


import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


_A		:				Tuple									=       datasets.utils.logging.get_logger(__name__)



@dataclass
class        a					(			datasets.BuilderConfig   ):
     UpperCAmelCase__ :  Optional[datasets.Features]						      =	None
     UpperCAmelCase__ :  str						      =	"utf-8"
     UpperCAmelCase__ :  Optional[str]						      =	None
     UpperCAmelCase__ :  Optional[str]						      =	None
     UpperCAmelCase__ :  bool						      =	True  # deprecated
     UpperCAmelCase__ :  Optional[int]						      =	None  # deprecated
     UpperCAmelCase__ :  int						      =	10 << 20  # 10MB
     UpperCAmelCase__ :  Optional[bool]						      =	None



class        a					(			datasets.ArrowBasedBuilder   ):
     UpperCAmelCase__ :  Tuple						      =	JsonConfig





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        if self.config.block_size is not None:
           logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""							)
           __lowerCamelCase:				List[str]     =	self.config.block_size
        if self.config.use_threads is not True:
           logger.warning(
               """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""							)
        if self.config.newlines_in_values is not None:
           raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""							)
        return datasets.DatasetInfo(features=self.config.features							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        if not self.config.data_files:
           raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}'							)
        __lowerCamelCase:				Any     =	dl_manager.download_and_extract(self.config.data_files							)
        if isinstance(SCREAMING_SNAKE_CASE_    ,	(str, list, tuple)							):
           __lowerCamelCase:				str     =	data_files
           if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
              __lowerCamelCase:				List[str]     =	[files]
           __lowerCamelCase:				Optional[Any]     =	[dl_manager.iter_files(SCREAMING_SNAKE_CASE_							) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,	gen_kwargs={"""files""": files}							)]
        __lowerCamelCase:				Union[str, Any]     =	[]
        for split_name, files in data_files.items():
           if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):
              __lowerCamelCase:				str     =	[files]
           __lowerCamelCase:				int     =	[dl_manager.iter_files(SCREAMING_SNAKE_CASE_							) for file in files]
           splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE_    ,	gen_kwargs={"""files""": files}							)							)
        return splits





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				pa.Table							):
        if self.config.features is not None:
           # adding missing columns
           for column_name in set(self.config.features							) - set(pa_table.column_names							):
              __lowerCamelCase:				Optional[Any]     =	self.config.features.arrow_schema.field(SCREAMING_SNAKE_CASE_							).type
              __lowerCamelCase:				Optional[Any]     =	pa_table.append_column(SCREAMING_SNAKE_CASE_    ,	pa.array([None] * len(SCREAMING_SNAKE_CASE_							)    ,	type=SCREAMING_SNAKE_CASE_							)							)
           # more expensive cast to support nested structures with keys in a different order
           # allows str <-> int/float or str to Audio for example
           __lowerCamelCase:				List[str]     =	table_cast(SCREAMING_SNAKE_CASE_    ,	self.config.features.arrow_schema							)
        return pa_table







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        for file_idx, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_							)							):
           # If the file is one json object and if we need to look at the list of items in one specific field
           if self.config.field is not None:
              with open(SCREAMING_SNAKE_CASE_    ,	encoding=self.config.encoding    ,	errors=self.config.encoding_errors							) as f:
                 __lowerCamelCase:				Union[str, Any]     =	json.load(SCREAMING_SNAKE_CASE_							)

              # We keep only the field we are interested in
              __lowerCamelCase:				str     =	dataset[self.config.field]

              # We accept two format: a list of dicts or a dict of lists
              if isinstance(SCREAMING_SNAKE_CASE_    ,	(list, tuple)							):
                 __lowerCamelCase:				Union[str, Any]     =	set().union(*[row.keys() for row in dataset]							)
                 __lowerCamelCase:				Tuple     =	{col: [row.get(SCREAMING_SNAKE_CASE_							) for row in dataset] for col in keys}
              else:
                 __lowerCamelCase:				Tuple     =	dataset
              __lowerCamelCase:				Dict     =	pa.Table.from_pydict(SCREAMING_SNAKE_CASE_							)
              yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE_							)

