

import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
 import faiss






@require_faiss
class 						__magic_name__					(							snake_case				):






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						tempfile.mkdtemp()
         UpperCamelCase_			  =						8

         # DPR tok
         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						"dpr_tokenizer"	)
         os.makedirs(_lowercase		,						exist_ok=_lowercase	)
         UpperCamelCase_			  =						os.path.join(_lowercase		,						DPR_VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

         # BART tok
         UpperCamelCase_			  =						[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "<unk>",
         ]
         UpperCamelCase_			  =						dict(zip(_lowercase		,						range(len(_lowercase	)	)	)	)
         UpperCamelCase_			  =						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         UpperCamelCase_			  =						{"unk_token": "<unk>"}

         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						"bart_tokenizer"	)
         os.makedirs(_lowercase		,						exist_ok=_lowercase	)
         UpperCamelCase_			  =						os.path.join(_lowercase		,						BART_VOCAB_FILES_NAMES["vocab_file"]	)
         UpperCamelCase_			  =						os.path.join(_lowercase		,						BART_VOCAB_FILES_NAMES["merges_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as fp:
             fp.write(json.dumps(_lowercase	) + "\n"	)
         with open(self.merges_file		,						"w"		,						encoding="utf-8"	) as fp:
             fp.write("\n".join(_lowercase	)	)






     def 	UpperCAmelCase_				(				self	)->     DPRQuestionEncoderTokenizer:
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,						"dpr_tokenizer"	)	)






     def 	UpperCAmelCase_				(				self	)->     DPRContextEncoderTokenizer:
         return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,						"dpr_tokenizer"	)	)






     def 	UpperCAmelCase_				(				self	)->     BartTokenizer:
         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,						"bart_tokenizer"	)	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         shutil.rmtree(self.tmpdirname	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "text": ["foo", "bar"],
                 "title": ["Foo", "Bar"],
                 "embeddings": [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
             }	)
         dataset.add_faiss_index("embeddings"		,						string_factory="Flat"		,						metric_type=faiss.METRIC_INNER_PRODUCT	)
         return dataset






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.get_dummy_dataset()
         UpperCamelCase_			  =						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size		,						question_encoder=DPRConfig().to_dict()		,						generator=BartConfig().to_dict()		,						)
         with patch("transformers.models.rag.retrieval_rag.load_dataset"	) as mock_load_dataset:
             UpperCamelCase_			  =						dataset
             UpperCamelCase_			  =						RagRetriever(
                 _lowercase		,						question_encoder_tokenizer=self.get_dpr_tokenizer()		,						generator_tokenizer=self.get_bart_tokenizer()		,						)
         return retriever






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						self.get_dummy_dataset()
         UpperCamelCase_			  =						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size		,						question_encoder=DPRConfig().to_dict()		,						generator=BartConfig().to_dict()		,						index_name="custom"		,						)
         if from_disk:
             UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						"dataset"	)
             UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						"index.faiss"	)
             dataset.get_index("embeddings"	).save(os.path.join(self.tmpdirname		,						"index.faiss"	)	)
             dataset.drop_index("embeddings"	)
             dataset.save_to_disk(os.path.join(self.tmpdirname		,						"dataset"	)	)
             del dataset
             UpperCamelCase_			  =						RagRetriever(
                 _lowercase		,						question_encoder_tokenizer=self.get_dpr_tokenizer()		,						generator_tokenizer=self.get_bart_tokenizer()		,						)
         else:
             UpperCamelCase_			  =						RagRetriever(
                 _lowercase		,						question_encoder_tokenizer=self.get_dpr_tokenizer()		,						generator_tokenizer=self.get_bart_tokenizer()		,						index=CustomHFIndex(config.retrieval_vector_size		,						_lowercase	)		,						)
         return retriever






     def 	UpperCAmelCase_				(				self	)->     List[str]:
         UpperCamelCase_			  =						Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "text": ["foo", "bar"],
                 "title": ["Foo", "Bar"],
                 "embeddings": [np.ones(self.retrieval_vector_size + 1	), 2 * np.ones(self.retrieval_vector_size + 1	)],
             }	)
         dataset.add_faiss_index("embeddings"		,						string_factory="Flat"		,						metric_type=faiss.METRIC_INNER_PRODUCT	)

         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						"hf_bert_base.hnswSQ8_correct_phi_128.c_index"	)
         dataset.save_faiss_index("embeddings"		,						index_file_name + ".index.dpr"	)
         pickle.dump(dataset["id"]		,						open(index_file_name + ".index_meta.dpr"		,						"wb"	)	)

