





'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_SCREAMING_SNAKE_CASE		       =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE		       =							{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}

class       lowerCAmelCase_						(						_UpperCAmelCase						):
 __lowerCamelCase  :							Dict				     =    "deberta-v2"




 def __init__(		self	,   _lowerCAmelCase=128100	,   _lowerCAmelCase=1536	,   _lowerCAmelCase=24	,   _lowerCAmelCase=24	,   _lowerCAmelCase=6144	,   _lowerCAmelCase="gelu"	,   _lowerCAmelCase=0.1	,   _lowerCAmelCase=0.1	,   _lowerCAmelCase=512	,   _lowerCAmelCase=0	,   _lowerCAmelCase=0.02	,   _lowerCAmelCase=1E-7	,   _lowerCAmelCase=False	,   _lowerCAmelCase=-1	,   _lowerCAmelCase=0	,   _lowerCAmelCase=True	,   _lowerCAmelCase=None	,   _lowerCAmelCase=0	,   _lowerCAmelCase="gelu"	,   **_lowerCAmelCase	,   )			->							Union[str, Any]:
     super().__init__(**lowercase_						)

     _lowerCAmelCase			=					hidden_size
     _lowerCAmelCase			=					num_hidden_layers
     _lowerCAmelCase			=					num_attention_heads
     _lowerCAmelCase			=					intermediate_size
     _lowerCAmelCase			=					hidden_act
     _lowerCAmelCase			=					hidden_dropout_prob
     _lowerCAmelCase			=					attention_probs_dropout_prob
     _lowerCAmelCase			=					max_position_embeddings
     _lowerCAmelCase			=					type_vocab_size
     _lowerCAmelCase			=					initializer_range
     _lowerCAmelCase			=					relative_attention
     _lowerCAmelCase			=					max_relative_positions
     _lowerCAmelCase			=					pad_token_id
     _lowerCAmelCase			=					position_biased_input

     # Backwards compatibility
     if type(lowercase_						) == str:
         _lowerCAmelCase			=					[x.strip() for x in pos_att_type.lower().split("|"						)]

     _lowerCAmelCase			=					pos_att_type
     _lowerCAmelCase			=					vocab_size
     _lowerCAmelCase			=					layer_norm_eps

     _lowerCAmelCase			=					kwargs.get("pooler_hidden_size"	,   lowercase_						)
     _lowerCAmelCase			=					pooler_dropout
     _lowerCAmelCase			=					pooler_hidden_act







class       lowerCAmelCase_						(						_UpperCAmelCase						):




 @property
 def 							_snake_case      (		self						)			->							List[str]:
     if self.task == "multiple-choice":
         _lowerCAmelCase			=					{0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
         _lowerCAmelCase			=					{0: """batch""", 1: """sequence"""}
     if self._config.type_vocab_size > 0:
         return OrderedDict(
             [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]						)
     else:
         return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]						)




 @property
 def 							_snake_case      (		self						)			->							Optional[Any]:
     return 12





 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase = -1	,   _lowerCAmelCase = -1	,   _lowerCAmelCase = -1	,   _lowerCAmelCase = False	,   _lowerCAmelCase = None	,   _lowerCAmelCase = 3	,   _lowerCAmelCase = 40	,   _lowerCAmelCase = 40	,   _lowerCAmelCase = None	,   )			->							Tuple:
     _lowerCAmelCase			=					super().generate_dummy_inputs(preprocessor=lowercase_	,   framework=lowercase_						)
     if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
     return dummy_inputs



'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_lowercase						: List[str]						=						"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_lowercase						: Tuple						=						"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_lowercase						: Optional[int]						=						"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										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="""http://www.cs.umd.edu/~snover/tercom/"""				,			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#ter"""]				,			reference_urls=[
										        """https://github.com/jhclark/tercom""",
										    ]				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Any				,			lowercase_				:  Tuple				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			):
										lowercase_					:			int										=       len(references[0]    )
										if any(len(lowercase_    ) != references_per_prediction for refs in references    ):
																	raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
										lowercase_					:			Optional[int]										=       [[refs[i] for refs in references] for i in range(lowercase_    )]

										lowercase_					:			List[str]										=       TER(
										    normalized=lowercase_				,			no_punct=lowercase_				,			asian_support=lowercase_				,			case_sensitive=lowercase_				,			)
										lowercase_					:			List[str]										=       sb_ter.corpus_score(lowercase_				,			lowercase_    )

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





