



'''simple docstring'''





from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


_UpperCamelCase											=		[
    '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.',
]

_UpperCamelCase											=		[
    '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_	(				) -> Tuple:
  __lowerCamelCase			:			Dict       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,bootstrap_aggregation=_lowerCAmelCase			,rouge_keys=['rouge2', 'rougeL']			)
  assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			)
  __lowerCamelCase			:			Any       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,bootstrap_aggregation=_lowerCAmelCase			,rouge_keys=['rouge2']			)
  assert (
      pd.DataFrame(no_aggregation['rouge2']			).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra['rouge2']			).fmeasure.mean()
  )

def 			a_	(				) -> List[str]:
  __lowerCamelCase			:			str       			=					'rougeLsum'
  __lowerCamelCase			:			List[Any]       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,newline_sep=_lowerCAmelCase			,rouge_keys=[k]			)[k]
  __lowerCamelCase			:			Tuple       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,newline_sep=_lowerCAmelCase			,rouge_keys=[k]			)[k]
  assert score > score_no_sep

def 			a_	(				) -> Optional[int]:
  __lowerCamelCase			:			int       			=					['rouge1', 'rouge2', 'rougeL']
  __lowerCamelCase			:			Union[str, Any]       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,newline_sep=_lowerCAmelCase			,rouge_keys=_lowerCAmelCase			)
  __lowerCamelCase			:			Union[str, Any]       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,newline_sep=_lowerCAmelCase			,rouge_keys=_lowerCAmelCase			)
  assert score_sep == score_no_sep

def 			a_	(				) -> Tuple:
  __lowerCamelCase			:			Optional[int]       			=					[
      '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			:			List[str]       			=					[
      '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(_lowerCAmelCase			,_lowerCAmelCase			,newline_sep=_lowerCAmelCase			) == calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,newline_sep=_lowerCAmelCase			)

def 			a_	(				) -> Optional[Any]:
  __lowerCamelCase			:			Tuple       			=					[
      '" "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(_lowerCAmelCase			,_lowerCAmelCase			,rouge_keys=['rougeLsum']			,newline_sep=_lowerCAmelCase			)['rougeLsum']
  __lowerCamelCase			:			str       			=					calculate_rouge(_lowerCAmelCase			,_lowerCAmelCase			,rouge_keys=['rougeLsum']			)['rougeLsum']
  assert new_score > prev_score


def 			a_	(				) -> Any:
  __lowerCamelCase			:			Dict       			=					Path('examples/seq2seq/test_data/wmt_en_ro'			)
  __lowerCamelCase			:			int       			=					calculate_rouge_path(data_dir.joinpath('test.source'			)			,data_dir.joinpath('test.target'			)			)
  assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			)
  __lowerCamelCase			:			List[Any]       			=					calculate_rouge_path(
      data_dir.joinpath('test.source'			)			,data_dir.joinpath('test.target'			)			,bootstrap_aggregation=_lowerCAmelCase			)
  assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			)











'''simple docstring'''





import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       DebertaVaForMaskedLM,
       DebertaVaForMultipleChoice,
       DebertaVaForQuestionAnswering,
       DebertaVaForSequenceClassification,
       DebertaVaForTokenClassification,
       DebertaVaModel,
   )
   from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Dict     ,      _a :      int     ,      _a :      Any=13     ,      _a :      Union[str, Any]=7     ,      _a :      int=True     ,      _a :      Optional[int]=True     ,      _a :      Union[str, Any]=True     ,      _a :      Dict=True     ,      _a :      Optional[Any]=99     ,      _a :      List[str]=32     ,      _a :      Union[str, Any]=5     ,      _a :      Optional[Any]=4     ,      _a :      Dict=37     ,      _a :      Dict="gelu"     ,      _a :      Optional[int]=0.1     ,      _a :      Tuple=0.1     ,      _a :      Dict=512     ,      _a :      Tuple=16     ,      _a :      List[str]=2     ,      _a :      Union[str, Any]=0.02     ,      _a :      List[Any]=False     ,      _a :      str=True     ,      _a :      List[Any]="None"     ,      _a :      Dict=3     ,      _a :      Union[str, Any]=4     ,      _a :      Union[str, Any]=None     ,      )   ->   List[str]:
    __lowerCamelCase			:			Any       			=					parent
    __lowerCamelCase			:			List[Any]       			=					batch_size
    __lowerCamelCase			:			str       			=					seq_length
    __lowerCamelCase			:			Dict       			=					is_training
    __lowerCamelCase			:			str       			=					use_input_mask
    __lowerCamelCase			:			Any       			=					use_token_type_ids
    __lowerCamelCase			:			Dict       			=					use_labels
    __lowerCamelCase			:			str       			=					vocab_size
    __lowerCamelCase			:			Optional[Any]       			=					hidden_size
    __lowerCamelCase			:			Union[str, Any]       			=					num_hidden_layers
    __lowerCamelCase			:			Union[str, Any]       			=					num_attention_heads
    __lowerCamelCase			:			str       			=					intermediate_size
    __lowerCamelCase			:			Union[str, Any]       			=					hidden_act
    __lowerCamelCase			:			Tuple       			=					hidden_dropout_prob
    __lowerCamelCase			:			Union[str, Any]       			=					attention_probs_dropout_prob
    __lowerCamelCase			:			Optional[Any]       			=					max_position_embeddings
    __lowerCamelCase			:			Tuple       			=					type_vocab_size
    __lowerCamelCase			:			Dict       			=					type_sequence_label_size
    __lowerCamelCase			:			str       			=					initializer_range
    __lowerCamelCase			:			Tuple       			=					num_labels
    __lowerCamelCase			:			Optional[Any]       			=					num_choices
    __lowerCamelCase			:			Any       			=					relative_attention
    __lowerCamelCase			:			Any       			=					position_biased_input
    __lowerCamelCase			:			Dict       			=					pos_att_type
    __lowerCamelCase			:			Any       			=					scope




