




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_    ={
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""GPT2TokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GPT2DoubleHeadsModel""",
		    """GPT2ForQuestionAnswering""",
		    """GPT2ForSequenceClassification""",
		    """GPT2ForTokenClassification""",
		    """GPT2LMHeadModel""",
		    """GPT2Model""",
		    """GPT2PreTrainedModel""",
		    """load_tf_weights_in_gpt2""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =[
		    """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGPT2DoubleHeadsModel""",
		    """TFGPT2ForSequenceClassification""",
		    """TFGPT2LMHeadModel""",
		    """TFGPT2MainLayer""",
		    """TFGPT2Model""",
		    """TFGPT2PreTrainedModel""",
		]

try:
		if not is_keras_nlp_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""TFGPT2Tokenizer"""]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_    =["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
		from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
		from .tokenization_gpta import GPTaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_gpta_fast import GPTaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpta import (
				    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTaDoubleHeadsModel,
				    GPTaForQuestionAnswering,
				    GPTaForSequenceClassification,
				    GPTaForTokenClassification,
				    GPTaLMHeadModel,
				    GPTaModel,
				    GPTaPreTrainedModel,
				    load_tf_weights_in_gpta,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gpta import (
				    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGPTaDoubleHeadsModel,
				    TFGPTaForSequenceClassification,
				    TFGPTaLMHeadModel,
				    TFGPTaMainLayer,
				    TFGPTaModel,
				    TFGPTaPreTrainedModel,
				)

		try:
				if not is_keras_nlp_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_gpta_tf import TFGPTaTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
		import sys

		UpperCAmelCase_    =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









import sacrebleu as scb
from packaging import version

import datasets


UpperCAmelCase_    ="""\
@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\",
}
"""

UpperCAmelCase_    ="""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

UpperCAmelCase_    ="""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


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





				'''simple docstring'''



				def    __snake_case		(			self    ):
											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'''							,			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''']							,			reference_urls=[
											        '''https://github.com/mjpost/sacreBLEU''',
											        '''https://en.wikipedia.org/wiki/BLEU''',
											        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
											    ]							,			)






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_="exp"							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			):
											lowerCAmelCase =		len(references[0]    )
											if any(len(UpperCAmelCase_    ) != references_per_prediction for refs in references    ):
																		raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
											lowerCAmelCase =		[[refs[i] for refs in references] for i in range(UpperCAmelCase_    )]
											lowerCAmelCase =		scb.corpus_bleu(
											    UpperCAmelCase_							,			UpperCAmelCase_							,			smooth_method=UpperCAmelCase_							,			smooth_value=UpperCAmelCase_							,			force=UpperCAmelCase_							,			lowercase=UpperCAmelCase_							,			use_effective_order=UpperCAmelCase_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
											lowerCAmelCase =		{
											    '''score''': output.score,
											    '''counts''': output.counts,
											    '''totals''': output.totals,
											    '''precisions''': output.precisions,
											    '''bp''': output.bp,
											    '''sys_len''': output.sys_len,
											    '''ref_len''': output.ref_len,
											}
											return output_dict