         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						"psgs_w100.tsv.pkl"	)
         UpperCamelCase_			  =						{sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
         pickle.dump(_lowercase		,						open(_lowercase		,						"wb"	)	)

         UpperCamelCase_			  =						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size		,						question_encoder=DPRConfig().to_dict()		,						generator=BartConfig().to_dict()		,						index_name="legacy"		,						index_path=self.tmpdirname		,						)
         UpperCamelCase_			  =						RagRetriever(
             _lowercase		,						question_encoder_tokenizer=self.get_dpr_tokenizer()		,						generator_tokenizer=self.get_bart_tokenizer()	)
         return retriever






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						self.get_dummy_canonical_hf_index_retriever()
         UpperCamelCase_			  =						np.array(
             [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=_lowercase	)
         self.assertEqual(retrieved_doc_embeds.shape		,						(2, n_docs, self.retrieval_vector_size)	)
         self.assertEqual(len(_lowercase	)		,						2	)
         self.assertEqual(sorted(doc_dicts[0]	)		,						["embeddings", "id", "text", "title"]	)
         self.assertEqual(len(doc_dicts[0]["id"]	)		,						_lowercase	)
         self.assertEqual(doc_dicts[0]["id"][0]		,						"1"	)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0]		,						"0"	)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()		,						[[1], [0]]	)






     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						self.get_dummy_canonical_hf_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
             with patch("transformers.models.rag.retrieval_rag.load_dataset"	) as mock_load_dataset:
                 UpperCamelCase_			  =						self.get_dummy_dataset()
                 retriever.save_pretrained(_lowercase	)
                 UpperCamelCase_			  =						RagRetriever.from_pretrained(_lowercase	)
                 self.assertIsInstance(_lowercase		,						_lowercase	)
                 UpperCamelCase_			  =						np.array(
                     [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
                 UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=1	)
                 self.assertTrue(out is not None	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase	)
         UpperCamelCase_			  =						np.array(
             [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=_lowercase	)
         self.assertEqual(retrieved_doc_embeds.shape		,						(2, n_docs, self.retrieval_vector_size)	)
         self.assertEqual(len(_lowercase	)		,						2	)
         self.assertEqual(sorted(doc_dicts[0]	)		,						["embeddings", "id", "text", "title"]	)
         self.assertEqual(len(doc_dicts[0]["id"]	)		,						_lowercase	)
         self.assertEqual(doc_dicts[0]["id"][0]		,						"1"	)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0]		,						"0"	)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()		,						[[1], [0]]	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase	)
         with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(_lowercase	)
             UpperCamelCase_			  =						RagRetriever.from_pretrained(_lowercase	)
             self.assertIsInstance(_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						np.array(
                 [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
             UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=1	)
             self.assertTrue(out is not None	)






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase	)
         UpperCamelCase_			  =						np.array(
             [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=_lowercase	)
         self.assertEqual(retrieved_doc_embeds.shape		,						(2, n_docs, self.retrieval_vector_size)	)
         self.assertEqual(len(_lowercase	)		,						2	)
         self.assertEqual(sorted(doc_dicts[0]	)		,						["embeddings", "id", "text", "title"]	)
         self.assertEqual(len(doc_dicts[0]["id"]	)		,						_lowercase	)
         self.assertEqual(doc_dicts[0]["id"][0]		,						"1"	)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0]		,						"0"	)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()		,						[[1], [0]]	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase	)
         with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(_lowercase	)
             UpperCamelCase_			  =						RagRetriever.from_pretrained(_lowercase	)
             self.assertIsInstance(_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						np.array(
                 [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
             UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=1	)
             self.assertTrue(out is not None	)






     def 	UpperCAmelCase_				(				self	)->     str:
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						self.get_dummy_legacy_index_retriever()
         UpperCamelCase_			  =						np.array(
             [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=_lowercase	)
         self.assertEqual(retrieved_doc_embeds.shape		,						(2, n_docs, self.retrieval_vector_size)	)
         self.assertEqual(len(_lowercase	)		,						2	)
         self.assertEqual(sorted(doc_dicts[0]	)		,						["text", "title"]	)
         self.assertEqual(len(doc_dicts[0]["text"]	)		,						_lowercase	)
         self.assertEqual(doc_dicts[0]["text"][0]		,						"bar"	)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["text"][0]		,						"foo"	)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()		,						[[1], [0]]	)