  def 		_lowercase	(					self :      Dict  )   ->   str:
    __lowerCamelCase			:			List[str]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size  )

    __lowerCamelCase			:			Optional[Any]       			=					None
    if self.use_input_mask:
      __lowerCamelCase			:			Optional[Any]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      vocab_size=2  )

    __lowerCamelCase			:			Optional[Any]       			=					None
    if self.use_token_type_ids:
      __lowerCamelCase			:			Optional[Any]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.type_vocab_size  )

    __lowerCamelCase			:			Any       			=					None
    __lowerCamelCase			:			Optional[int]       			=					None
    __lowerCamelCase			:			Tuple       			=					None
    if self.use_labels:
      __lowerCamelCase			:			Union[str, Any]       			=					ids_tensor([self.batch_size]     ,      self.type_sequence_label_size  )
      __lowerCamelCase			:			Optional[Any]       			=					ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels  )
      __lowerCamelCase			:			str       			=					ids_tensor([self.batch_size]     ,      self.num_choices  )

    __lowerCamelCase			:			List[str]       			=					self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




  def 		_lowercase	(					self :      List[str]  )   ->   Optional[int]:
    return DebertaVaConfig(
        vocab_size=self.vocab_size     ,      hidden_size=self.hidden_size     ,      num_hidden_layers=self.num_hidden_layers     ,      num_attention_heads=self.num_attention_heads     ,      intermediate_size=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      hidden_dropout_prob=self.hidden_dropout_prob     ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      type_vocab_size=self.type_vocab_size     ,      initializer_range=self.initializer_range     ,      relative_attention=self.relative_attention     ,      position_biased_input=self.position_biased_input     ,      pos_att_type=self.pos_att_type     ,      )




  def 		_lowercase	(					self :      Any     ,      _a :      Optional[Any]  )   ->   Any:
    self.parent.assertListEqual(list(result.loss.size()  )     ,      []  )




  def 		_lowercase	(					self :      List[str]     ,      _a :      Any     ,      _a :      List[str]     ,      _a :      List[Any]     ,      _a :      Dict     ,      _a :      Union[str, Any]     ,      _a :      Tuple     ,      _a :      List[Any]  )   ->   List[str]:
    __lowerCamelCase			:			str       			=					DebertaVaModel(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Optional[Any]       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a  )[0]
    __lowerCamelCase			:			List[Any]       			=					model(_a     ,      token_type_ids=_a  )[0]
    __lowerCamelCase			:			int       			=					model(_a  )[0]

    self.parent.assertListEqual(list(sequence_output.size()  )     ,      [self.batch_size, self.seq_length, self.hidden_size]  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[int]     ,      _a :      Dict     ,      _a :      Optional[int]     ,      _a :      Dict     ,      _a :      Optional[Any]     ,      _a :      Optional[Any]  )   ->   str:
    __lowerCamelCase			:			List[Any]       			=					DebertaVaForMaskedLM(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Optional[int]       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )

    self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)  )




