

'''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						:  List[str]							       =				[
    "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						:  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       snake_case   (	) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		bootstrap_aggregation=snake_case					,		rouge_keys=['rouge2', 'rougeL']			)
    assert isinstance(snake_case					,		snake_case			)
    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		bootstrap_aggregation=snake_case					,		rouge_keys=['rouge2']			)
    assert (
        pd.DataFrame(no_aggregation['rouge2']			).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra['rouge2']			).fmeasure.mean()
    )


def       snake_case   (	) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							'rougeLsum'
    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		newline_sep=snake_case					,		rouge_keys=[k]			)[k]
    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		newline_sep=snake_case					,		rouge_keys=[k]			)[k]
    assert score > score_no_sep


def       snake_case   (	) ->  Any:


    """simple docstring"""


    lowerCAmelCase 						=							['rouge1', 'rouge2', 'rougeL']
    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		newline_sep=snake_case					,		rouge_keys=snake_case			)
    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		newline_sep=snake_case					,		rouge_keys=snake_case			)
    assert score_sep == score_no_sep


def       snake_case   (	) ->  Tuple:


    """simple docstring"""


    lowerCAmelCase 						=							[
        '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 						=							[
        '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(snake_case					,		snake_case					,		newline_sep=snake_case			) == calculate_rouge(snake_case					,		snake_case					,		newline_sep=snake_case			)


def       snake_case   (	) ->  Optional[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							[
        '" "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 						=							[
        ' 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 						=							calculate_rouge(snake_case					,		snake_case					,		rouge_keys=['rougeLsum']					,		newline_sep=snake_case			)['rougeLsum']
    lowerCAmelCase 						=							calculate_rouge(snake_case					,		snake_case					,		rouge_keys=['rougeLsum']			)['rougeLsum']
    assert new_score > prev_score


def       snake_case   (	) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							Path('examples/seq2seq/test_data/wmt_en_ro'			)
    lowerCAmelCase 						=							calculate_rouge_path(data_dir.joinpath('test.source'			)					,		data_dir.joinpath('test.target'			)			)
    assert isinstance(snake_case					,		snake_case			)
    lowerCAmelCase 						=							calculate_rouge_path(
        data_dir.joinpath('test.source'			)					,		data_dir.joinpath('test.target'			)					,		bootstrap_aggregation=snake_case			)
    assert isinstance(snake_case					,		snake_case			)








'''simple docstring'''



import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def       snake_case   (	snake_case      : Any			) ->  Any:


    """simple docstring"""


    return 1.0 / (1.0 + np.exp(-_outputs			))


def       snake_case   (	snake_case      : Dict			) ->  str:


    """simple docstring"""


    lowerCAmelCase 						=							np.max(_outputs					,		axis=-1					,		keepdims=snake_case			)
    lowerCAmelCase 						=							np.exp(_outputs - maxes			)
    return shifted_exp / shifted_exp.sum(axis=-1					,		keepdims=snake_case			)



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       List[str]				=     '''sigmoid'''
  SCREAMING_SNAKE_CASE			:       List[Any]				=     '''softmax'''
  SCREAMING_SNAKE_CASE			:       Optional[Any]				=     '''none'''



@add_end_docstrings(
    a_     , R'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    '''     , )
class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     False
  SCREAMING_SNAKE_CASE			:       Dict				=     ClassificationFunction.NONE

  def __init__(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__(**_SCREAMING_SNAKE_CASE	)

      self.check_model_type(
          TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
          if self.framework == 'tf'
          else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=""      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							tokenizer_kwargs

      lowerCAmelCase 						=							{}
      if hasattr(self.model.config      , 'return_all_scores'	) and return_all_scores is None:
          lowerCAmelCase 						=							self.model.config.return_all_scores

      if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) or top_k is None:
          lowerCAmelCase 						=							top_k
          lowerCAmelCase 						=							False
      elif return_all_scores is not None:
          warnings.warn(
              '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
              ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'      , _SCREAMING_SNAKE_CASE      , )
          if return_all_scores:
              lowerCAmelCase 						=							None
          else:
              lowerCAmelCase 						=							1

      if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          lowerCAmelCase 						=							ClassificationFunction[function_to_apply.upper()]

      if function_to_apply is not None:
          lowerCAmelCase 						=							function_to_apply
      return preprocess_params, {}, postprocess_params

  def __call__(				self      , *_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							super().__call__(*_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      # TODO try and retrieve it in a nicer way from _sanitize_parameters.
      lowerCAmelCase 						=							'top_k' not in kwargs
      if isinstance(args[0]      , _SCREAMING_SNAKE_CASE	) and _legacy:
          # This pipeline is odd, and return a list when single item is run
          return [result]
      else:
          return result

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.framework
      if isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          return self.tokenizer(**_SCREAMING_SNAKE_CASE      , return_tensors=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      elif isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	) and len(_SCREAMING_SNAKE_CASE	) == 1 and isinstance(inputs[0]      , _SCREAMING_SNAKE_CASE	) and len(inputs[0]	) == 2:
          # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
          return self.tokenizer(
              text=inputs[0][0]      , text_pair=inputs[0][1]      , return_tensors=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)
      elif isinstance(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):
          # This is likely an invalid usage of the pipeline attempting to pass text pairs.
          raise ValueError(
              'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
              ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'	)
      return self.tokenizer(_SCREAMING_SNAKE_CASE      , return_tensors=_SCREAMING_SNAKE_CASE      , **_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      return self.model(**_SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE=None      , _SCREAMING_SNAKE_CASE=1      , _SCREAMING_SNAKE_CASE=True	):





      '''simple docstring'''

      if function_to_apply is None:
          if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
              lowerCAmelCase 						=							ClassificationFunction.SIGMOID
          elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
              lowerCAmelCase 						=							ClassificationFunction.SOFTMAX
          elif hasattr(self.model.config      , 'function_to_apply'	) and function_to_apply is None:
              lowerCAmelCase 						=							self.model.config.function_to_apply
          else:
              lowerCAmelCase 						=							ClassificationFunction.NONE

      lowerCAmelCase 						=							model_outputs['logits'][0]
      lowerCAmelCase 						=							outputs.numpy()

      if function_to_apply == ClassificationFunction.SIGMOID:
          lowerCAmelCase 						=							sigmoid(_SCREAMING_SNAKE_CASE	)
      elif function_to_apply == ClassificationFunction.SOFTMAX:
          lowerCAmelCase 						=							softmax(_SCREAMING_SNAKE_CASE	)
      elif function_to_apply == ClassificationFunction.NONE:
          lowerCAmelCase 						=							outputs
      else:
          raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'	)

      if top_k == 1 and _legacy:
          return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

      lowerCAmelCase 						=							[
          {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(_SCREAMING_SNAKE_CASE	)
      ]
      if not _legacy:
          dict_scores.sort(key=lambda _SCREAMING_SNAKE_CASE	: x["score"]      , reverse=_SCREAMING_SNAKE_CASE	)
          if top_k is not None:
              lowerCAmelCase 						=							dict_scores[:top_k]
      return dict_scores