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         UpperCamelCase_			  =						self.get_dummy_legacy_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
             retriever.save_pretrained(_lowercase	)
             UpperCamelCase_			  =						RagRetriever.from_pretrained(_lowercase	)
             self.assertIsInstance(_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						np.array(
                 [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
             UpperCamelCase_			  =						retriever.retrieve(_lowercase		,						n_docs=1	)
             self.assertTrue(out is not None	)






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         import torch

         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						self.get_dummy_canonical_hf_index_retriever()
         UpperCamelCase_			  =						[[5, 7], [10, 11]]
         UpperCamelCase_			  =						np.array(
             [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
         UpperCamelCase_			  =						retriever(_lowercase		,						_lowercase		,						prefix=retriever.config.generator.prefix		,						n_docs=_lowercase	)
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						(
             out["context_input_ids"],
             out["context_attention_mask"],
             out["retrieved_doc_embeds"],
         )
         self.assertEqual(retrieved_doc_embeds.shape		,						(2, n_docs, self.retrieval_vector_size)	)
         self.assertIsInstance(_lowercase		,						_lowercase	)
         self.assertIsInstance(_lowercase		,						_lowercase	)
         self.assertIsInstance(_lowercase		,						np.ndarray	)

         UpperCamelCase_			  =						retriever(
             _lowercase		,						_lowercase		,						prefix=retriever.config.generator.prefix		,						n_docs=_lowercase		,						return_tensors="pt"		,						)
         UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						(  # noqa: F841
             out["context_input_ids"],
             out["context_attention_mask"],
             out["retrieved_doc_embeds"],
             out["doc_ids"],
         )
         self.assertEqual(retrieved_doc_embeds.shape		,						(2, n_docs, self.retrieval_vector_size)	)
         self.assertIsInstance(_lowercase		,						torch.Tensor	)
         self.assertIsInstance(_lowercase		,						torch.Tensor	)
         self.assertIsInstance(_lowercase		,						torch.Tensor	)






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         UpperCamelCase_			  =						self.get_dpr_ctx_encoder_tokenizer()
         UpperCamelCase_			  =						1
         UpperCamelCase_			  =						self.get_dummy_custom_hf_index_retriever(from_disk=_lowercase	)
         retriever.set_ctx_encoder_tokenizer(_lowercase	)

         UpperCamelCase_			  =						[[5, 7], [10, 11]]
         UpperCamelCase_			  =						np.array(
             [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,						dtype=np.floataa	)
         UpperCamelCase_			  =						retriever(_lowercase		,						_lowercase		,						prefix=retriever.config.generator.prefix		,						n_docs=_lowercase	)

         self.assertEqual(
             len(_lowercase	)		,						6	)  # check whether the retriever output consist of 6 attributes including tokenized docs
         self.assertEqual(
             all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")	)		,						_lowercase	)  # check for doc token related keys in dictionary.



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


SCREAMING_SNAKE_CASE		:Optional[int]						=     logging.getLogger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_=2			,						SCREAMING_SNAKE_CASE_=3			,						SCREAMING_SNAKE_CASE_=1_6			,						SCREAMING_SNAKE_CASE_ = 1_0			,						SCREAMING_SNAKE_CASE_ = 2			)->						Optional[Any]:



    """simple docstring"""






    def get_dataset(SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						torch.randn(batch_size * n_batches			,						1			)
        return TensorDataset(SCREAMING_SNAKE_CASE_			,						a * x + b + 0.1 * torch.randn(batch_size * n_batches			,						1			)			)

    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						get_dataset(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    UpperCamelCase_			  =						DataLoader(SCREAMING_SNAKE_CASE_			,						shuffle=SCREAMING_SNAKE_CASE_			,						batch_size=SCREAMING_SNAKE_CASE_			,						num_workers=4			)
    return (train_dataloader, valid_dataloader)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    for epoch in range(SCREAMING_SNAKE_CASE_			):
        # Train quickly
        model.train()
        for batch in dataloader:
            UpperCamelCase_  ,       UpperCamelCase_			  =						batch
            UpperCamelCase_			  =						model(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            accelerator.backward(SCREAMING_SNAKE_CASE_			)
            optimizer.step()
            optimizer.zero_grad()
        rands.append(random.random()			)  # Introduce some randomness
        if scheduler is not None:
            scheduler.step()
    return rands






class 						__magic_name__					(							nn.Module				):






