


import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase__			:      List[str]								="""\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowerCAmelCase__			:      List[str]								="""\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

lowerCAmelCase__			:      Dict								="""
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if version.parse(scb.__version__       ) < version.parse('1.4.12'       ):
													raise ImportWarning(
													    'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
													    'You can install it with `pip install \"sacrebleu>=1.4.12\"`.'       )
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,						citation=_CITATION			,						homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf'			,						inputs_description=_KWARGS_DESCRIPTION			,						features=datasets.Features(
										        {
										            'predictions': datasets.Value('string'			,						id='sequence'       ),
										            'references': datasets.Sequence(datasets.Value('string'			,						id='sequence'       )			,						id='references'       ),
										        }       )			,						codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf']			,						reference_urls=[
										        'https://github.com/m-popovic/chrF',
										    ]			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = CHRF.CHAR_ORDER			,						lowerCAmelCase__ = CHRF.WORD_ORDER			,						lowerCAmelCase__ = CHRF.BETA			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(references[0]       )
										if any(len(lowerCAmelCase__       ) != references_per_prediction for refs in references       ):
													raise ValueError('Sacrebleu requires the same number of references for each prediction'       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					[[refs[i] for refs in references] for i in range(lowerCAmelCase__       )]

										SCREAMING_SNAKE_CASE_		:     Any					=					CHRF(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     int					=					sb_chrf.corpus_score(lowerCAmelCase__			,						lowerCAmelCase__       )

										return {
										    "score": output.score,
										    "char_order": output.char_order,
										    "word_order": output.word_order,
										    "beta": output.beta,
										}







import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
							import bitsandbytes as bnb
							import torch
							import torch.nn as nn

							from ..pytorch_utils import ConvaD

if is_accelerate_available():
							from accelerate import init_empty_weights
							from accelerate.utils import find_tied_parameters

UpperCamelCase_						:     int	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Optional[Any] ,			_UpperCAmelCase  :   Dict=None ,			_UpperCAmelCase  :   Dict=None  )   ->			Tuple:






						'''simple docstring'''

						if "." in tensor_name:
												_lowercase       :	Dict           =   tensor_name.split("."  )
												for split in splits[:-1]:
																		_lowercase       :	Optional[int]           =   getattr(_UpperCAmelCase ,			_UpperCAmelCase  )
																		if new_module is None:
																								raise ValueError(f"""{module} has no attribute {split}."""  )
																		_lowercase       :	List[Any]           =   new_module
												_lowercase       :	Dict           =   splits[-1]

						if tensor_name not in module._parameters and tensor_name not in module._buffers:
												raise ValueError(f"""{module} does not have a parameter or a buffer named {tensor_name}."""  )
						_lowercase       :	Dict           =   tensor_name in module._buffers
						_lowercase       :	Union[str, Any]           =   getattr(_UpperCAmelCase ,			_UpperCAmelCase  )

						if old_value.device == torch.device("meta"  ) and device not in ["meta", torch.device("meta"  )] and value is None:
												raise ValueError(f"""{tensor_name} is on the meta device, we need a `value` to put in on {device}."""  )

						_lowercase       :	int           =   False
						_lowercase       :	str           =   False
						if is_buffer or not is_bitsandbytes_available():
												_lowercase       :	int           =   False
												_lowercase       :	List[str]           =   False
						else:
												_lowercase       :	List[str]           =   hasattr(bnb.nn ,			"Params4bit"  ) and isinstance(module._parameters[tensor_name] ,			bnb.nn.Paramsabit  )
												_lowercase       :	int           =   isinstance(module._parameters[tensor_name] ,			bnb.nn.IntaParams  )

						if is_abit or is_abit:
												_lowercase       :	Optional[int]           =   module._parameters[tensor_name]
												if param.device.type != "cuda":
																		if value is None:
																								_lowercase       :	int           =   old_value.to(_UpperCAmelCase  )
																		elif isinstance(_UpperCAmelCase ,			torch.Tensor  ):
																								_lowercase       :	Dict           =   value.to("cpu"  )
																								if value.dtype == torch.inta:
																														_lowercase       :	str           =   version.parse(importlib.metadata.version("bitsandbytes"  )  ) > version.parse(
																														    "0.37.2"  )
																														if not is_abit_serializable:
																																				raise ValueError(
																																				    "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
																																				    "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."  )
																		else:
																								_lowercase       :	List[str]           =   torch.tensor(_UpperCAmelCase ,			device="cpu"  )

