



'''simple docstring'''





from sklearn.metrics import recall_score

import datasets


lowercase_		       =  "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


lowercase_		       =  "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


lowercase_		       =  "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"






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



 '''simple docstring'''







 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      return datasets.MetricInfo(
          description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
              {
                  '''predictions''': datasets.Sequence(datasets.Value('''int32'''       )       ),
                  '''references''': datasets.Sequence(datasets.Value('''int32'''       )       ),
              }
              if self.config_name == '''multilabel'''
              else {
                  '''predictions''': datasets.Value('''int32'''       ),
                  '''references''': datasets.Value('''int32'''       ),
              }       ) ,   reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'''] ,   )



 def 					a__  (self ,   A ,   A ,   A=None ,   A=1 ,   A="binary" ,   A=None ,   A="warn" ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  recall_score(
          A ,   A ,   labels=A ,   pos_label=A ,   average=A ,   sample_weight=A ,   zero_division=A ,   )
      return {"recall": float(A       ) if score.size == 1 else score}








'''simple docstring'''





import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
  import tensorflow as tf






class __A			(  enum.Enum						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       0
 __lowerCamelCase    :       Any          =       1
 __lowerCamelCase    :       List[Any]          =       2






@add_end_docstrings(A						)
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       '\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '



 def __init__(self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      super().__init__(*A ,   **A       )
      self.check_model_type(
          TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == '''tf''' else MODEL_FOR_CAUSAL_LM_MAPPING       )
      if "prefix" not in self._preprocess_params:
           # This is very specific. The logic is quite complex and needs to be done
           # as a "default".
           # It also defines both some preprocess_kwargs and generate_kwargs
           # which is why we cannot put them in their respective methods.
           _a      =  None
           if self.model.config.prefix is not None:
                _a      =  self.model.config.prefix
           if prefix is None and self.model.__class__.__name__ in [
               "XLNetLMHeadModel",
               "TransfoXLLMHeadModel",
               "TFXLNetLMHeadModel",
               "TFTransfoXLLMHeadModel",
           ]:
                # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                _a      =  self.XL_PREFIX
           if prefix is not None:
                # Recalculate some generate_kwargs linked to prefix.
                _a  ,    _a  ,    _a      =  self._sanitize_parameters(prefix=A ,   **self._forward_params       )
                _a      =  {**self._preprocess_params, **preprocess_params}
                _a      =  {**self._forward_params, **forward_params}



 def 					a__  (self ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   **A ,   )    ->     str:


      """simple docstring"""



      _a      =  {}
      if prefix is not None:
           _a      =  prefix
      if prefix:
           _a      =  self.tokenizer(
               A ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
           _a      =  prefix_inputs['''input_ids'''].shape[-1]

      if handle_long_generation is not None:
           if handle_long_generation not in {"hole"}:
                raise ValueError(
                    f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
                    ''' [None, \'hole\']'''       )
           _a      =  handle_long_generation

      preprocess_params.update(A       )
      _a      =  generate_kwargs

      _a      =  {}
      if return_full_text is not None and return_type is None:
           if return_text is not None:
                raise ValueError('''`return_text` is mutually exclusive with `return_full_text`'''       )
           if return_tensors is not None:
                raise ValueError('''`return_full_text` is mutually exclusive with `return_tensors`'''       )
           _a      =  ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
      if return_tensors is not None and return_type is None:
           if return_text is not None:
                raise ValueError('''`return_text` is mutually exclusive with `return_tensors`'''       )
           _a      =  ReturnType.TENSORS
      if return_type is not None:
           _a      =  return_type
      if clean_up_tokenization_spaces is not None:
           _a      =  clean_up_tokenization_spaces

      if stop_sequence is not None:
           _a      =  self.tokenizer.encode(A ,   add_special_tokens=A       )
           if len(A       ) > 1:
                warnings.warn(
                    '''Stopping on a multiple token sequence is not yet supported on transformers. The first token of'''
                    ''' the stop sequence will be used as the stop sequence string in the interim.'''       )
           _a      =  stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params



 def 					a__  (self ,   *A ,   **A       )    ->     List[Any]:


      """simple docstring"""



      if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
           kwargs.update({'''add_space_before_punct_symbol''': True}       )

      return super()._parse_and_tokenize(*A ,   **A       )



 def __call__(self ,   A ,   **A       )    ->     int:


