized.expand([T5_TINY, MBART_TINY])
     @slow
     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Tuple						=			Path(self.get_auto_remove_tmp_dir()) / 'utest_input.source'
          __A		:					str						=			input_file_name.parent / 'utest_output.txt'
          assert not output_file_name.exists()

          __A		:					int						=			{
              'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
              'de': [
                  'Maschinelles Lernen ist großartig, oder?',
                  'Ich esse gerne Bananen',
                  'Morgen ist wieder ein toller Tag!',
              ],
          }

          __A		:					Dict						=			Path(self.get_auto_remove_tmp_dir())
          __A		:					str						=			str(tmp_dir / 'scores.json')
          __A		:					int						=			str(tmp_dir / 'val.target')
          _dump_articles(_UpperCAmelCase		,      text['en'])
          _dump_articles(_UpperCAmelCase		,      text['de'])
          __A		:					Optional[int]						=			'translation_en_to_de' if model == T5_TINY else 'summarization'
          __A		:					int						=			F'\n            run_eval_search.py\n            {model}\n            {str(_UpperCAmelCase)}\n            {str(_UpperCAmelCase)}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            '.split()
          testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'])

          with patch.object(_UpperCAmelCase		,      'argv'		,      _UpperCAmelCase):
               with CaptureStdout() as cs:
                    run_search()
               __A		:					str						=			[' num_beams | length_penalty', model, 'Best score args']
               __A		:					List[Any]						=			['Info']
               if "translation" in task:
                    expected_strings.append('bleu')
               else:
                    expected_strings.extend(_UpperCAmelCase)
               for w in expected_strings:
                    assert w in cs.out
               for w in un_expected_strings:
                    assert w not in cs.out
               assert Path(_UpperCAmelCase).exists()
               os.remove(Path(_UpperCAmelCase))


# 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


UpperCamelCase__		 =     logging.get_logger(__name__)

@dataclass
class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=6.0      , _UpperCamelCase=None      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=None      , _UpperCamelCase="fp4"      , _UpperCamelCase=False      , **_UpperCamelCase      , )      ->							int:






              """simple docstring"""


              __snake_case       	=							load_in_abit
              __snake_case       	=							load_in_abit
              __snake_case       	=							llm_inta_threshold
              __snake_case       	=							llm_inta_skip_modules
              __snake_case       	=							llm_inta_enable_fpaa_cpu_offload
              __snake_case       	=							llm_inta_has_fpaa_weight
              __snake_case       	=							bnb_abit_quant_type
              __snake_case       	=							bnb_abit_use_double_quant

              if bnb_abit_compute_dtype is None:
                     __snake_case       	=							torch.floataa
              elif isinstance(_UpperCamelCase      , _UpperCamelCase   ):
                     __snake_case       	=							getattr(_UpperCamelCase      , _UpperCamelCase   )
              elif isinstance(_UpperCamelCase      , torch.dtype   ):
                     __snake_case       	=							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   )      ->							Union[str, Any]:






              """simple docstring"""


              if not isinstance(self.llm_inta_threshold      , _UpperCamelCase   ):
                     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      , _UpperCamelCase   ):
                     raise ValueError("""llm_int8_skip_modules must be a list of strings"""   )
              if not isinstance(self.llm_inta_enable_fpaa_cpu_offload      , _UpperCamelCase   ):
                     raise ValueError("""llm_int8_enable_fp32_cpu_offload must be a boolean"""   )

              if not isinstance(self.llm_inta_has_fpaa_weight      , _UpperCamelCase   ):
                     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      , _UpperCamelCase   ):
                     raise ValueError("""bnb_4bit_quant_type must be a string"""   )

              if not isinstance(self.bnb_abit_use_double_quant      , _UpperCamelCase   ):
                     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   )      ->							int:






              """simple docstring"""


              return self.load_in_abit or self.load_in_abit


       def 			a		(		self   )      ->							int:






              """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      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							cls(**_UpperCamelCase   )

              __snake_case       	=							[]
              for key, value in kwargs.items():
                     if hasattr(_UpperCamelCase      , _UpperCamelCase   ):
                            setattr(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
                            to_remove.append(_UpperCamelCase   )
              for key in to_remove:
                     kwargs.pop(_UpperCamelCase      , _UpperCamelCase   )

              if return_unused_kwargs:
                     return config, kwargs
              else:
                     return config


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


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

                     writer.write(_UpperCamelCase   )


       def 			a		(		self   )      ->							Dict[str, Any]:






              """simple docstring"""


              __snake_case       	=							copy.deepcopy(self.__dict__   )
              __snake_case       	=							str(output["""bnb_4bit_compute_dtype"""]   ).split("""."""   )[1]

              return output


       def __repr__(		self   )      ->							Dict:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase = True   )      ->							str:






              """simple docstring"""


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






       def 			a		(		self   )      ->							Dict[str, Any]:






              """simple docstring"""


              __snake_case       	=							self.to_dict()

              # get the default config dict
              __snake_case       	=							BitsAndBytesConfig().to_dict()

              __snake_case       	=							{}

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

              return serializable_config_dict

