import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import MaskFormerSwinBackbone
      from transformers.models.maskformer import MaskFormerSwinModel


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Dict  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Union[str, Any]=13  ,     __magic_name__ :       Optional[Any]=32  ,     __magic_name__ :       Union[str, Any]=2  ,     __magic_name__ :       Dict=3  ,     __magic_name__ :       List[Any]=16  ,     __magic_name__ :       Optional[Any]=[1, 2, 1]  ,     __magic_name__ :       int=[2, 2, 4]  ,     __magic_name__ :       List[Any]=2  ,     __magic_name__ :       Optional[Any]=2.0  ,     __magic_name__ :       Any=True  ,     __magic_name__ :       Optional[int]=0.0  ,     __magic_name__ :       Optional[int]=0.0  ,     __magic_name__ :       Any=0.1  ,     __magic_name__ :       Optional[int]="gelu"  ,     __magic_name__ :       Any=False  ,     __magic_name__ :       int=True  ,     __magic_name__ :       Tuple=0.02  ,     __magic_name__ :       List[str]=1e-5  ,     __magic_name__ :       Union[str, Any]=True  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       int=10  ,     __magic_name__ :       List[Any]=8  ,     __magic_name__ :       List[str]=["stage1", "stage2", "stage3"]  ,     __magic_name__ :       Any=[1, 2, 3]  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  image_size
        SCREAMING_SNAKE_CASE_          =  patch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  embed_dim
        SCREAMING_SNAKE_CASE_          =  depths
        SCREAMING_SNAKE_CASE_          =  num_heads
        SCREAMING_SNAKE_CASE_          =  window_size
        SCREAMING_SNAKE_CASE_          =  mlp_ratio
        SCREAMING_SNAKE_CASE_          =  qkv_bias
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  drop_path_rate
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  use_absolute_embeddings
        SCREAMING_SNAKE_CASE_          =  patch_norm
        SCREAMING_SNAKE_CASE_          =  layer_norm_eps
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  is_training
        SCREAMING_SNAKE_CASE_          =  scope
        SCREAMING_SNAKE_CASE_          =  use_labels
        SCREAMING_SNAKE_CASE_          =  type_sequence_label_size
        SCREAMING_SNAKE_CASE_          =  encoder_stride
        SCREAMING_SNAKE_CASE_          =  out_features
        SCREAMING_SNAKE_CASE_          =  out_indices







  def    __A     (							self :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

        SCREAMING_SNAKE_CASE_          =  None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size]  ,     self.type_sequence_label_size   )

        SCREAMING_SNAKE_CASE_          =  self.get_config()

        return config, pixel_values, labels







  def    __A     (							self :       List[Any]   )  ->	int:
        return MaskFormerSwinConfig(
            image_size=self.image_size  ,     patch_size=self.patch_size  ,     num_channels=self.num_channels  ,     embed_dim=self.embed_dim  ,     depths=self.depths  ,     num_heads=self.num_heads  ,     window_size=self.window_size  ,     mlp_ratio=self.mlp_ratio  ,     qkv_bias=self.qkv_bias  ,     hidden_dropout_prob=self.hidden_dropout_prob  ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,     drop_path_rate=self.drop_path_rate  ,     hidden_act=self.hidden_act  ,     use_absolute_embeddings=self.use_absolute_embeddings  ,     path_norm=self.patch_norm  ,     layer_norm_eps=self.layer_norm_eps  ,     initializer_range=self.initializer_range  ,     encoder_stride=self.encoder_stride  ,     out_features=self.out_features  ,     out_indices=self.out_indices  ,     )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  MaskFormerSwinModel(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
        SCREAMING_SNAKE_CASE_          =  int(config.embed_dim * 2 ** (len(config.depths   ) - 1)   )

        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, expected_seq_len, expected_dim)   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  MaskFormerSwinBackbone(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps   )  ,     len(config.out_features   )   )
        self.parent.assertListEqual(list(result.feature_maps[0].shape   )  ,     [13, 16, 16, 16]   )

        # verify channels
        self.parent.assertEqual(len(model.channels   )  ,     len(config.out_features   )   )
        self.parent.assertListEqual(model.channels  ,     [16, 32, 64]   )

        # verify ValueError
        with self.parent.assertRaises(__magic_name__   ):
              SCREAMING_SNAKE_CASE_          =  ["stem"]
              SCREAMING_SNAKE_CASE_          =  MaskFormerSwinBackbone(config=__magic_name__   )







  def    __A     (							self :       Optional[int]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  config_and_inputs
        SCREAMING_SNAKE_CASE_          =  {"pixel_values": pixel_values}
        return config, inputs_dict