																		# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
																		# Since weights are saved in the correct "orientation", we skip transposing when loading.
																		if issubclass(module.source_cls ,			_UpperCAmelCase  ) and fpaa_statistics is None:
																								_lowercase       :	List[Any]           =   new_value.T

																		_lowercase       :	List[str]           =   old_value.__dict__
																		if is_abit:
																								_lowercase       :	Optional[Any]           =   bnb.nn.IntaParams(_UpperCAmelCase ,			requires_grad=_UpperCAmelCase ,			**_UpperCAmelCase  ).to(_UpperCAmelCase  )
																		elif is_abit:
																								_lowercase       :	Dict           =   bnb.nn.Paramsabit(_UpperCAmelCase ,			requires_grad=_UpperCAmelCase ,			**_UpperCAmelCase  ).to(_UpperCAmelCase  )

																		_lowercase       :	str           =   new_value
																		if fpaa_statistics is not None:
																								setattr(module.weight ,			"SCB" ,			fpaa_statistics.to(_UpperCAmelCase  )  )

						else:
												if value is None:
																		_lowercase       :	Optional[Any]           =   old_value.to(_UpperCAmelCase  )
												elif isinstance(_UpperCAmelCase ,			torch.Tensor  ):
																		_lowercase       :	Tuple           =   value.to(_UpperCAmelCase  )
												else:
																		_lowercase       :	Optional[Any]           =   torch.tensor(_UpperCAmelCase ,			device=_UpperCAmelCase  )

												if is_buffer:
																		_lowercase       :	str           =   new_value
												else:
																		_lowercase       :	int           =   nn.Parameter(_UpperCAmelCase ,			requires_grad=old_value.requires_grad  )
																		_lowercase       :	str           =   new_value



def 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   str=None ,			_UpperCAmelCase  :   List[str]=None ,			_UpperCAmelCase  :   Tuple=None ,			_UpperCAmelCase  :   Tuple=False  )   ->			int:






						'''simple docstring'''

						for name, module in model.named_children():
												if current_key_name is None:
																		_lowercase       :	int           =   []
												current_key_name.append(_UpperCAmelCase  )

												if (isinstance(_UpperCAmelCase ,			nn.Linear  ) or isinstance(_UpperCAmelCase ,			_UpperCAmelCase  )) and name not in modules_to_not_convert:
																		# Check if the current key is not in the `modules_to_not_convert`
																		if not any(key in ".".join(_UpperCAmelCase  ) for key in modules_to_not_convert  ):
																								with init_empty_weights():
																														if isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ):
																																				_lowercase      ,    _lowercase       :	Tuple           =   module.weight.shape
																														else:
																																				_lowercase       :	List[Any]           =   module.in_features
																																				_lowercase       :	List[str]           =   module.out_features

																														if quantization_config.quantization_method() == "llm_int8":
																																				_lowercase       :	Union[str, Any]           =   bnb.nn.LinearabitLt(
																																				    _UpperCAmelCase ,			_UpperCAmelCase ,			module.bias is not None ,			has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight ,			threshold=quantization_config.llm_inta_threshold ,			)
																																				_lowercase       :	Tuple           =   True
																														else:
																																				if (
																																				    quantization_config.llm_inta_skip_modules is not None
																																				    and name in quantization_config.llm_inta_skip_modules
																																				):
																																										pass
																																				else:
																																										_lowercase       :	List[Any]           =   bnb.nn.Linearabit(
																																										    _UpperCAmelCase ,			_UpperCAmelCase ,			module.bias is not None ,			quantization_config.bnb_abit_compute_dtype ,			compress_statistics=quantization_config.bnb_abit_use_double_quant ,			quant_type=quantization_config.bnb_abit_quant_type ,			)
																																										_lowercase       :	Optional[int]           =   True
                    # Store the module class in case we need to transpose the weight later
																														_lowercase       :	str           =   type(_UpperCAmelCase  )
																														# Force requires grad to False to avoid unexpected errors
																														model._modules[name].requires_grad_(_UpperCAmelCase  )
												if len(list(module.children()  )  ) > 0:
																		_lowercase      ,    _lowercase       :	List[str]           =   _replace_with_bnb_linear(
																		    _UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			has_been_replaced=_UpperCAmelCase ,			)
												# Remove the last key for recursion
												current_key_name.pop(-1  )
						return model, has_been_replaced