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      List[str]     ,      _a :      str     ,      _a :      Any     ,      _a :      Dict     ,      _a :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      str  )   ->   List[Any]:
    __lowerCamelCase			:			List[str]       			=					self.num_labels
    __lowerCamelCase			:			List[str]       			=					DebertaVaForSequenceClassification(_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			Tuple       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )
    self.parent.assertListEqual(list(result.logits.size()  )     ,      [self.batch_size, self.num_labels]  )
    self.check_loss_output(_a  )




  def 		_lowercase	(					self :      int     ,      _a :      List[str]     ,      _a :      Union[str, Any]     ,      _a :      Any     ,      _a :      int     ,      _a :      Optional[int]     ,      _a :      Tuple     ,      _a :      Tuple  )   ->   Union[str, Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					self.num_labels
    __lowerCamelCase			:			Union[str, Any]       			=					DebertaVaForTokenClassification(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			str       			=					model(_a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a  )
    self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.num_labels)  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Optional[int]     ,      _a :      List[Any]     ,      _a :      Optional[Any]     ,      _a :      List[str]     ,      _a :      Optional[int]     ,      _a :      Union[str, Any]     ,      _a :      Any  )   ->   Tuple:
    __lowerCamelCase			:			int       			=					DebertaVaForQuestionAnswering(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			List[str]       			=					model(
        _a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      start_positions=_a     ,      end_positions=_a     ,      )
    self.parent.assertEqual(result.start_logits.shape     ,      (self.batch_size, self.seq_length)  )
    self.parent.assertEqual(result.end_logits.shape     ,      (self.batch_size, self.seq_length)  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      Any     ,      _a :      List[str]     ,      _a :      str     ,      _a :      str     ,      _a :      List[Any]     ,      _a :      Tuple     ,      _a :      str  )   ->   Optional[int]:
    __lowerCamelCase			:			Union[str, Any]       			=					DebertaVaForMultipleChoice(config=_a  )
    model.to(_a  )
    model.eval()
    __lowerCamelCase			:			List[Any]       			=					input_ids.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			List[str]       			=					token_type_ids.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			Union[str, Any]       			=					input_mask.unsqueeze(1  ).expand(-1     ,      self.num_choices     ,      -1  ).contiguous()
    __lowerCamelCase			:			Optional[int]       			=					model(
        _a     ,      attention_mask=_a     ,      token_type_ids=_a     ,      labels=_a     ,      )
    self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_choices)  )




  def 		_lowercase	(					self :      Any  )   ->   Any:
    __lowerCamelCase			:			Tuple       			=					self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,(
        __lowerCamelCase
    )			,
    )			:			Optional[int]       			=					config_and_inputs
    __lowerCamelCase			:			List[Any]       			=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
    return config, inputs_dict






@require_torch
class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  unittest.TestCase	):
  """simple docstring"""




  a_				   =(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  a_				   =(
      {
          """feature-extraction""": DebertaVaModel,
          """fill-mask""": DebertaVaForMaskedLM,
          """question-answering""": DebertaVaForQuestionAnswering,
          """text-classification""": DebertaVaForSequenceClassification,
          """token-classification""": DebertaVaForTokenClassification,
          """zero-shot""": DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  a_				   =True
  a_				   =False
  a_				   =False
  a_				   =False
  a_				   =False




  def 		_lowercase	(					self :      Tuple  )   ->   Dict:
    __lowerCamelCase			:			str       			=					DebertaVaModelTester(self  )
    __lowerCamelCase			:			Dict       			=					ConfigTester(self     ,      config_class=_a     ,      hidden_size=37  )




  def 		_lowercase	(					self :      int  )   ->   List[str]:
    self.config_tester.run_common_tests()




  def 		_lowercase	(					self :      Any  )   ->   int:
    __lowerCamelCase			:			Any       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_model(*_a  )




  def 		_lowercase	(					self :      Dict  )   ->   Tuple:
    __lowerCamelCase			:			List[str]       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_sequence_classification(*_a  )




  def 		_lowercase	(					self :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			Tuple       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_masked_lm(*_a  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Union[str, Any]       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_question_answering(*_a  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[Any]:
    __lowerCamelCase			:			int       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_token_classification(*_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Tuple       			=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_deberta_for_multiple_choice(*_a  )




  @slow
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __lowerCamelCase			:			Any       			=					DebertaVaModel.from_pretrained(_a  )
      self.assertIsNotNone(_a  )






@require_torch
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  @unittest.skip(reason='Model not available yet'  )
  def 		_lowercase	(					self :      List[str]  )   ->   Dict:
    pass




  @slow
  def 		_lowercase	(					self :      List[str]  )   ->   List[Any]:
    __lowerCamelCase			:			List[Any]       			=					DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'  )

    __lowerCamelCase			:			Optional[Any]       			=					torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
    __lowerCamelCase			:			List[str]       			=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
    with torch.no_grad():
      __lowerCamelCase			:			List[str]       			=					model(_a     ,      attention_mask=_a  )[0]
    # compare the actual values for a slice.
    __lowerCamelCase			:			Tuple       			=					torch.tensor(
        [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]  )
    self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,      _a     ,      atol=1e-4  )     ,      f'{output[:, 1:4, 1:4]}'  )






