


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


A      :    List[Any]		=					{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

A      :    str		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"maskformer"
       A__											=			{"hidden_size": "mask_feature_size"}
       A__											=			["resnet", "swin"]
       A__											=			["detr"]





       def __init__(   self							:			Dict					,      __lowerCamelCase							:			int = 256					,      __lowerCamelCase							:			int = 256					,      __lowerCamelCase							:			float = 0.1					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			Optional[Dict] = None					,      __lowerCamelCase							:			Optional[Dict] = None					,      __lowerCamelCase							:			float = 0.0_2					,      __lowerCamelCase							:			float = 1.0					,      __lowerCamelCase							:			float = 1.0					,      __lowerCamelCase							:			float = 1.0					,      __lowerCamelCase							:			float = 2_0.0					,      __lowerCamelCase							:			Optional[bool] = None					,      **__lowerCamelCase							:			Tuple					,      ):


             '''simple docstring'''



             if backbone_config is None:
                   # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                   lowerCamelCase__		:				Any    =  SwinConfig(
                       image_size=384					,      in_channels=3					,      patch_size=4					,      embed_dim=128					,      depths=[2, 2, 18, 2]					,      num_heads=[4, 8, 16, 32]					,      window_size=12					,      drop_path_rate=0.3					,      out_features=["stage1", "stage2", "stage3", "stage4"]					,      )

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				Optional[int]    =  backbone_config.pop("model_type"       )
                   lowerCamelCase__		:				Optional[int]    =  CONFIG_MAPPING[backbone_model_type]
                   lowerCamelCase__		:				Optional[int]    =  config_class.from_dict(__lowerCamelCase       )

             # verify that the backbone is supported
             if backbone_config.model_type not in self.backbones_supported:
                   logger.warning_once(
                       f"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
                       f"Supported model types: {','.join(self.backbones_supported       )}"       )

             if decoder_config is None:
                   # fall back to https://huggingface.co/facebook/detr-resnet-50
                   lowerCamelCase__		:				str    =  DetrConfig()
             else:
                   # verify that the decoder is supported
                   lowerCamelCase__		:				Tuple    =  (
                       decoder_config.pop("model_type"       ) if isinstance(__lowerCamelCase					,      __lowerCamelCase       ) else decoder_config.model_type
                   )
                   if decoder_type not in self.decoders_supported:
                         raise ValueError(
                             f"Transformer Decoder {decoder_type} not supported, please use one of"
                             f" {','.join(self.decoders_supported       )}"       )
                   if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                         lowerCamelCase__		:				Optional[int]    =  CONFIG_MAPPING[decoder_type]
                         lowerCamelCase__		:				Optional[Any]    =  config_class.from_dict(__lowerCamelCase       )

             lowerCamelCase__		:				Optional[int]    =  backbone_config
             lowerCamelCase__		:				Optional[int]    =  decoder_config
             # main feature dimension for the model
             lowerCamelCase__		:				Union[str, Any]    =  fpn_feature_size
             lowerCamelCase__		:				Optional[int]    =  mask_feature_size
             # initializer
             lowerCamelCase__		:				Optional[int]    =  init_std
             lowerCamelCase__		:				List[str]    =  init_xavier_std
             # Hungarian matcher && loss
             lowerCamelCase__		:				List[str]    =  cross_entropy_weight
             lowerCamelCase__		:				Optional[Any]    =  dice_weight
             lowerCamelCase__		:				List[Any]    =  mask_weight
             lowerCamelCase__		:				List[Any]    =  use_auxiliary_loss
             lowerCamelCase__		:				Optional[int]    =  no_object_weight
             lowerCamelCase__		:				str    =  output_auxiliary_logits

             lowerCamelCase__		:				Optional[Any]    =  self.decoder_config.encoder_attention_heads
             lowerCamelCase__		:				Union[str, Any]    =  self.decoder_config.num_hidden_layers
             super().__init__(**__lowerCamelCase       )





       @classmethod
       def 						lowerCAmelCase     (   cls							:			Dict					,      __lowerCamelCase							:			PretrainedConfig					,      __lowerCamelCase							:			PretrainedConfig					,      **__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             return cls(
                 backbone_config=__lowerCamelCase					,      decoder_config=__lowerCamelCase					,      **__lowerCamelCase					,      )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  copy.deepcopy(self.__dict__       )
             lowerCamelCase__		:				Any    =  self.backbone_config.to_dict()
             lowerCamelCase__		:				int    =  self.decoder_config.to_dict()
             lowerCamelCase__		:				List[Any]    =  self.__class__.model_type
             return output






from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A      :    Optional[int]		=					[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A      :    List[Any]		=					[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2", "rougeL"]						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  calculate_rouge(_A    ,					_A    ,					bootstrap_aggregation=_A    ,					rouge_keys=["rouge2"]						)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]						).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]						).fmeasure.mean()
      )

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Any    =  "rougeLsum"
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      lowerCamelCase__		:				str    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=[k]						)[k]
      assert score > score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  ["rouge1", "rouge2", "rougeL"]
      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      lowerCamelCase__		:				Any    =  calculate_rouge(_A    ,					_A    ,					newline_sep=_A    ,					rouge_keys=_A						)
      assert score_sep == score_no_sep

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      lowerCamelCase__		:				Tuple    =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(_A    ,					_A    ,					newline_sep=_A						) == calculate_rouge(_A    ,					_A    ,					newline_sep=_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      lowerCamelCase__		:				str    =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      lowerCamelCase__		:				Union[str, Any]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]    ,					newline_sep=_A						)["rougeLsum"]
      lowerCamelCase__		:				List[str]    =  calculate_rouge(_A    ,					_A    ,					rouge_keys=["rougeLsum"]						)["rougeLsum"]
      assert new_score > prev_score


def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  Path("examples/seq2seq/test_data/wmt_en_ro"						)
      lowerCamelCase__		:				Any    =  calculate_rouge_path(data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)						)
      assert isinstance(_A    ,					_A						)
      lowerCamelCase__		:				str    =  calculate_rouge_path(
          data_dir.joinpath("test.source"						)    ,					data_dir.joinpath("test.target"						)    ,					bootstrap_aggregation=_A						)
      assert isinstance(_A    ,					_A						)