@require_torch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						(
      (
          MaskFormerSwinModel,
          MaskFormerSwinBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase__     =						{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False







  def    __A     (							self :       Any   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  MaskFormerSwinModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     embed_dim=37   )







  @require_torch_multi_gpu
  @unittest.skip(
      reason=(
          "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
          " `nn.DataParallel`"
      )   )
  def    __A     (							self :       Optional[int]   )  ->	List[Any]:
        pass







  def    __A     (							self :       Optional[int]   )  ->	Tuple:
        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







  def    __A     (							self :       Any   )  ->	Optional[int]:
        return







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__magic_name__   )







  def    __A     (							self :       str   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__magic_name__   )







  @unittest.skip("Swin does not use inputs_embeds"   )
  def    __A     (							self :       Optional[int]   )  ->	str:
        pass







  @unittest.skip("Swin does not support feedforward chunking"   )
  def    __A     (							self :       Union[str, Any]   )  ->	str:
        pass







  def    __A     (							self :       str   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )
              self.assertIsInstance(model.get_input_embeddings()  ,     (nn.Module)   )
              SCREAMING_SNAKE_CASE_          =  model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__magic_name__  ,     nn.Linear   )   )







  def    __A     (							self :       List[Any]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  inspect.signature(model.forward   )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE_          =  [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE_          =  ["pixel_values"]
              self.assertListEqual(arg_names[:1]  ,     __magic_name__   )







  @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions"   )
  def    __A     (							self :       str   )  ->	int:
        pass







  @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"   )
  def    __A     (							self :       Optional[Any]   )  ->	Union[str, Any]:
        pass







  def    __A     (							self :       Dict  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       int   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(**self._prepare_for_class(__magic_name__  ,     __magic_name__   )   )

        SCREAMING_SNAKE_CASE_          =  outputs.hidden_states

        SCREAMING_SNAKE_CASE_          =  getattr(
            self.model_tester  ,     "expected_num_hidden_layers"  ,     len(self.model_tester.depths   ) + 1   )
        self.assertEqual(len(__magic_name__   )  ,     __magic_name__   )

        # Swin has a different seq_length
        SCREAMING_SNAKE_CASE_          =  (
            config.patch_size
            if isinstance(config.patch_size  ,     collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        SCREAMING_SNAKE_CASE_          =  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )  ,     [num_patches, self.model_tester.embed_dim]  ,     )







  def    __A     (							self :       Dict   )  ->	str:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        SCREAMING_SNAKE_CASE_          =  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,     collections.abc.Iterable   )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  True
              self.check_hidden_states_output(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_          =  True

              self.check_hidden_states_output(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_          =  3

        SCREAMING_SNAKE_CASE_          =  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,     collections.abc.Iterable   )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        SCREAMING_SNAKE_CASE_          =  (
            config.patch_size
            if isinstance(config.patch_size  ,     collections.abc.Iterable   )
            else (config.patch_size, config.patch_size)
        )

        SCREAMING_SNAKE_CASE_          =  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        SCREAMING_SNAKE_CASE_          =  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  True
              self.check_hidden_states_output(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     (padded_height, padded_width)   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE_          =  True
              self.check_hidden_states_output(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     (padded_height, padded_width)   )







  @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints"   )
  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        pass







  @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"   )
  def    __A     (							self :       str   )  ->	Tuple:
        pass







  @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"   )
  def    __A     (							self :       int   )  ->	Any:
        pass







  def    __A     (							self :       Any   )  ->	Any:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        def set_nan_tensor_to_zero(__magic_name__ :       Dict   ):
              SCREAMING_SNAKE_CASE_          =  0
              return t

        def check_equivalence(__magic_name__ :       List[str]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[str]={}   ):
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE_          =  model(**__magic_name__  ,     return_dict=__magic_name__  ,     **__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  model(**__magic_name__  ,     return_dict=__magic_name__  ,     **__magic_name__   ).to_tuple()