def 		UpperCamelCase     (       _UpperCAmelCase  :   Optional[int] ,			_UpperCAmelCase  :   Tuple=None ,			_UpperCAmelCase  :   Optional[int]=None ,			_UpperCAmelCase  :   int=None  )   ->			Optional[int]:






						'''simple docstring'''

						_lowercase       :	str           =   ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
						_lowercase      ,    _lowercase       :	Any           =   _replace_with_bnb_linear(
						    _UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  )

						if not has_been_replaced:
												logger.warning(
												    "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
												    " Please double check your model architecture, or submit an issue on github if you think this is"
												    " a bug."  )

						return model



def 		UpperCamelCase     (       *_UpperCAmelCase  :   Optional[Any] ,			**_UpperCAmelCase  :   Dict  )   ->			Tuple:






						'''simple docstring'''

						warnings.warn(
						    "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead" ,			_UpperCAmelCase ,			)
						return replace_with_bnb_linear(*_UpperCAmelCase ,			**_UpperCAmelCase  )



def 		UpperCamelCase     (       *_UpperCAmelCase  :   Dict ,			**_UpperCAmelCase  :   Dict  )   ->			int:






						'''simple docstring'''

						warnings.warn(
						    "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead" ,			_UpperCAmelCase ,			)
						return set_module_quantized_tensor_to_device(*_UpperCAmelCase ,			**_UpperCAmelCase  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[Any]  )   ->			Tuple:






						'''simple docstring'''

						_lowercase       :	Tuple           =   deepcopy(_UpperCAmelCase  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
						tied_model.tie_weights()

						_lowercase       :	List[Any]           =   find_tied_parameters(_UpperCAmelCase  )
						# For compatibility with Accelerate < 0.18
						if isinstance(_UpperCAmelCase ,			_UpperCAmelCase  ):
												_lowercase       :	Any           =   sum(list(tied_params.values()  ) ,			[]  ) + list(tied_params.keys()  )
						else:
												_lowercase       :	Tuple           =   sum(_UpperCAmelCase ,			[]  )
						_lowercase       :	List[Any]           =   len(_UpperCAmelCase  ) > 0

						# Check if it is a base model
						_lowercase       :	Any           =   not hasattr(_UpperCAmelCase ,			model.base_model_prefix  )

						# Ignore this for base models (BertModel, GPT2Model, etc.)
						if (not has_tied_params) and is_base_model:
												return []

						# otherwise they have an attached head
						_lowercase       :	int           =   list(model.named_children()  )
						_lowercase       :	str           =   [list_modules[-1][0]]

						# add last module together with tied weights
						_lowercase       :	Optional[int]           =   set(_UpperCAmelCase  ) - set(_UpperCAmelCase  )
						_lowercase       :	Optional[int]           =   list(set(_UpperCAmelCase  )  ) + list(_UpperCAmelCase  )

						# remove ".weight" from the keys
						_lowercase       :	str           =   [".weight", ".bias"]
						_lowercase       :	Optional[Any]           =   []
						for name in list_untouched:
												for name_to_remove in names_to_remove:
																		if name_to_remove in name:
																								_lowercase       :	Optional[Any]           =   name.replace(_UpperCAmelCase ,			""  )
												filtered_module_names.append(_UpperCAmelCase  )

						return filtered_module_names




