




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE			    =logging.get_logger(__name__)

def       a (_lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     SwinConfig(
							    embed_dim=1_9_2	,     depths=(2, 2, 1_8, 2)	,     num_heads=(6, 1_2, 2_4, 4_8)	,     window_size=1_2	,     out_features=['''stage2''', '''stage3''', '''stage4''']	,     )

							SCREAMING_SNAKE_CASE_ 						=     DetaConfig(
							    backbone_config=SCREAMING_SNAKE_CASE_	,     num_queries=9_0_0	,     encoder_ffn_dim=2_0_4_8	,     decoder_ffn_dim=2_0_4_8	,     num_feature_levels=5	,     assign_first_stage=SCREAMING_SNAKE_CASE_	,     with_box_refine=SCREAMING_SNAKE_CASE_	,     two_stage=SCREAMING_SNAKE_CASE_	,     )

							# set labels
							SCREAMING_SNAKE_CASE_ 						=     '''huggingface/label-files'''
							if "o365" in model_name:
														SCREAMING_SNAKE_CASE_ 						=     3_6_6
														SCREAMING_SNAKE_CASE_ 						=     '''object365-id2label.json'''
							else:
														SCREAMING_SNAKE_CASE_ 						=     9_1
														SCREAMING_SNAKE_CASE_ 						=     '''coco-detection-id2label.json'''

							SCREAMING_SNAKE_CASE_ 						=     num_labels
							SCREAMING_SNAKE_CASE_ 						=     json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     repo_type='''dataset'''  )  )	,     '''r'''  )  )
							SCREAMING_SNAKE_CASE_ 						=     {int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE_ 						=     idalabel
							SCREAMING_SNAKE_CASE_ 						=     {v: k for k, v in idalabel.items()}

							return config

def       a (_lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     []

							# stem
							# fmt: off
							rename_keys.append(('''backbone.0.body.patch_embed.proj.weight''', '''model.backbone.model.embeddings.patch_embeddings.projection.weight''')  )
							rename_keys.append(('''backbone.0.body.patch_embed.proj.bias''', '''model.backbone.model.embeddings.patch_embeddings.projection.bias''')  )
							rename_keys.append(('''backbone.0.body.patch_embed.norm.weight''', '''model.backbone.model.embeddings.norm.weight''')  )
							rename_keys.append(('''backbone.0.body.patch_embed.norm.bias''', '''model.backbone.model.embeddings.norm.bias''')  )
							# stages
							for i in range(len(config.backbone_config.depths  )  ):
														for j in range(config.backbone_config.depths[i]  ):
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")  )

														if i < 3:
																					rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")  )
																					rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")  )

							rename_keys.append(('''backbone.0.body.norm1.weight''', '''model.backbone.model.hidden_states_norms.stage2.weight''')  )
							rename_keys.append(('''backbone.0.body.norm1.bias''', '''model.backbone.model.hidden_states_norms.stage2.bias''')  )
							rename_keys.append(('''backbone.0.body.norm2.weight''', '''model.backbone.model.hidden_states_norms.stage3.weight''')  )
							rename_keys.append(('''backbone.0.body.norm2.bias''', '''model.backbone.model.hidden_states_norms.stage3.bias''')  )
							rename_keys.append(('''backbone.0.body.norm3.weight''', '''model.backbone.model.hidden_states_norms.stage4.weight''')  )
							rename_keys.append(('''backbone.0.body.norm3.bias''', '''model.backbone.model.hidden_states_norms.stage4.bias''')  )

							# transformer encoder
							for i in range(config.encoder_layers  ):
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")  )
														rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")  )

							# transformer decoder
							for i in range(config.decoder_layers  ):
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")  )
														rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")  )

							# fmt: on

							return rename_keys

def       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     dct.pop(SCREAMING_SNAKE_CASE_  )
							SCREAMING_SNAKE_CASE_ 						=     val

def       a (_lowerCAmelCase	,     _lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
							for i in range(len(backbone_config.depths  )  ):
														SCREAMING_SNAKE_CASE_ 						=     num_features[i]
														for j in range(backbone_config.depths[i]  ):
																					# fmt: off
																					# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																					SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"  )
																					SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"  )
																					# next, add query, keys and values (in that order) to the state dict
																					SCREAMING_SNAKE_CASE_ 						=     in_proj_weight[:dim, :]
																					SCREAMING_SNAKE_CASE_ 						=     in_proj_bias[: dim]
																					SCREAMING_SNAKE_CASE_ 						=     in_proj_weight[
																					    dim : dim * 2, :
																					]
																					SCREAMING_SNAKE_CASE_ 						=     in_proj_bias[
																					    dim : dim * 2
																					]
																					SCREAMING_SNAKE_CASE_ 						=     in_proj_weight[
																					    -dim :, :
																					]
																					SCREAMING_SNAKE_CASE_ 						=     in_proj_bias[-dim :]
																					# fmt: on

