


"""simple docstring"""
import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",
    author = \"Moosavi, Nafise Sadat  and
      Strube, Michael\",
    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",
    month = aug,
    year = \"2016\",
    address = \"Berlin, Germany\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/P16-1060\",
    doi = \"10.18653/v1/P16-1060\",
    pages = \"632--642\",
}

"""

SCREAMING_SNAKE_CASE :    Optional[int]									=				"""\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
"""

SCREAMING_SNAKE_CASE :    Optional[int]									=				"""
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting 'keep_singletons=False', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    'mentions': mentions
    'muc': MUC metric [Vilain et al, 1995]
    'bcub': B-cubed [Bagga and Baldwin, 1998]
    'ceafe': CEAFe [Luo et al., 2005]
    'lea': LEA [Moosavi and Strube, 2016]
    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric('coval')
    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',
    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',
    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',
    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',
    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',
    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}
"""






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      Optional[int]=False     ,   _snake_case						:      int=False     ,   _snake_case						:      Optional[Any]=True     ,   _snake_case						:      Optional[int]=False     ,   _snake_case						:      Optional[int]="dummy_doc"     )     ->int:
	"""simple docstring"""





	__snake_case	:						Any						=				{doc: key_lines}
	__snake_case	:						str						=				{doc: sys_lines}

	__snake_case	:						Any						=				{}

	__snake_case	:						Union[str, Any]						=				0
	__snake_case	:						Dict						=				0
	__snake_case	:						Optional[Any]						=				0
	__snake_case	:						List[Any]						=				0
	__snake_case	:						int						=				0
	__snake_case	:						Optional[Any]						=				0

	__snake_case	,							__snake_case	:						str						=				reader.get_doc_mentions(_snake_case     ,   key_doc_lines[doc]     ,   _snake_case     )
	key_singletons_num += singletons_num

	if NP_only or min_span:
		__snake_case	:						List[Any]						=				reader.set_annotated_parse_trees(_snake_case     ,   key_doc_lines[doc]     ,   _snake_case     ,   _snake_case     )

	__snake_case	,							__snake_case	:						int						=				reader.get_doc_mentions(_snake_case     ,   sys_doc_lines[doc]     ,   _snake_case     )
	sys_singletons_num += singletons_num

	if NP_only or min_span:
		__snake_case	:						str						=				reader.set_annotated_parse_trees(_snake_case     ,   key_doc_lines[doc]     ,   _snake_case     ,   _snake_case     )

	if remove_nested:
		__snake_case	,							__snake_case	:						Optional[int]						=				reader.remove_nested_coref_mentions(_snake_case     ,   _snake_case     )
		key_nested_coref_num += nested_mentions
		key_removed_nested_clusters += removed_clusters

		__snake_case	,							__snake_case	:						int						=				reader.remove_nested_coref_mentions(_snake_case     ,   _snake_case     )
		sys_nested_coref_num += nested_mentions
		sys_removed_nested_clusters += removed_clusters

	__snake_case	:						List[Any]						=				reader.get_mention_assignments(_snake_case     ,   _snake_case     )
	__snake_case	:						Any						=				reader.get_mention_assignments(_snake_case     ,   _snake_case     )

	__snake_case	:						str						=				(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

	if remove_nested:
		logger.info(
		    '''Number of removed nested coreferring mentions in the key '''
		    f"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""     )
		logger.info(
		    '''Number of resulting singleton clusters in the key '''
		    f"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""     )

	if not keep_singletons:
		logger.info(
		    f"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
		    '''files, respectively'''     )

	return doc_coref_infos






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      str     ,   _snake_case						:      List[Any]     ,   _snake_case						:      Union[str, Any]     ,   _snake_case						:      List[Any]     )     ->Optional[int]:
	"""simple docstring"""





	__snake_case	:						str						=				get_coref_infos(_snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     ,   _snake_case     )

