





"""simple docstring"""

import re
import string

import numpy as np

import datasets


__UpperCAmelCase        ="""\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"""

__UpperCAmelCase        ="""\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It\'s like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It\'s like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"""

__UpperCAmelCase        ="""\n"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,					_KWARGS_DESCRIPTION  )
class       lowerCAmelCase__				(   datasets.Metric  ):







 def  lowercase_       (   self					):

    '''simple docstring'''


    return datasets.MetricInfo(
        description=_DESCRIPTION		,	citation=_CITATION		,	inputs_description=_KWARGS_DESCRIPTION		,	features=datasets.Features(
            {
                "predictions": datasets.Value("string"		,	id="sequence"					),
                "references": datasets.Value("string"		,	id="sequence"					),
            }					)		,	reference_urls=[]		,	)







 def  lowercase_       (   self		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__=None		,	UpperCamelCase__=False		,	UpperCamelCase__=False		,	UpperCamelCase__=False		,	):

    '''simple docstring'''


    if regexes_to_ignore is not None:
       for s in regexes_to_ignore:
          A__											=					np.array([re.sub(UpperCamelCase__		,	""		,	UpperCamelCase__					) for x in predictions]					)
          A__											=					np.array([re.sub(UpperCamelCase__		,	""		,	UpperCamelCase__					) for x in references]					)
    else:
       A__											=					np.asarray(UpperCamelCase__					)
       A__											=					np.asarray(UpperCamelCase__					)

    if ignore_case:
       A__											=					np.char.lower(UpperCamelCase__					)
       A__											=					np.char.lower(UpperCamelCase__					)

    if ignore_punctuation:
       A__											=					string.punctuation.maketrans(""		,	""		,	string.punctuation					)
       A__											=					np.char.translate(UpperCamelCase__		,	table=UpperCamelCase__					)
       A__											=					np.char.translate(UpperCamelCase__		,	table=UpperCamelCase__					)

    if ignore_numbers:
       A__											=					string.digits.maketrans(""		,	""		,	string.digits					)
       A__											=					np.char.translate(UpperCamelCase__		,	table=UpperCamelCase__					)
       A__											=					np.char.translate(UpperCamelCase__		,	table=UpperCamelCase__					)

    A__											=					predictions == references

    return {"exact_match": np.mean(UpperCamelCase__					) * 1_00}







'''simple docstring'''



# 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


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
@dataclass
class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		List[Any]  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Union[str, Any]=6.0  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		Optional[Any]=False  ,   UpperCAmelCase      :		List[Any]=False  ,   UpperCAmelCase      :		Dict=None  ,   UpperCAmelCase      :		Tuple="fp4"  ,   UpperCAmelCase      :		str=False  ,   **UpperCAmelCase      :		Union[str, Any]  ,   )	->	str:


       '''simple docstring'''

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

       if bnb_abit_compute_dtype is None:
        lowercase       : str						=torch.floataa
       elif isinstance(UpperCAmelCase  ,   UpperCAmelCase		):
        lowercase       : Tuple						=getattr(UpperCAmelCase  ,   UpperCAmelCase		)
       elif isinstance(UpperCAmelCase  ,   torch.dtype		):
        lowercase       : Optional[int]						=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      :		List[Any]		)	->	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      :		Any		)	->	Tuple:


       '''simple docstring'''

       return self.load_in_abit or self.load_in_abit






      def 					A__   (			self      :		Tuple		)	->	Any:


       '''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      :		Union[str, Any]  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Any  ,   **UpperCAmelCase      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : Dict						=cls(**UpperCAmelCase		)

       lowercase       : Dict						=[]
       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      :		List[Any]  ,   UpperCAmelCase      :		Union[str, os.PathLike]		)	->	Dict:


       '''simple docstring'''

       with open(UpperCAmelCase  ,   '''w'''  ,   encoding='''utf-8'''		) as writer:
        lowercase       : Any						=self.to_dict()
        lowercase       : List[Any]						=json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + '''\n'''

        writer.write(UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase       : Tuple						=copy.deepcopy(self.__dict__		)
       lowercase       : Optional[Any]						=str(output['''bnb_4bit_compute_dtype''']		).split('''.'''		)[1]

       return output






      def __repr__(			self      :		Optional[Any]		)	->	Union[str, Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		bool = True		)	->	str:


       '''simple docstring'''

       if use_diff is True:
        lowercase       : int						=self.to_diff_dict()
       else:
        lowercase       : List[str]						=self.to_dict()
       return json.dumps(UpperCAmelCase  ,   indent=2  ,   sort_keys=UpperCAmelCase		) + "\n"






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


       '''simple docstring'''

       lowercase       : Any						=self.to_dict()

       # get the default config dict
       lowercase       : Union[str, Any]						=BitsAndBytesConfig().to_dict()

       lowercase       : int						={}

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

       return serializable_config_dict