def       a (_lowerCAmelCase	,     _lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     config.d_model
							for i in range(config.decoder_layers  ):
														# read in weights + bias of input projection layer of self-attention
														SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"  )
														SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"  )
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE_ 						=     in_proj_weight[:hidden_size, :]
														SCREAMING_SNAKE_CASE_ 						=     in_proj_bias[:hidden_size]
														SCREAMING_SNAKE_CASE_ 						=     in_proj_weight[
														    hidden_size : hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE_ 						=     in_proj_bias[hidden_size : hidden_size * 2]
														SCREAMING_SNAKE_CASE_ 						=     in_proj_weight[-hidden_size:, :]
														SCREAMING_SNAKE_CASE_ 						=     in_proj_bias[-hidden_size:]

def       a ():
							SCREAMING_SNAKE_CASE_ 						=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE_ 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE_	,     stream=SCREAMING_SNAKE_CASE_  ).raw  )

							return im

@torch.no_grad()
def       a (_lowerCAmelCase	,     _lowerCAmelCase	,     _lowerCAmelCase  ):
							SCREAMING_SNAKE_CASE_ 						=     get_deta_config(SCREAMING_SNAKE_CASE_  )

							# load original state dict
							if model_name == "deta-swin-large":
														SCREAMING_SNAKE_CASE_ 						=     hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,     filename='''adet_swin_ft.pth'''  )
							elif model_name == "deta-swin-large-o365":
														SCREAMING_SNAKE_CASE_ 						=     hf_hub_download(repo_id='''jozhang97/deta-swin-l-o365'''	,     filename='''deta_swin_pt_o365.pth'''  )
							else:
														raise ValueError(F"Model name {model_name} not supported"  )

							SCREAMING_SNAKE_CASE_ 						=     torch.load(SCREAMING_SNAKE_CASE_	,     map_location='''cpu'''  )['''model''']

							# original state dict
							for name, param in state_dict.items():
														print(SCREAMING_SNAKE_CASE_	,     param.shape  )

							# rename keys
							SCREAMING_SNAKE_CASE_ 						=     create_rename_keys(SCREAMING_SNAKE_CASE_  )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_  )
							read_in_swin_q_k_v(SCREAMING_SNAKE_CASE_	,     config.backbone_config  )
							read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_  )

							# fix some prefixes
							for key in state_dict.copy().keys():
														if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																					SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(SCREAMING_SNAKE_CASE_  )
																					SCREAMING_SNAKE_CASE_ 						=     val
														if "input_proj" in key:
																					SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(SCREAMING_SNAKE_CASE_  )
																					SCREAMING_SNAKE_CASE_ 						=     val
														if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																					SCREAMING_SNAKE_CASE_ 						=     state_dict.pop(SCREAMING_SNAKE_CASE_  )
																					SCREAMING_SNAKE_CASE_ 						=     val

    # finally, create HuggingFace model and load state dict
							SCREAMING_SNAKE_CASE_ 						=     DetaForObjectDetection(SCREAMING_SNAKE_CASE_  )
							model.load_state_dict(SCREAMING_SNAKE_CASE_  )
							model.eval()

							SCREAMING_SNAKE_CASE_ 						=     '''cuda''' if torch.cuda.is_available() else '''cpu'''
							model.to(SCREAMING_SNAKE_CASE_  )

							# load image processor
							SCREAMING_SNAKE_CASE_ 						=     DetaImageProcessor(format='''coco_detection'''  )

							# verify our conversion on image
							SCREAMING_SNAKE_CASE_ 						=     prepare_img()
							SCREAMING_SNAKE_CASE_ 						=     processor(images=SCREAMING_SNAKE_CASE_	,     return_tensors='''pt'''  )
							SCREAMING_SNAKE_CASE_ 						=     encoding['''pixel_values''']
							SCREAMING_SNAKE_CASE_ 						=     model(pixel_values.to(SCREAMING_SNAKE_CASE_  )  )

							# verify logits
							print('''Logits:'''	,     outputs.logits[0, :3, :3]  )
							print('''Boxes:'''	,     outputs.pred_boxes[0, :3, :3]  )
							if model_name == "deta-swin-large":
														SCREAMING_SNAKE_CASE_ 						=     torch.tensor(
														    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
														SCREAMING_SNAKE_CASE_ 						=     torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
							elif model_name == "deta-swin-large-o365":
														SCREAMING_SNAKE_CASE_ 						=     torch.tensor(
														    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
														SCREAMING_SNAKE_CASE_ 						=     torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
							assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(SCREAMING_SNAKE_CASE_  )	,     atol=1e-4  )
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(SCREAMING_SNAKE_CASE_  )	,     atol=1e-4  )
							print('''Everything ok!'''  )

							if pytorch_dump_folder_path:
														# Save model and processor
														logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."  )
														Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
														model.save_pretrained(SCREAMING_SNAKE_CASE_  )
														processor.save_pretrained(SCREAMING_SNAKE_CASE_  )

							# Push to hub
							if push_to_hub:
														print('''Pushing model and processor to hub...'''  )
														model.push_to_hub(F"jozhang97/{model_name}"  )
														processor.push_to_hub(F"jozhang97/{model_name}"  )


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE			    =argparse.ArgumentParser()

			parser.add_argument(
			    """--model_name""",
			    type=str,
			    default="""deta-swin-large""",
			    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
			    help="""Name of the model you'd like to convert.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default=None,
			    type=str,
			    help="""Path to the folder to output PyTorch model.""",
			)
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
			)
			__SCREAMING_SNAKE_CASE			    =parser.parse_args()
			convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





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()