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






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     str:
         return x * self.a + self.b






class 						__magic_name__					(							unittest.TestCase				):






     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(total_limit=1		,						project_dir=_lowercase		,						automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()

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






     def 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             # Train baseline
             UpperCamelCase_			  =						Accelerator()
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             UpperCamelCase_			  =						os.path.join(_lowercase		,						"initial"	)
             accelerator.save_state(_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             UpperCamelCase_			  =						train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()

             # Train partially
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						Accelerator()
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             accelerator.load_state(_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						train(2		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save everything
             UpperCamelCase_			  =						os.path.join(_lowercase		,						"checkpoint"	)
             accelerator.save_state(_lowercase	)

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






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)

             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             UpperCamelCase_			  =						train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()

             # Train partially
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(iteration=1		,						automatic_checkpoint_naming=_lowercase	)
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						train(2		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save everything
             accelerator.save_state()

             # Load everything back in and make sure all states work
             accelerator.load_state(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_1"	)	)
             test_rands += train(1		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             ((UpperCamelCase_)  ,       (UpperCamelCase_))			  =						model.a.item(), model.b.item()
             UpperCamelCase_			  =						optimizer.state_dict()
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)
             self.assertEqual(_lowercase		,						_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						torch.tensor([1, 2, 3]	)
         UpperCamelCase_			  =						torch.tensor([2, 3, 4]	)
         UpperCamelCase_			  =						DummyModel()
         UpperCamelCase_			  =						torch.optim.Adam(net.parameters()	)
         UpperCamelCase_			  =						Accelerator()
         with self.assertRaises(_lowercase	) as ve:
             accelerator.register_for_checkpointing(_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
         UpperCamelCase_			  =						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 	UpperCAmelCase_				(				self	)->     Optional[Any]:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						torch.optim.Adam(params=model.parameters()		,						lr=1e-3	)
             UpperCamelCase_			  =						torch.optim.lr_scheduler.StepLR(_lowercase		,						step_size=1		,						gamma=0.99	)
             UpperCamelCase_  ,       UpperCamelCase_			  =						dummy_dataloaders()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_  ,       UpperCamelCase_			  =						accelerator.prepare(
                 _lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             # Save initial
             accelerator.save_state()
             UpperCamelCase_			  =						scheduler.state_dict()
             train(3		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase		,						_lowercase	)
             self.assertNotEqual(_lowercase		,						scheduler.state_dict()	)

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






     def 	UpperCAmelCase_				(				self	)->     str:
         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42	)
             UpperCamelCase_			  =						DummyModel()
             UpperCamelCase_			  =						ProjectConfiguration(automatic_checkpoint_naming=_lowercase		,						total_limit=2	)
             # Train baseline
             UpperCamelCase_			  =						Accelerator(project_dir=_lowercase		,						project_config=_lowercase	)
             UpperCamelCase_			  =						accelerator.prepare(_lowercase	)
             # Save 3 states:
             for _ in range(11	):
                 accelerator.save_state()
             self.assertTrue(not os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_0"	)	)	)
             self.assertTrue(os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_9"	)	)	)
             self.assertTrue(os.path.exists(os.path.join(_lowercase		,						"checkpoints"		,						"checkpoint_10"	)	)	)






     @require_cuda
     def 	UpperCAmelCase_				(				self	)->     int:
         UpperCamelCase_			  =						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__	)]
         execute_subprocess_async(_lowercase		,						env=os.environ.copy()	)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Any						=     """/tmp/accelerate/state_checkpointing"""
 SCREAMING_SNAKE_CASE		:Any						=     DummyModel()
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     torch.optim.Adam(params=model.parameters(), lr=1e-3)
 SCREAMING_SNAKE_CASE		:Dict						=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:str						=     dummy_dataloaders()
 SCREAMING_SNAKE_CASE		:Tuple						=     ProjectConfiguration(automatic_checkpoint_naming=True)
 # Train baseline
 SCREAMING_SNAKE_CASE		:int						=     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)
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:Tuple						=     accelerator.prepare(
     model, optimizer, train_dataloader, valid_dataloader, scheduler
 )
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:List[str]						=     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:
  SCREAMING_SNAKE_CASE		:Optional[Any]						=     group["""params"""][0].device
  break
 assert param_device.type == accelerator.device.type
 SCREAMING_SNAKE_CASE		:List[str]						=     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:
  SCREAMING_SNAKE_CASE		:Optional[int]						=     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:
  SCREAMING_SNAKE_CASE		:str						=     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()