                    def recursive_check(__magic_name__ :       Optional[Any]  ,     __magic_name__ :       Any   ):
                          if isinstance(__magic_name__  ,     (List, Tuple)   ):
                                for tuple_iterable_value, dict_iterable_value in zip(__magic_name__  ,     __magic_name__   ):
                                      recursive_check(__magic_name__  ,     __magic_name__   )
                          elif isinstance(__magic_name__  ,     __magic_name__   ):
                                for tuple_iterable_value, dict_iterable_value in zip(
                                    tuple_object.values()  ,     dict_object.values()   ):
                                      recursive_check(__magic_name__  ,     __magic_name__   )
                          elif tuple_object is None:
                                return
                          else:
                                self.assertTrue(
                                    torch.allclose(
                                        set_nan_tensor_to_zero(__magic_name__   )  ,     set_nan_tensor_to_zero(__magic_name__   )  ,     atol=1e-5   )  ,     msg=(
                                        "Tuple and dict output are not equal. Difference:"
                                        F''' {torch.max(torch.abs(tuple_object - dict_object   )   )}. Tuple has `nan`:'''
                                        F''' {torch.isnan(__magic_name__   ).any()} and `inf`: {torch.isinf(__magic_name__   )}. Dict has'''
                                        F''' `nan`: {torch.isnan(__magic_name__   ).any()} and `inf`: {torch.isinf(__magic_name__   )}.'''
                                    )  ,     )

                    recursive_check(__magic_name__  ,     __magic_name__   )

        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )
              model.to(__magic_name__   )
              model.eval()

              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__   )
              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__   )
              check_equivalence(__magic_name__  ,     __magic_name__  ,     __magic_name__   )

              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__  ,     return_labels=__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__  ,     return_labels=__magic_name__   )
              check_equivalence(__magic_name__  ,     __magic_name__  ,     __magic_name__   )

              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__   )
              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__   )
              check_equivalence(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     {"output_hidden_states": True}   )

              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__  ,     return_labels=__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__  ,     return_labels=__magic_name__   )
              check_equivalence(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     {"output_hidden_states": True}   )



@require_torch
class   lowerCamelCase					(unittest.TestCase  ,      SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						(MaskFormerSwinBackbone,) if is_torch_available() else ()
  lowerCamelCase__     =						MaskFormerSwinConfig







  def    __A     (							self :       Union[str, Any]   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  MaskFormerSwinModelTester(self   )







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()
        SCREAMING_SNAKE_CASE_          =  inputs_dict["pixel_values"].shape[0]

        for backbone_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  backbone_class(__magic_name__   )
              backbone.to(__magic_name__   )
              backbone.eval()

              SCREAMING_SNAKE_CASE_          =  backbone(**__magic_name__   )

              # Test default outputs and verify feature maps
              self.assertIsInstance(outputs.feature_maps  ,     __magic_name__   )
              self.assertTrue(len(outputs.feature_maps   ) == len(backbone.channels   )   )
              for feature_map, n_channels in zip(outputs.feature_maps  ,     backbone.channels   ):
                    self.assertTrue(feature_map.shape[:2]  ,     (batch_size, n_channels)   )
              self.assertIsNone(outputs.hidden_states   )
              self.assertIsNone(outputs.attentions   )

              # Test output_hidden_states=True
              SCREAMING_SNAKE_CASE_          =  backbone(**__magic_name__  ,     output_hidden_states=__magic_name__   )
              self.assertIsNotNone(outputs.hidden_states   )
              self.assertTrue(len(outputs.hidden_states   )  ,     len(backbone.stage_names   )   )
              # We skip the stem layer
              for hidden_states, n_channels in zip(outputs.hidden_states[1:]  ,     backbone.channels   ):
                    for hidden_state in hidden_states:
                          # Hidden states are in the format (batch_size, (height * width), n_channels)
                          SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  hidden_state.shape
                          self.assertTrue((h_batch_size, h_n_channels)  ,     (batch_size, n_channels)   )

            # Test output_attentions=True
              if self.has_attentions:
                    SCREAMING_SNAKE_CASE_          =  backbone(**__magic_name__  ,     output_attentions=__magic_name__   )
                    self.assertIsNotNone(outputs.attentions   )



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


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "rougeLsum"
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      assert score > score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  ["rouge1", "rouge2", "rougeL"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      assert score_sep == score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "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 .",
      ]
      SCREAMING_SNAKE_CASE_          =  [
          "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(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				) == calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "\" \"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\" "
      ]
      SCREAMING_SNAKE_CASE_          =  [
          " 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 ."
      ]

      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]     ,      newline_sep=__UpperCamelCase				)["rougeLsum"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]				)["rougeLsum"]
      assert new_score > prev_score





def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  Path("examples/seq2seq/test_data/wmt_en_ro"				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(
          data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)     ,      bootstrap_aggregation=__UpperCamelCase				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)