	__snake_case	:						Union[str, Any]						=				{}
	__snake_case	:						Union[str, Any]						=				0
	__snake_case	:						Optional[int]						=				0

	for name, metric in metrics:
		__snake_case	,							__snake_case	,							__snake_case	:						Tuple						=				evaluator.evaluate_documents(_snake_case     ,   _snake_case     ,   beta=1     )
		if name in ["muc", "bcub", "ceafe"]:
			conll += fa
			conll_subparts_num += 1
		output_scores.update({f"""{name}/recall""": recall, f"""{name}/precision""": precision, f"""{name}/f1""": fa}     )

		logger.info(
		    name.ljust(10     )     ,   f"""Recall: {recall * 100:.2f}"""     ,   f""" Precision: {precision * 100:.2f}"""     ,   f""" F1: {fa * 100:.2f}"""     ,   )

	if conll_subparts_num == 3:
		__snake_case	:						Optional[Any]						=				(conll / 3) * 100
		logger.info(f"""CoNLL score: {conll:.2f}"""     )
		output_scores.update({'''conll_score''': conll}     )

	return output_scores






def 		lowercase							(     _snake_case						:      Optional[int]     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						Any						=				False
	for line in key_lines:
		if not line.startswith('''#'''     ):
			if len(line.split()     ) > 6:
				__snake_case	:						Dict						=				line.split()[5]
				if not parse_col == "-":
					__snake_case	:						Optional[int]						=				True
					break
				else:
					break
	return has_gold_parse



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return datasets.MetricInfo(
			    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
			        {
			            '''predictions''': datasets.Sequence(datasets.Value('''string'''					)					),
			            '''references''': datasets.Sequence(datasets.Value('''string'''					)					),
			        }					)				,       codebase_urls=['''https://github.com/ns-moosavi/coval''']				,       reference_urls=[
			        '''https://github.com/ns-moosavi/coval''',
			        '''https://www.aclweb.org/anthology/P16-1060''',
			        '''http://www.conll.cemantix.org/2012/data.html''',
			    ]				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_=True				,       a_=False				,       a_=False				,       a_=False					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				[
			    ('''mentions''', evaluator.mentions),
			    ('''muc''', evaluator.muc),
			    ('''bcub''', evaluator.b_cubed),
			    ('''ceafe''', evaluator.ceafe),
			    ('''lea''', evaluator.lea),
			]

			if min_span:
				__snake_case	:						Optional[int]						=				util.check_gold_parse_annotation(a_					)
				if not has_gold_parse:
					raise NotImplementedError('''References should have gold parse annotation to use \'min_span\'.'''					)
					# util.parse_key_file(key_file)
					# key_file = key_file + ".parsed"

			__snake_case	:						Tuple						=				evaluate(
			    key_lines=a_				,       sys_lines=a_				,       metrics=a_				,       NP_only=a_				,       remove_nested=a_				,       keep_singletons=a_				,       min_span=a_				,       )

			return score










"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCAmelCase    :Tuple     =					logging.getLogger(__name__)





def  lowerCamelCase_						(UpperCamelCase__		:		List[Any]=2     ,       UpperCamelCase__		:		List[Any]=3     ,       UpperCamelCase__		:		List[Any]=16     ,       UpperCamelCase__		:		int = 10     ,       UpperCamelCase__		:		int = 2							):

    def get_dataset(UpperCamelCase__		:		List[str]							):
        _UpperCAmelCase							:  Optional[Any]											=    torch.randn(batch_size * n_batches     ,       1							)
        return TensorDataset(UpperCamelCase__     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1							)							)

    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    get_dataset(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    _UpperCAmelCase							:  List[str]											=    DataLoader(UpperCamelCase__     ,       shuffle=UpperCamelCase__     ,       batch_size=UpperCamelCase__     ,       num_workers=4							)
    return (train_dataloader, valid_dataloader)





