
import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


_A       =					"\\n@inproceedings{ramshaw-marcus-1995-text,\n    title = \"Text Chunking using Transformation-Based Learning\",\n    author = \"Ramshaw, Lance  and\n      Marcus, Mitch\",\n    booktitle = \"Third Workshop on Very Large Corpora\",\n    year = \"1995\",\n    url = \"https://www.aclweb.org/anthology/W95-0107\",\n}\n@misc{seqeval,\n  title={{seqeval}: A Python framework for sequence labeling evaluation},\n  url={https://github.com/chakki-works/seqeval},\n  note={Software available from https://github.com/chakki-works/seqeval},\n  author={Hiroki Nakayama},\n  year={2018},\n}\n"

_A       =					"\\nseqeval is a Python framework for sequence labeling evaluation.\nseqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.\n\nThis is well-tested by using the Perl script conlleval, which can be used for\nmeasuring the performance of a system that has processed the CoNLL-2000 shared task data.\n\nseqeval supports following formats:\nIOB1\nIOB2\nIOE1\nIOE2\nIOBES\n\nSee the [README.md] file at https://github.com/chakki-works/seqeval for more information.\n"

_A       =					"\nProduces labelling scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)\n    references: List of List of reference labels (Ground truth (correct) target values)\n    suffix: True if the IOB prefix is after type, False otherwise. default: False\n    scheme: Specify target tagging scheme. Should be one of [\"IOB1\", \"IOB2\", \"IOE1\", \"IOE2\", \"IOBES\", \"BILOU\"].\n        default: None\n    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.\n        If you want to only count exact matches, pass mode=\"strict\". default: None.\n    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None\n    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,\n        \"warn\". \"warn\" acts as 0, but the warning is raised.\n\nReturns:\n    'scores': dict. Summary of the scores for overall and per type\n        Overall:\n            'accuracy': accuracy,\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure,\n        Per type:\n            'precision': precision,\n            'recall': recall,\n            'f1': F1 score, also known as balanced F-score or F-measure\nExamples:\n\n    >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]\n    >>> seqeval = datasets.load_metric(\"seqeval\")\n    >>> results = seqeval.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['MISC', 'PER', 'overall_precision', 'overall_recall', 'overall_f1', 'overall_accuracy']\n    >>> print(results[\"overall_f1\"])\n    0.5\n    >>> print(results[\"PER\"][\"f1\"])\n    1.0\n"





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




				def 					__a						( self					)    ->				Dict:
							return datasets.MetricInfo(
							    description=_DESCRIPTION    ,    citation=_CITATION    ,    homepage="https://github.com/chakki-works/seqeval"    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
							        {
							            "predictions": datasets.Sequence(datasets.Value("string"    ,    id="label"					)    ,    id="sequence"					),
							            "references": datasets.Sequence(datasets.Value("string"    ,    id="label"					)    ,    id="sequence"					),
							        }					)    ,    codebase_urls=["https://github.com/chakki-works/seqeval"]    ,    reference_urls=["https://github.com/chakki-works/seqeval"]    ,    )




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase = False    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = None    ,    _UpperCamelCase = "warn"    ,    )    ->				Any:
							if scheme is not None:
										try:
													lowerCAmelCase_      =     importlib.import_module("seqeval.scheme"					)
													lowerCAmelCase_      =     getattr(_UpperCamelCase    ,    _UpperCamelCase					)
										except AttributeError:
													raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""					)
							lowerCAmelCase_      =     classification_report(
							    y_true=_UpperCamelCase    ,    y_pred=_UpperCamelCase    ,    suffix=_UpperCamelCase    ,    output_dict=_UpperCamelCase    ,    scheme=_UpperCamelCase    ,    mode=_UpperCamelCase    ,    sample_weight=_UpperCamelCase    ,    zero_division=_UpperCamelCase    ,    )
							report.pop("macro avg"					)
							report.pop("weighted avg"					)
							lowerCAmelCase_      =     report.pop("micro avg"					)

							lowerCAmelCase_      =     {
							    type_name: {
							        "precision": score["precision"],
							        "recall": score["recall"],
							        "f1": score["f1-score"],
							        "number": score["support"],
							    }
							    for type_name, score in report.items()
							}
							lowerCAmelCase_      =     overall_score["precision"]
							lowerCAmelCase_      =     overall_score["recall"]
							lowerCAmelCase_      =     overall_score["f1-score"]
							lowerCAmelCase_      =     accuracy_score(y_true=_UpperCamelCase    ,    y_pred=_UpperCamelCase					)

							return scores





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_A       =					10_00_00_00_00_00
_A       =					1_00_00

_A,				_A       =					os.path.split(__file__)
_A       =					os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     pa.Table.from_pydict({"col": [0] * chunk_size}    )
			lowerCAmelCase_      =     pa.concat_tables([table] * (num_examples // chunk_size)    )
			return datasets.Dataset(__lowerCAmelCase   ,							fingerprint="table_100B"    )





@dataclass
class      _lowerCAmelCase :
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=42




				def 					__a						( self					)    ->				List[Any]:
							lowerCAmelCase_      =     np.random.default_rng(self.seed					)
							lowerCAmelCase_      =     rng.integers(low=self.low    ,    high=self.high    ,    size=self.size					).tolist()




				def __iter__( self					)    ->				List[str]:
							return iter(self._sampled_values					)




				def __len__( self					)    ->				Optional[int]:
							return self.size






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[0]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[-1]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[range(len(__lowerCAmelCase    ) // 2   ,							len(__lowerCAmelCase    ) // 2 + 1024    )]






@get_duration
def        lowerCamelCase__    (    __lowerCAmelCase  :  datasets.Dataset    ):




			"""simple docstring"""


			lowerCAmelCase_      =     dataset[RandIter(0   ,							len(__lowerCAmelCase    )   ,							1024   ,							seed=42    )]






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     {"num examples": SPEED_TEST_N_EXAMPLES}
			lowerCAmelCase_      =     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
			print("generating dataset"    )
			lowerCAmelCase_      =     generate_100B_dataset(num_examples=__lowerCAmelCase   ,							chunk_size=__lowerCAmelCase    )
			print("Functions"    )
			for func in functions:
						print(func.__name__    )
						lowerCAmelCase_      =     func(__lowerCAmelCase    )

			with open(__lowerCAmelCase   ,							"wb"    ) as f:
						f.write(json.dumps(__lowerCAmelCase    ).encode("utf-8"    )    )


if __name__ == "__main__":  # useful to run the profiler
			benchmark_table_100B()



