





import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_UpperCAmelCase							:			str        =							logging.get_logger(__name__)

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""linear"""
 UpperCAmelCase__						=						"""cosine"""
 UpperCAmelCase__						=						"""cosine_with_restarts"""
 UpperCAmelCase__						=						"""polynomial"""
 UpperCAmelCase__						=						"""constant"""
 UpperCAmelCase__						=						"""constant_with_warmup"""
 UpperCAmelCase__						=						"""piecewise_constant"""


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase = -1					)     ->	Union[str, Any]:
    return LambdaLR(_UpperCAmelCase		,      lambda _UpperCAmelCase					: 1		,      last_epoch=_UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = -1					)     ->	str:

    def lr_lambda(_UpperCAmelCase					):
        if current_step < num_warmup_steps:
            return float(_UpperCAmelCase					) / float(max(1.0		,      _UpperCAmelCase					)					)
        return 1.0

    return LambdaLR(_UpperCAmelCase		,      _UpperCAmelCase		,      last_epoch=_UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = -1					)     ->	int:
    lowerCamelCase__       : int   					=						{}
    lowerCamelCase__       : Optional[Any]   					=						step_rules.split(','					)
    for rule_str in rule_list[:-1]:
        lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						rule_str.split(':'					)
        lowerCamelCase__       : Union[str, Any]   					=						int(_UpperCAmelCase					)
        lowerCamelCase__       : List[Any]   					=						float(_UpperCAmelCase					)
        lowerCamelCase__       : List[Any]   					=						value
    lowerCamelCase__       : int   					=						float(rule_list[-1]					)

    def create_rules_function(_UpperCAmelCase		,      _UpperCAmelCase					):
        def rule_func(_UpperCAmelCase					) -> float:
            lowerCamelCase__       : Optional[Any]   					=						sorted(rules_dict.keys()					)
            for i, sorted_step in enumerate(_UpperCAmelCase					):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    lowerCamelCase__       : Any   					=						create_rules_function(_UpperCAmelCase		,      _UpperCAmelCase					)

    return LambdaLR(_UpperCAmelCase		,      _UpperCAmelCase		,      last_epoch=_UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=-1					)     ->	Dict:

    def lr_lambda(_UpperCAmelCase					):
        if current_step < num_warmup_steps:
            return float(_UpperCAmelCase					) / float(max(1		,      _UpperCAmelCase					)					)
        return max(
            0.0		,      float(num_training_steps - current_step					) / float(max(1		,      num_training_steps - num_warmup_steps					)					)					)

    return LambdaLR(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = 0.5		,      _UpperCAmelCase = -1					)     ->	Optional[Any]:

    def lr_lambda(_UpperCAmelCase					):
        if current_step < num_warmup_steps:
            return float(_UpperCAmelCase					) / float(max(1		,      _UpperCAmelCase					)					)
        lowerCamelCase__       : str   					=						float(current_step - num_warmup_steps					) / float(max(1		,      num_training_steps - num_warmup_steps					)					)
        return max(0.0		,      0.5 * (1.0 + math.cos(math.pi * float(_UpperCAmelCase					) * 2.0 * progress					))					)

    return LambdaLR(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = -1					)     ->	List[Any]:

    def lr_lambda(_UpperCAmelCase					):
        if current_step < num_warmup_steps:
            return float(_UpperCAmelCase					) / float(max(1		,      _UpperCAmelCase					)					)
        lowerCamelCase__       : int   					=						float(current_step - num_warmup_steps					) / float(max(1		,      num_training_steps - num_warmup_steps					)					)
        if progress >= 1.0:
            return 0.0
        return max(0.0		,      0.5 * (1.0 + math.cos(math.pi * ((float(_UpperCAmelCase					) * progress) % 1.0)					))					)

    return LambdaLR(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=1e-7		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=-1					)     ->	Tuple:
    lowerCamelCase__       : Optional[Any]   					=						optimizer.defaults['lr']
    if not (lr_init > lr_end):
        raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""					)

    def lr_lambda(_UpperCAmelCase					):
        if current_step < num_warmup_steps:
            return float(_UpperCAmelCase					) / float(max(1		,      _UpperCAmelCase					)					)
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            lowerCamelCase__       : Dict   					=						lr_init - lr_end
            lowerCamelCase__       : Tuple   					=						num_training_steps - num_warmup_steps
            lowerCamelCase__       : Any   					=						1 - (current_step - num_warmup_steps) / decay_steps
            lowerCamelCase__       : Optional[Any]   					=						lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)


_UpperCAmelCase							:			int        =							{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = 1.0		,      _UpperCAmelCase = -1		,      )     ->	Optional[Any]:
    lowerCamelCase__       : Union[str, Any]   					=						SchedulerType(_UpperCAmelCase					)
    lowerCamelCase__       : List[Any]   					=						TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_UpperCAmelCase		,      last_epoch=_UpperCAmelCase					)

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(_UpperCAmelCase		,      step_rules=_UpperCAmelCase		,      last_epoch=_UpperCAmelCase					)

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""					)

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(_UpperCAmelCase		,      num_warmup_steps=_UpperCAmelCase		,      last_epoch=_UpperCAmelCase					)

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""					)

    if name == SchedulerType.COSINE_WITH_RESTARTS:
        return schedule_func(
            _UpperCAmelCase		,      num_warmup_steps=_UpperCAmelCase		,      num_training_steps=_UpperCAmelCase		,      num_cycles=_UpperCAmelCase		,      last_epoch=_UpperCAmelCase		,      )

    if name == SchedulerType.POLYNOMIAL:
        return schedule_func(
            _UpperCAmelCase		,      num_warmup_steps=_UpperCAmelCase		,      num_training_steps=_UpperCAmelCase		,      power=_UpperCAmelCase		,      last_epoch=_UpperCAmelCase		,      )

    return schedule_func(
        _UpperCAmelCase		,      num_warmup_steps=_UpperCAmelCase		,      num_training_steps=_UpperCAmelCase		,      last_epoch=_UpperCAmelCase					)












import pytest


_UpperCAmelCase							:			Optional[int]        =							"""__dummy_dataset1__"""

_UpperCAmelCase							:			Dict        =							"""
import json
import os

import datasets


REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),
                \"ner_tags\": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            \"O\",
                            \"B-PER\",
                            \"I-PER\",
                            \"B-ORG\",
                            \"I-ORG\",
                            \"B-LOC\",
                            \"I-LOC\",
                        ]
                    )
                ),
                \"langs\": datasets.Sequence(datasets.Value(\"string\")),
                \"spans\": datasets.Sequence(datasets.Value(\"string\")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, \"r\", encoding=\"utf-8\") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
"""


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    return DATASET_LOADING_SCRIPT_NAME


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	List[Any]:
    return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[Any]:
    lowerCamelCase__       : int   					=						dataset_loading_script_name
    lowerCamelCase__       : Any   					=						tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						script_dir / F"""{script_name}.py"""
    with open(_UpperCAmelCase		,      'w'					) as f:
        f.write(_UpperCAmelCase					)
    return str(_UpperCAmelCase					)





