


"""simple docstring"""
import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		@staticmethod
		def 							SCREAMING_SNAKE_CASE    (a_   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							parser.add_parser('''login'''							,			help='''Log in using the same credentials as on huggingface.co'''   )
			login_parser.set_defaults(func=lambda a_   : LoginCommand(a_   )   )
			__snake_case      :		Optional[Any]         =							parser.add_parser('''whoami'''							,			help='''Find out which huggingface.co account you are logged in as.'''   )
			whoami_parser.set_defaults(func=lambda a_   : WhoamiCommand(a_   )   )
			__snake_case      :		Optional[int]         =							parser.add_parser('''logout'''							,			help='''Log out'''   )
			logout_parser.set_defaults(func=lambda a_   : LogoutCommand(a_   )   )

			# new system: git-based repo system
			__snake_case      :		int         =							parser.add_parser(
			    '''repo'''							,			help='''Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'''							,			)
			__snake_case      :		Any         =							repo_parser.add_subparsers(
			    help='''Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands'''   )
			__snake_case      :		Tuple         =							repo_subparsers.add_parser(
			    '''create'''							,			help='''Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co'''   )
			repo_create_parser.add_argument(
			    '''name'''							,			type=a_							,			help='''Name for your model\'s repo. Will be namespaced under your username to build the model id.'''							,			)
			repo_create_parser.add_argument('''--organization'''							,			type=a_							,			help='''Optional: organization namespace.'''   )
			repo_create_parser.add_argument('''-y'''							,			'''--yes'''							,			action='''store_true'''							,			help='''Optional: answer Yes to the prompt'''   )
			repo_create_parser.set_defaults(func=lambda a_   : RepoCreateCommand(a_   )   )



class      _UpperCAmelCase					:






		'''simple docstring'''

		lowerCamelCase__           ='\u001b[1m'
		lowerCamelCase__           ='\u001b[31m'
		lowerCamelCase__           ='\u001b[90m'
		lowerCamelCase__           ='\u001b[0m'



		@classmethod
		def 							SCREAMING_SNAKE_CASE    (cls							,			a_   ):





			'''simple docstring'''

			return f"""{cls._bold}{s}{cls._reset}"""



		@classmethod
		def 							SCREAMING_SNAKE_CASE    (cls							,			a_   ):





			'''simple docstring'''

			return f"""{cls._bold}{cls._red}{s}{cls._reset}"""



		@classmethod
		def 							SCREAMING_SNAKE_CASE    (cls							,			a_   ):





			'''simple docstring'''

			return f"""{cls._gray}{s}{cls._reset}"""






def 						lowercase				(	_snake_case		: List[List[Union[str, int]]]	,  _snake_case		: List[str]	)	->str:
	"""simple docstring"""





	__snake_case      :		str         =							[max(len(str(_snake_case	)	) for x in col	) for col in zip(*_snake_case	,  _snake_case	)]
	__snake_case      :		Any         =							('''{{:{}}} ''' * len(_snake_case	)).format(*_snake_case	)
	__snake_case      :		Dict         =							[]
	lines.append(row_format.format(*_snake_case	)	)
	lines.append(row_format.format(*['''-''' * w for w in col_widths]	)	)
	for row in rows:
		lines.append(row_format.format(*_snake_case	)	)
	return "\n".join(_snake_case	)



class      _UpperCAmelCase					:






		'''simple docstring'''

		def __init__(self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							args



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			print(
			    ANSI.red(
			        '''ERROR! `huggingface-cli login` uses an outdated login mechanism '''
			        '''that is not compatible with the Hugging Face Hub backend anymore. '''
			        '''Please use `huggingface-cli login instead.'''   )   )



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			print(
			    ANSI.red(
			        '''WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '''
			        '''`huggingface-cli whoami` instead.'''   )   )
			__snake_case      :		List[str]         =							HfFolder.get_token()
			if token is None:
				print('''Not logged in'''   )
				exit()
			try:
				__snake_case      ,				__snake_case      :		str         =							whoami(a_   )
				print(a_   )
				if orgs:
					print(ANSI.bold('''orgs: '''   )							,			''','''.join(a_   )   )
			except HTTPError as e:
				print(a_   )
				print(ANSI.red(e.response.text   )   )
				exit(1   )