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		List[Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Tuple=None							):
    _UpperCAmelCase							:  Tuple											=    []
    for epoch in range(UpperCamelCase__							):
        # Train quickly
        model.train()
        for batch in dataloader:
            _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    batch
            _UpperCAmelCase							:  int											=    model(UpperCamelCase__							)
            _UpperCAmelCase							:  Dict											=    torch.nn.functional.mse_loss(UpperCamelCase__     ,       UpperCamelCase__							)
            accelerator.backward(UpperCamelCase__							)
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()							)  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands
class 							_UpperCAmelCase							( nn.Module      ):





     '''simple docstring'''



     def __init__(					self		)							->					List[Any]:
         super().__init__()
         _UpperCAmelCase							:  List[Any]											=    nn.Parameter(torch.randn(1		)		)
         _UpperCAmelCase							:  int											=    nn.Parameter(torch.randn(1		)		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Tuple:
         return x * self.a + self.b



class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					Any:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Any											=    ProjectConfiguration(total_limit=1       ,	project_dir=A       ,	automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()

             # Save second state
             accelerator.save_state()
             self.assertEqual(len(os.listdir(accelerator.project_dir		)		)       ,	1		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Optional[Any]											=    DummyModel()
             _UpperCAmelCase							:  int											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Dict											=    dummy_dataloaders()
             # Train baseline
             _UpperCAmelCase							:  Optional[int]											=    Accelerator()
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  List[str]											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             _UpperCAmelCase							:  Union[str, Any]											=    os.path.join(A       ,	'''initial'''		)
             accelerator.save_state(A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Optional[Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  str											=    optimizer.state_dict()
             _UpperCAmelCase							:  Tuple											=    train(3       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  List[Any]											=    optimizer.state_dict()

             # Train partially
             set_seed(4_2		)
             _UpperCAmelCase							:  Dict											=    DummyModel()
             _UpperCAmelCase							:  Optional[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  Tuple											=    Accelerator()
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             accelerator.load_state(A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  List[str]											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)

             _UpperCAmelCase							:  Union[str, Any]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             _UpperCAmelCase							:  List[str]											=    os.path.join(A       ,	'''checkpoint'''		)
             accelerator.save_state(A		)

             # Load everything back in and make sure all states work
             accelerator.load_state(A		)
             test_rands += train(1       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Dict											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					int:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  List[str]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)

             # Train baseline
             _UpperCAmelCase							:  str											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Dict											=    optimizer.state_dict()
             _UpperCAmelCase							:  int											=    train(3       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Union[str, Any]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Union[str, Any]											=    optimizer.state_dict()

             # Train partially
             set_seed(4_2		)
             _UpperCAmelCase							:  List[Any]											=    DummyModel()
             _UpperCAmelCase							:  Union[str, Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(iteration=1       ,	automatic_checkpoint_naming=A		)
             _UpperCAmelCase							:  Tuple											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A		)
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_0'''		)		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  Dict											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  str											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)

             _UpperCAmelCase							:  List[str]											=    train(2       ,	A       ,	A       ,	A       ,	A		)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_1'''		)		)
             test_rands += train(1       ,	A       ,	A       ,	A       ,	A		)
             ((_UpperCAmelCase)							,				(_UpperCAmelCase))							:  List[str]											=    model.a.item(), model.b.item()
             _UpperCAmelCase							:  Tuple											=    optimizer.state_dict()
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)
             self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[Any]											=    torch.tensor([1, 2, 3]		)
         _UpperCAmelCase							:  List[str]											=    torch.tensor([2, 3, 4]		)
         _UpperCAmelCase							:  Optional[int]											=    DummyModel()
         _UpperCAmelCase							:  Dict											=    torch.optim.Adam(net.parameters()		)
         _UpperCAmelCase							:  Optional[int]											=    Accelerator()
         with self.assertRaises(A		) as ve:
             accelerator.register_for_checkpointing(A       ,	A       ,	A       ,	A		)
         _UpperCAmelCase							:  Dict											=    str(ve.exception		)
         self.assertTrue('''Item at index 0''' in message		)
         self.assertTrue('''Item at index 1''' in message		)
         self.assertFalse('''Item at index 2''' in message		)
         self.assertFalse('''Item at index 3''' in message		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  Tuple											=    DummyModel()
             _UpperCAmelCase							:  List[Any]											=    torch.optim.Adam(params=model.parameters()       ,	lr=1E-3		)
             _UpperCAmelCase							:  Optional[int]											=    torch.optim.lr_scheduler.StepLR(A       ,	step_size=1       ,	gamma=0.99		)
             _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    dummy_dataloaders()
             _UpperCAmelCase							:  List[str]											=    ProjectConfiguration(automatic_checkpoint_naming=A		)
             # Train baseline
             _UpperCAmelCase							:  int											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  str											=    accelerator.prepare(
                 A       ,	A       ,	A       ,	A       ,	A		)
             # Save initial
             accelerator.save_state()
             _UpperCAmelCase							:  List[str]											=    scheduler.state_dict()
             train(3       ,	A       ,	A       ,	A       ,	A       ,	A		)
             self.assertNotEqual(A       ,	scheduler.state_dict()		)