           # If the file has one json object per line
           else:
              with open(SCREAMING_SNAKE_CASE_    ,	"""rb"""							) as f:
                 __lowerCamelCase:				Union[str, Any]     =	0
                 # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                 # Set a default minimum value of 16kB if the chunk size is really small
                 __lowerCamelCase:				Dict     =	max(self.config.chunksize // 32    ,	16 << 10							)
                 __lowerCamelCase:				List[Any]     =	(
                     self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                 )
                 while True:
                    __lowerCamelCase:				Union[str, Any]     =	f.read(self.config.chunksize							)
                    if not batch:
                       break
                    # Finish current line
                    try:
                       batch += f.readline()
                    except (AttributeError, io.UnsupportedOperation):
                       batch += readline(SCREAMING_SNAKE_CASE_							)
                    # PyArrow only accepts utf-8 encoded bytes
                    if self.config.encoding != "utf-8":
                       __lowerCamelCase:				Optional[Any]     =	batch.decode(self.config.encoding    ,	errors=SCREAMING_SNAKE_CASE_							).encode("""utf-8"""							)
                    try:
                       while True:
                          try:
                             __lowerCamelCase:				List[Any]     =	paj.read_json(
                                 io.BytesIO(SCREAMING_SNAKE_CASE_							)    ,	read_options=paj.ReadOptions(block_size=SCREAMING_SNAKE_CASE_							)							)
                             break
                          except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                             if (
                                 isinstance(SCREAMING_SNAKE_CASE_    ,	pa.ArrowInvalid							)
                                 and "straddling" not in str(SCREAMING_SNAKE_CASE_							)
                                 or block_size > len(SCREAMING_SNAKE_CASE_							)
                             ):
                                raise
                             else:
                                # Increase the block size in case it was too small.
                                # The block size will be reset for the next file.
                                logger.debug(
                                    F'Batch of {len(SCREAMING_SNAKE_CASE_							)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'							)
                                block_size *= 2
                    except pa.ArrowInvalid as e:
                       try:
                          with open(
                              SCREAMING_SNAKE_CASE_    ,	encoding=self.config.encoding    ,	errors=self.config.encoding_errors							) as f:
                             __lowerCamelCase:				int     =	json.load(SCREAMING_SNAKE_CASE_							)
                       except json.JSONDecodeError:
                          logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_							)}: {e}'							)
                          raise e
                       # If possible, parse the file as a list of json objects and exit the loop
                       if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):  # list is the only sequence type supported in JSON
                          try:
                             __lowerCamelCase:				str     =	set().union(*[row.keys() for row in dataset]							)
                             __lowerCamelCase:				int     =	{col: [row.get(SCREAMING_SNAKE_CASE_							) for row in dataset] for col in keys}
                             __lowerCamelCase:				str     =	pa.Table.from_pydict(SCREAMING_SNAKE_CASE_							)
                          except (pa.ArrowInvalid, AttributeError) as e:
                             logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_							)}: {e}'							)
                             raise ValueError(F'Not able to read records in the JSON file at {file}.'							) from None
                          yield file_idx, self._cast_table(SCREAMING_SNAKE_CASE_							)
                          break
                       else:
                          logger.error(F'Failed to read file \'{file}\' with error {type(SCREAMING_SNAKE_CASE_							)}: {e}'							)
                          raise ValueError(
                              F'Not able to read records in the JSON file at {file}. '
                              F'You should probably indicate the field of the JSON file containing your records. '
                              F'This JSON file contain the following fields: {str(list(dataset.keys()							)							)}. '
                              F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '							) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                    yield (file_idx, batch_idx), self._cast_table(SCREAMING_SNAKE_CASE_							)
                    batch_idx += 1






from sklearn.metrics import fa_score

import datasets


_A		:				Any									=       '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


_A		:				Dict									=       '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. 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 `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'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. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


_A		:				Union[str, Any]									=       '''
@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}
}
'''



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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        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.f1_score.html"""]    ,	)







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				str=None    ,	SCREAMING_SNAKE_CASE_					:				str=1    ,	SCREAMING_SNAKE_CASE_					:				List[str]="binary"    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None							):
        __lowerCamelCase:				str     =	fa_score(
            SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_    ,	pos_label=SCREAMING_SNAKE_CASE_    ,	average=SCREAMING_SNAKE_CASE_    ,	sample_weight=SCREAMING_SNAKE_CASE_							)
        return {"f1": float(SCREAMING_SNAKE_CASE_							) if score.size == 1 else score}