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			print(
			    ANSI.red(
			        '''ERROR! `transformers-cli logout` uses an outdated logout mechanism '''
			        '''that is not compatible with the Hugging Face Hub backend anymore. '''
			        '''Please use `huggingface-cli logout instead.'''   )   )



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			print(
			    ANSI.red(
			        '''WARNING! Managing repositories through transformers-cli is deprecated. '''
			        '''Please use `huggingface-cli` instead.'''   )   )
			__snake_case      :		Tuple         =							HfFolder.get_token()
			if token is None:
				print('''Not logged in'''   )
				exit(1   )
			try:
				__snake_case      :		str         =							subprocess.check_output(['''git''', '''--version''']   ).decode('''utf-8'''   )
				print(ANSI.gray(stdout.strip()   )   )
			except FileNotFoundError:
				print('''Looks like you do not have git installed, please install.'''   )

			try:
				__snake_case      :		List[Any]         =							subprocess.check_output(['''git-lfs''', '''--version''']   ).decode('''utf-8'''   )
				print(ANSI.gray(stdout.strip()   )   )
			except FileNotFoundError:
				print(
				    ANSI.red(
				        '''Looks like you do not have git-lfs installed, please install.'''
				        ''' You can install from https://git-lfs.github.com/.'''
				        ''' Then run `git lfs install` (you only have to do this once).'''   )   )
			print(''''''   )

			__snake_case      ,				__snake_case      :		Optional[Any]         =							whoami(a_   )
			__snake_case      :		Union[str, Any]         =							self.args.organization if self.args.organization is not None else user
			__snake_case      :		Any         =							f"""{namespace}/{self.args.name}"""
			print(f"""You are about to create {ANSI.bold(a_   )}"""   )

			if not self.args.yes:
				__snake_case      :		List[str]         =							input('''Proceed? [Y/n] '''   ).lower()
				if not (choice == "" or choice == "y" or choice == "yes"):
					print('''Abort'''   )
					exit()
			try:
				__snake_case      :		Tuple         =							create_repo(a_							,			name=self.args.name							,			organization=self.args.organization   )
			except HTTPError as e:
				print(a_   )
				print(ANSI.red(e.response.text   )   )
				exit(1   )
			print('''\nYour repo now lives at:'''   )
			print(f"""  {ANSI.bold(a_   )}"""   )
			print('''\nYou can clone it locally with the command below, and commit/push as usual.'''   )
			print(f"""\n  git clone {url}"""   )
			print(''''''   )












"""simple docstring"""





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCAmelCase__        =      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

lowerCAmelCase__        =      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


lowerCAmelCase__        =      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''





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


						def      snake_case_ (self		)			-> Tuple:
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,				citation=_CITATION ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"		),
								            "references": datasets.Value("string"		),
								        }		) ,				homepage="https://github.com/moussaKam/FrugalScore" ,				)


						def      snake_case_ (self ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "predictions and references should have the same number of sentences."
								if device is not None:
										assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
								else:
										UpperCamelCase =						"gpu" if torch.cuda.is_available() else "cpu"
								UpperCamelCase =						TrainingArguments(
								    "trainer" ,				fpaa=(device == "gpu") ,				per_device_eval_batch_size=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

								def tokenize_function(__a		):
										return self.tokenizer(
										    data["sentence1"] ,				data["sentence2"] ,				max_length=__a ,				truncation=__a ,				padding=__a		)

								UpperCamelCase =						raw_datasets.map(__a ,				batched=__a		)
								tokenized_datasets.remove_columns(["sentence1", "sentence2"]		)
								UpperCamelCase =						Trainer(self.model ,				__a ,				tokenizer=self.tokenizer		)
								UpperCamelCase =						trainer.predict(__a		)
								return {"scores": list(predictions.predictions.squeeze(-1		)		)}