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_0'''		)		)
             self.assertEqual(A       ,	scheduler.state_dict()		)





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(4_2		)
             _UpperCAmelCase							:  int											=    DummyModel()
             _UpperCAmelCase							:  str											=    ProjectConfiguration(automatic_checkpoint_naming=A       ,	total_limit=2		)
             # Train baseline
             _UpperCAmelCase							:  Union[str, Any]											=    Accelerator(project_dir=A       ,	project_config=A		)
             _UpperCAmelCase							:  Optional[Any]											=    accelerator.prepare(A		)
             # Save 3 states:
             for _ in range(1_1		):
                 accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_0'''		)		)		)
             self.assertTrue(os.path.exists(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_9'''		)		)		)
             self.assertTrue(os.path.exists(os.path.join(A       ,	'''checkpoints'''       ,	'''checkpoint_10'''		)		)		)





     @require_cuda
     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  str											=    ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__		)]
         execute_subprocess_async(A       ,	env=os.environ.copy()		)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					'/tmp/accelerate/state_checkpointing'
  _lowerCAmelCase    :Any     =					DummyModel()
  _lowerCAmelCase    :Tuple     =					torch.optim.Adam(params=model.parameters(), lr=1E-3)
  _lowerCAmelCase    :Dict     =					torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
  _lowerCAmelCase,_lowerCAmelCase    :Any     =					dummy_dataloaders()
  _lowerCAmelCase    :Tuple     =					ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  _lowerCAmelCase    :Optional[Any]     =					Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
  if accelerator.process_index == 0:
    if os.path.exists(savedir):
      shutil.rmtree(savedir)
    os.makedirs(savedir)
  _lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase,_lowerCAmelCase    :str     =					accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  _lowerCAmelCase,_lowerCAmelCase    :List[Any]     =					accelerator.prepare(model, optimizer)
  train(3, model, train_dataloader, optimizer, accelerator, scheduler)
  # Check that the intial optimizer is loaded on the GPU
  for group in optimizer.param_groups:
    _lowerCAmelCase    :int     =					group['params'][0].device
    break
  assert param_device.type == accelerator.device.type
  _lowerCAmelCase    :Dict     =					model.cpu()
  accelerator.wait_for_everyone()
  accelerator.save_state()
  accelerator.wait_for_everyone()

  # Check CPU state
  accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
  for group in optimizer.param_groups:
    _lowerCAmelCase    :List[Any]     =					group['params'][0].device
    break
  assert (
      param_device.type == torch.device('cpu').type
  ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

  # Check device state
  model.to(accelerator.device)
  accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
  for group in optimizer.param_groups:
    _lowerCAmelCase    :Union[str, Any]     =					group['params'][0].device
    break
  assert (
      param_device.type == accelerator.device.type
  ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

  # Check error
  with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
    accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
  accelerator.wait_for_everyone()
  if accelerator.process_index == 0:
    shutil.rmtree(savedir)
  accelerator.wait_for_everyone()


