

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


__a    :Optional[int]      =			logging.get_logger(__name__)

__a    :Optional[int]      =			{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Dict      		=       'detr'
      _lowerCamelCase       :			Union[str, Any]      		=       ['past_key_values']
      _lowerCamelCase       :			Optional[int]      		=       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(   self							:				int						,			UpperCAmelCase							:				List[Any]=True						,			UpperCAmelCase							:				Union[str, Any]=None						,			UpperCAmelCase							:				Dict=3						,			UpperCAmelCase							:				List[Any]=100						,			UpperCAmelCase							:				Union[str, Any]=6						,			UpperCAmelCase							:				Optional[int]=2048						,			UpperCAmelCase							:				str=8						,			UpperCAmelCase							:				Optional[Any]=6						,			UpperCAmelCase							:				List[Any]=2048						,			UpperCAmelCase							:				Tuple=8						,			UpperCAmelCase							:				List[Any]=0.0						,			UpperCAmelCase							:				Any=0.0						,			UpperCAmelCase							:				Tuple=True						,			UpperCAmelCase							:				List[str]="relu"						,			UpperCAmelCase							:				List[str]=256						,			UpperCAmelCase							:				str=0.1						,			UpperCAmelCase							:				Optional[Any]=0.0						,			UpperCAmelCase							:				Any=0.0						,			UpperCAmelCase							:				Optional[int]=0.02						,			UpperCAmelCase							:				Tuple=1.0						,			UpperCAmelCase							:				Any=False						,			UpperCAmelCase							:				Union[str, Any]="sine"						,			UpperCAmelCase							:				str="resnet50"						,			UpperCAmelCase							:				List[Any]=True						,			UpperCAmelCase							:				int=False						,			UpperCAmelCase							:				Dict=1						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Optional[int]=2						,			UpperCAmelCase							:				Dict=1						,			UpperCAmelCase							:				Union[str, Any]=1						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Optional[Any]=2						,			UpperCAmelCase							:				Dict=0.1						,			**UpperCAmelCase							:				int						,			):
            if backbone_config is not None and use_timm_backbone:
                  raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

            if not use_timm_backbone:
                  if backbone_config is None:
                        logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
                        A_						  =				CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
                  elif isinstance(UpperCAmelCase						,			UpperCAmelCase	):
                        A_						  =				backbone_config.get("model_type"	)
                        A_						  =				CONFIG_MAPPING[backbone_model_type]
                        A_						  =				config_class.from_dict(UpperCAmelCase	)
                  # set timm attributes to None
                  A_  ,     A_  ,     A_						  =				None, None, None

            A_						  =				use_timm_backbone
            A_						  =				backbone_config
            A_						  =				num_channels
            A_						  =				num_queries
            A_						  =				d_model
            A_						  =				encoder_ffn_dim
            A_						  =				encoder_layers
            A_						  =				encoder_attention_heads
            A_						  =				decoder_ffn_dim
            A_						  =				decoder_layers
            A_						  =				decoder_attention_heads
            A_						  =				dropout
            A_						  =				attention_dropout
            A_						  =				activation_dropout
            A_						  =				activation_function
            A_						  =				init_std
            A_						  =				init_xavier_std
            A_						  =				encoder_layerdrop
            A_						  =				decoder_layerdrop
            A_						  =				encoder_layers
            A_						  =				auxiliary_loss
            A_						  =				position_embedding_type
            A_						  =				backbone
            A_						  =				use_pretrained_backbone
            A_						  =				dilation
            # Hungarian matcher
            A_						  =				class_cost
            A_						  =				bbox_cost
            A_						  =				giou_cost
            # Loss coefficients
            A_						  =				mask_loss_coefficient
            A_						  =				dice_loss_coefficient
            A_						  =				bbox_loss_coefficient
            A_						  =				giou_loss_coefficient
            A_						  =				eos_coefficient
            super().__init__(is_encoder_decoder=UpperCAmelCase						,			**UpperCAmelCase	)





      @property
      def 		__A	(   self							:				Dict	):
            return self.encoder_attention_heads





      @property
      def 		__A	(   self							:				List[Any]	):
            return self.d_model





      @classmethod
      def 		__A	(   cls							:				Dict						,			UpperCAmelCase							:				PretrainedConfig						,			**UpperCAmelCase							:				Any	):
            return cls(backbone_config=UpperCAmelCase						,			**UpperCAmelCase	)







      def 		__A	(   self							:				int	):
            A_						  =				copy.deepcopy(self.__dict__	)
            if output["backbone_config"] is not None:
                  A_						  =				self.backbone_config.to_dict()
            A_						  =				self.__class__.model_type
            return output




class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Tuple      		=       version.parse('1.11'				)





      @property
      def 		__A	(   self							:				List[str]	):
            return OrderedDict(
                [
                    ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                    ("pixel_mask", {0: "batch"}),
                ]	)





      @property
      def 		__A	(   self							:				Tuple	):
            return 1E-5







      @property
      def 		__A	(   self							:				Union[str, Any]	):
            return 12

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

import datasets


_A  =	"\\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"

_A  =	"\\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"

_A  =	"\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       _lowerCAmelCase   (      datasets.Metric			):




				def 	__a		(   self	)							-> str:
							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							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							)							-> Any:
							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_         =      TER(
							    normalized=_UpperCamelCase							,							no_punct=_UpperCamelCase							,							asian_support=_UpperCamelCase							,							case_sensitive=_UpperCamelCase							,							)
							lowerCAmelCase_         =      sb_ter.corpus_score(_UpperCamelCase							,							_UpperCamelCase	)

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



