

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor






class 							A_ (		_a							,	_a					):
     _lowercase			:     List[str]								=	'pixel_values'
     _lowercase			:     Optional[int]								=	False
     _lowercase			:     Any								=	TimmBackboneConfig


     def __init__(							self			:		str			,  UpperCAmelCase			:		List[Any]			,  **UpperCAmelCase			:		Optional[Any]					)							->	int:
      requires_backends(self			,  'timm'					)
      super().__init__(UpperCamelCase__					)
      __lowerCAmelCase:   List[str]   	=      config

      if config.backbone is None:
       raise ValueError('backbone is not set in the config. Please set it to a timm model name.'					)

      if config.backbone not in timm.list_models():
       raise ValueError(F'''backbone {config.backbone} is not supported by timm.'''					)

      if hasattr(UpperCamelCase__			,  'out_features'					) and config.out_features is not None:
       raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'					)

      __lowerCAmelCase:   List[str]   	=      getattr(UpperCamelCase__			,  'use_pretrained_backbone'			,  UpperCamelCase__					)
      if pretrained is None:
       raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'					)

      # We just take the final layer by default. This matches the default for the transformers models.
      __lowerCAmelCase:   Tuple   	=      config.out_indices if getattr(UpperCamelCase__			,  'out_indices'			,  UpperCamelCase__					) is not None else (-1,)

      __lowerCAmelCase:   List[Any]   	=      timm.create_model(
          config.backbone			,  pretrained=UpperCamelCase__			,  features_only=config.features_only			,  in_chans=config.num_channels			,  out_indices=UpperCamelCase__			,  **UpperCamelCase__			,  )
      # These are used to control the output of the model when called. If output_hidden_states is True, then
      # return_layers is modified to include all layers.
      __lowerCAmelCase:   Tuple   	=      self._backbone.return_layers
      __lowerCAmelCase:   int   	=      {layer['module']: str(UpperCamelCase__					) for i, layer in enumerate(self._backbone.feature_info.info					)}
      super()._init_backbone(UpperCamelCase__					)


     @classmethod
     def   UpperCAmelCase  (							cls			:		List[Any]			,  UpperCAmelCase			:		Any			,  *UpperCAmelCase			:		List[str]			,  **UpperCAmelCase			:		Optional[Any]					)							->	int:
      requires_backends(cls			,  ['vision', 'timm']					)
      from ...models.timm_backbone import TimmBackboneConfig

      __lowerCAmelCase:   List[str]   	=      kwargs.pop('config'			,  TimmBackboneConfig()					)

      __lowerCAmelCase:   Optional[Any]   	=      kwargs.pop('use_timm_backbone'			,  UpperCamelCase__					)
      if not use_timm:
       raise ValueError('use_timm_backbone must be True for timm backbones'					)

      __lowerCAmelCase:   List[str]   	=      kwargs.pop('num_channels'			,  config.num_channels					)
      __lowerCAmelCase:   Union[str, Any]   	=      kwargs.pop('features_only'			,  config.features_only					)
      __lowerCAmelCase:   Optional[Any]   	=      kwargs.pop('use_pretrained_backbone'			,  config.use_pretrained_backbone					)
      __lowerCAmelCase:   Tuple   	=      kwargs.pop('out_indices'			,  config.out_indices					)
      __lowerCAmelCase:   Dict   	=      TimmBackboneConfig(
          backbone=UpperCamelCase__			,  num_channels=UpperCamelCase__			,  features_only=UpperCamelCase__			,  use_pretrained_backbone=UpperCamelCase__			,  out_indices=UpperCamelCase__			,  )
      return super()._from_config(UpperCamelCase__			,  **UpperCamelCase__					)


     def   UpperCAmelCase  (							self			:		Optional[int]			,  UpperCAmelCase			:		Dict					)							->	Any:
      pass


     def   UpperCAmelCase  (							self			:		str			,  UpperCAmelCase			:		str			,  UpperCAmelCase			:		Optional[int]=None			,  UpperCAmelCase			:		Optional[Any]=None			,  UpperCAmelCase			:		Optional[Any]=None			,  **UpperCAmelCase			:		Union[str, Any]					)							->	str:
      __lowerCAmelCase:   int   	=      return_dict if return_dict is not None else self.config.use_return_dict
      __lowerCAmelCase:   List[str]   	=      (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      __lowerCAmelCase:   List[str]   	=      output_attentions if output_attentions is not None else self.config.output_attentions

      if output_attentions:
       raise ValueError('Cannot output attentions for timm backbones at the moment'					)

      if output_hidden_states:
       # We modify the return layers to include all the stages of the backbone
       __lowerCAmelCase:   Tuple   	=      self._all_layers
       __lowerCAmelCase:   Dict   	=      self._backbone(UpperCamelCase__			,  **UpperCamelCase__					)
       __lowerCAmelCase:   Tuple   	=      self._return_layers
       __lowerCAmelCase:   Tuple   	=      tuple(hidden_states[i] for i in self.out_indices					)
      else:
       __lowerCAmelCase:   List[Any]   	=      self._backbone(UpperCamelCase__			,  **UpperCamelCase__					)
       __lowerCAmelCase:   List[Any]   	=      None

      __lowerCAmelCase:   List[Any]   	=      tuple(UpperCamelCase__					)
      __lowerCAmelCase:   Optional[Any]   	=      tuple(UpperCamelCase__					) if hidden_states is not None else None

      if not return_dict:
       __lowerCAmelCase:   Dict   	=      (feature_maps,)
       if output_hidden_states:
        __lowerCAmelCase:   List[Any]   	=      output + (hidden_states,)
       return output

      return BackboneOutput(feature_maps=UpperCamelCase__			,  hidden_states=UpperCamelCase__			,  attentions=UpperCamelCase__					)










'''simple docstring'''


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase	:       List[str]      	=	"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

_lowerCamelCase	:       Optional[int]      	=	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

_lowerCamelCase	:       str      	=	"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"

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






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
               {
                   'predictions': datasets.Value('string'  ,						id='sequence' ),
                   'references': datasets.Value('string'  ,						id='sequence' ),
               } )  ,						codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']  ,						reference_urls=[
               'https://en.wikipedia.org/wiki/ROUGE_(metric)',
               'https://github.com/google-research/google-research/tree/master/rouge',
           ]  ,						)






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







       if rouge_types is None:
          UpperCamelCase					=  ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

       UpperCamelCase					=  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__  ,						use_stemmer=UpperCamelCase__ )
       if use_aggregator:
          UpperCamelCase					=  scoring.BootstrapAggregator()
       else:
          UpperCamelCase					=  []

       for ref, pred in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  scorer.score(UpperCamelCase__  ,						UpperCamelCase__ )
          if use_aggregator:
             aggregator.add_scores(UpperCamelCase__ )
          else:
             scores.append(UpperCamelCase__ )

       if use_aggregator:
          UpperCamelCase					=  aggregator.aggregate()
       else:
          UpperCamelCase					=  {}
          for key in scores[0]:
             UpperCamelCase					=  [score[key] for score in scores]

       return result