      """simple docstring"""



      return super().__call__(A ,   **A       )



 def 					a__  (self ,   A ,   A="" ,   A=None ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  self.tokenizer(
          prefix + prompt_text ,   padding=A ,   add_special_tokens=A ,   return_tensors=self.framework       )
      _a      =  prompt_text

      if handle_long_generation == "hole":
           _a      =  inputs['''input_ids'''].shape[-1]
           if "max_new_tokens" in generate_kwargs:
                _a      =  generate_kwargs['''max_new_tokens''']
           else:
                _a      =  generate_kwargs.get('''max_length''' ,   self.model.config.max_length       ) - cur_len
                if new_tokens < 0:
                     raise ValueError('''We cannot infer how many new tokens are expected'''       )
           if cur_len + new_tokens > self.tokenizer.model_max_length:
                _a      =  self.tokenizer.model_max_length - new_tokens
                if keep_length <= 0:
                     raise ValueError(
                         '''We cannot use `hole` to handle this generation the number of desired tokens exceeds the'''
                         ''' models max length'''       )

                _a      =  inputs['''input_ids'''][:, -keep_length:]
                if "attention_mask" in inputs:
                     _a      =  inputs['''attention_mask'''][:, -keep_length:]

      return inputs



 def 					a__  (self ,   A ,   **A       )    ->     Any:


      """simple docstring"""



      _a      =  model_inputs['''input_ids''']
      _a      =  model_inputs.get('''attention_mask''' ,   A       )
      # Allow empty prompts
      if input_ids.shape[1] == 0:
           _a      =  None
           _a      =  None
           _a      =  1
      else:
           _a      =  input_ids.shape[0]
      _a      =  model_inputs.pop('''prompt_text'''       )

      # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
      # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
      _a      =  generate_kwargs.pop('''prefix_length''' ,   0       )
      if prefix_length > 0:
           _a      =  '''max_new_tokens''' in generate_kwargs or (
               '''generation_config''' in generate_kwargs
               and generate_kwargs['''generation_config'''].max_new_tokens is not None
           )
           if not has_max_new_tokens:
                _a      =  generate_kwargs.get('''max_length'''       ) or self.model.config.max_length
                generate_kwargs["max_length"] += prefix_length
           _a      =  '''min_new_tokens''' in generate_kwargs or (
               '''generation_config''' in generate_kwargs
               and generate_kwargs['''generation_config'''].min_new_tokens is not None
           )
           if not has_min_new_tokens and "min_length" in generate_kwargs:
                generate_kwargs["min_length"] += prefix_length

        # BS x SL
      _a      =  self.model.generate(input_ids=A ,   attention_mask=A ,   **A       )
      _a      =  generated_sequence.shape[0]
      if self.framework == "pt":
           _a      =  generated_sequence.reshape(A ,   out_b // in_b ,   *generated_sequence.shape[1:]       )
      elif self.framework == "tf":
           _a      =  tf.reshape(A ,   (in_b, out_b // in_b, *generated_sequence.shape[1:])       )
      return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}



 def 					a__  (self ,   A ,   A=ReturnType.FULL_TEXT ,   A=True       )    ->     str:


      """simple docstring"""



      _a      =  model_outputs['''generated_sequence'''][0]
      _a      =  model_outputs['''input_ids''']
      _a      =  model_outputs['''prompt_text''']
      _a      =  generated_sequence.numpy().tolist()
      _a      =  []
      for sequence in generated_sequence:
           if return_type == ReturnType.TENSORS:
                _a      =  {'''generated_token_ids''': sequence}
           elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                # Decode text
                _a      =  self.tokenizer.decode(
                    A ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )

                # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                if input_ids is None:
                     _a      =  0
                else:
                     _a      =  len(
                         self.tokenizer.decode(
                             input_ids[0] ,   skip_special_tokens=A ,   clean_up_tokenization_spaces=A ,   )       )

                if return_type == ReturnType.FULL_TEXT:
                     _a      =  prompt_text + text[prompt_length:]
                else:
                     _a      =  text[prompt_length:]

                _a      =  {'''generated_text''': all_text}
           records.append(A       )

      return records



