
'''simple docstring'''

import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


UpperCAmelCase__   : List[str]					=		"\\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"

UpperCAmelCase__   : Tuple					=		"\\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"

UpperCAmelCase__   : Optional[int]					=		"\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    A     (	datasets.Metric    ):





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple							):


				"""simple docstring"""

				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 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       Union[str, Any]      ,							__magic_name__			:       str      ,							__magic_name__			:       bool = False      ,							__magic_name__			:       Optional[str] = None      ,							__magic_name__			:       Optional[str] = None      ,							__magic_name__			:       Optional[List[int]] = None      ,							__magic_name__			:       Union[str, int] = "warn"      ,							):


				"""simple docstring"""

				if scheme is not None:
							try:
										lowerCAmelCase__								=							importlib.import_module("seqeval.scheme"							)
										lowerCAmelCase__								=							getattr(__magic_name__      ,							__magic_name__							)
							except AttributeError:
										raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"""							)
				lowerCAmelCase__								=							classification_report(
				    y_true=__magic_name__      ,							y_pred=__magic_name__      ,							suffix=__magic_name__      ,							output_dict=__magic_name__      ,							scheme=__magic_name__      ,							mode=__magic_name__      ,							sample_weight=__magic_name__      ,							zero_division=__magic_name__      ,							)
				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=__magic_name__      ,							y_pred=__magic_name__							)

				return scores







'''simple docstring'''

import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



			if isinstance(UpperCamelCase_						,	(list, tuple)      ):
						return honor_type(UpperCamelCase_						,	(move_to_device(UpperCamelCase_						,	UpperCamelCase_      ) for t in state)      )
			elif isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
						return type(UpperCamelCase_      )({k: move_to_device(UpperCamelCase_						,	UpperCamelCase_      ) for k, v in state.items()}      )
			elif isinstance(UpperCamelCase_						,	torch.Tensor      ):
						return state.to(UpperCamelCase_      )
			return state






class    A     (	torch.optim.Optimizer    ):





	def __init__(					self			:       Union[str, Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       List[str]=True      ,							__magic_name__			:       Optional[int]=None							):


				"""simple docstring"""

				lowerCAmelCase__								=							optimizer
				lowerCAmelCase__								=							scaler
				lowerCAmelCase__								=							AcceleratorState()
				lowerCAmelCase__								=							GradientState()
				lowerCAmelCase__								=							device_placement
				lowerCAmelCase__								=							False
				lowerCAmelCase__								=							None

				# Handle device placement
				if device_placement:
							lowerCAmelCase__								=							self.optimizer.state_dict()
							if self.accelerator_state.distributed_type == DistributedType.TPU:
										xm.send_cpu_data_to_device(__magic_name__      ,							self.accelerator_state.device							)
							else:
										lowerCAmelCase__								=							move_to_device(__magic_name__      ,							self.accelerator_state.device							)
							self.optimizer.load_state_dict(__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				return self.optimizer.state





	@state.setter
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							state





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				return self.optimizer.param_groups





	@param_groups.setter
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__								=							param_groups





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]							):


				"""simple docstring"""

				return self.optimizer.defaults





	@defaults.setter
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							defaults





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

				self.optimizer.add_param_group(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

				if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
							xm.send_cpu_data_to_device(__magic_name__      ,							self.accelerator_state.device							)
				self.optimizer.load_state_dict(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple							):


				"""simple docstring"""

				return self.optimizer.state_dict()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       List[Any]=None							):


				"""simple docstring"""

				if self.gradient_state.sync_gradients:
							lowerCAmelCase__								=							"set_to_none" in inspect.signature(self.optimizer.zero_grad							).parameters
							if accept_arg:
										if set_to_none is None:
													lowerCAmelCase__								=							False
										self.optimizer.zero_grad(set_to_none=__magic_name__							)
							else:
										if set_to_none is not None:
													raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."							)
										self.optimizer.zero_grad()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Dict=None							):


				"""simple docstring"""

				if self.gradient_state.sync_gradients:
							if self.accelerator_state.distributed_type == DistributedType.TPU:
										lowerCAmelCase__								=							{"closure": closure} if closure is not None else {}
										xm.optimizer_step(self.optimizer      ,							optimizer_args=__magic_name__							)
							elif self.scaler is not None:
										lowerCAmelCase__								=							False
										if self._last_scale is None:
													# `get_scale` is an async operation requiring full synchronization
													# on CPU and GPUs before finishing. As a result, we store away
													# the prior one to reduce the call overhead
													lowerCAmelCase__								=							self.scaler.get_scale()
													lowerCAmelCase__								=							True
										self.scaler.step(self.optimizer      ,							__magic_name__							)
										self.scaler.update()
										lowerCAmelCase__								=							self.scaler.get_scale()
										if not new_scale:
													# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
													lowerCAmelCase__								=							scale_after < self._last_scale
										lowerCAmelCase__								=							scale_after

							else:
										self.optimizer.step(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       List[str]							):


				"""simple docstring"""

				for param_group in self.optimizer.param_groups:
							lowerCAmelCase__								=							[parameters_map.get(__magic_name__      ,							__magic_name__							) for p in param_group["params"]]





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				warnings.warn(
				    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
				    "`optimizer.step_was_skipped` instead."      ,							__magic_name__      ,							)
				return self._is_overflow





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				return self._is_overflow





	def __getstate__(					self			:       int							):


				"""simple docstring"""

				return self.__dict__.copy()







	def __setstate__(					self			:       List[str]      ,							__magic_name__			:       Dict							):


				"""simple docstring"""

				self.__dict__.update(__magic_name__							)





