






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

import datasets


SCREAMING_SNAKE_CASE__   :      Tuple				=		"""\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@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\",
}
"""

SCREAMING_SNAKE_CASE__   :      Any				=		"""\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

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#ter for more information.
"""

SCREAMING_SNAKE_CASE__   :      List[Any]				=		"""
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,					_KWARGS_DESCRIPTION			)
class     lowerCamelCase_					(   datasets.Metric			):







				def       A						(    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='''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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       __lowerCAmelCase = False							,       ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   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'''  )
								__magic_name__		:Any   =   [[refs[i] for refs in references] for i in range(__lowerCAmelCase  )]

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

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











"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  str =       logging.get_logger(__name__)

__snake_case				:  Union[str, Any] =       {
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'git_vision_model'

	def __init__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[Any]=768			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=3072			,     _SCREAMING_SNAKE_CASE:						str=12			,     _SCREAMING_SNAKE_CASE:						Any=12			,     _SCREAMING_SNAKE_CASE:						Dict=3			,     _SCREAMING_SNAKE_CASE:						List[Any]=224			,     _SCREAMING_SNAKE_CASE:						List[str]=16			,     _SCREAMING_SNAKE_CASE:						str="quick_gelu"			,     _SCREAMING_SNAKE_CASE:						Any=1e-5			,     _SCREAMING_SNAKE_CASE:						List[str]=0.0			,     _SCREAMING_SNAKE_CASE:						Dict=0.02			,     **_SCREAMING_SNAKE_CASE:						str			,     )							->			Optional[Any]:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			hidden_size
				__lowerCAmelCase :    List[str]				=			intermediate_size
				__lowerCAmelCase :    List[str]				=			num_hidden_layers
				__lowerCAmelCase :    Tuple				=			num_attention_heads
				__lowerCAmelCase :    List[Any]				=			num_channels
				__lowerCAmelCase :    Dict				=			patch_size
				__lowerCAmelCase :    Optional[int]				=			image_size
				__lowerCAmelCase :    Optional[Any]				=			initializer_range
				__lowerCAmelCase :    str				=			attention_dropout
				__lowerCAmelCase :    Optional[Any]				=			layer_norm_eps
				__lowerCAmelCase :    Tuple				=			hidden_act







	@classmethod
	def 				_SCREAMING_SNAKE_CASE  (	cls:						Dict			,     _SCREAMING_SNAKE_CASE:						Union[str, os.PathLike]			,     **_SCREAMING_SNAKE_CASE:						Dict)							->			"PretrainedConfig":

				"""simple docstring"""

				cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			cls.get_config_dict(_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				# get the vision config dict if we are loading from GITConfig
				if config_dict.get("model_type") == "git":
							__lowerCAmelCase :    str				=			config_dict["vision_config"]

				if "model_type" in config_dict and hasattr(cls			,     "model_type") and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
							    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

				return cls.from_dict(_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'git'

	def __init__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Tuple=None			,     _SCREAMING_SNAKE_CASE:						Dict=3_0522			,     _SCREAMING_SNAKE_CASE:						Any=768			,     _SCREAMING_SNAKE_CASE:						str=6			,     _SCREAMING_SNAKE_CASE:						int=12			,     _SCREAMING_SNAKE_CASE:						Dict=3072			,     _SCREAMING_SNAKE_CASE:						Optional[Any]="gelu"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.1			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1024			,     _SCREAMING_SNAKE_CASE:						str=0.02			,     _SCREAMING_SNAKE_CASE:						Tuple=1e-12			,     _SCREAMING_SNAKE_CASE:						List[str]=0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]="absolute"			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=True			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						Dict=101			,     _SCREAMING_SNAKE_CASE:						Tuple=102			,     _SCREAMING_SNAKE_CASE:						Any=None			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[Any]:

				"""simple docstring"""

				super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     pad_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

				if vision_config is None:
							__lowerCAmelCase :    int				=			{}
							logger.info("vision_config is None. initializing the GitVisionConfig with default values.")

				__lowerCAmelCase :    List[str]				=			GitVisionConfig(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[Any]				=			vocab_size
				__lowerCAmelCase :    int				=			hidden_size
				__lowerCAmelCase :    Dict				=			num_hidden_layers
				__lowerCAmelCase :    Tuple				=			num_attention_heads
				__lowerCAmelCase :    Union[str, Any]				=			hidden_act
				__lowerCAmelCase :    Dict				=			intermediate_size
				__lowerCAmelCase :    List[str]				=			hidden_dropout_prob
				__lowerCAmelCase :    Union[str, Any]				=			attention_probs_dropout_prob
				__lowerCAmelCase :    Union[str, Any]				=			max_position_embeddings
				__lowerCAmelCase :    Optional[int]				=			initializer_range
				__lowerCAmelCase :    str				=			layer_norm_eps
				__lowerCAmelCase :    Optional[Any]				=			position_embedding_type
				__lowerCAmelCase :    str				=			use_cache
				__lowerCAmelCase :    str				=			tie_word_embeddings
				__lowerCAmelCase :    Optional[int]				=			num_image_with_embedding

				__lowerCAmelCase :    Optional[Any]				=			bos_token_id
				__lowerCAmelCase :    Any				=			eos_token_id







	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			copy.deepcopy(self.__dict__)
				__lowerCAmelCase :    List[Any]				=			self.vision_config.to_dict()
				__lowerCAmelCase :    Tuple				=			self.__class__.model_type
				return output