

"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__   )      ->      str:




   """simple docstring"""
   # initialize config
   if "resnet-50" in model_name:
      lowerCamelCase__						=			ResNetConfig.from_pretrained("microsoft/resnet-50"   )
   elif "resnet-101" in model_name:
      lowerCamelCase__						=			ResNetConfig.from_pretrained("microsoft/resnet-101"   )
   else:
      raise ValueError("Model name should include either resnet50 or resnet101"   )

   lowerCamelCase__						=			DetrConfig(use_timm_backbone=A__	,    backbone_config=A__   )

   # set label attributes
   lowerCamelCase__						=			"panoptic" in model_name
   if is_panoptic:
      lowerCamelCase__						=			250
   else:
      lowerCamelCase__						=			91
      lowerCamelCase__						=			"huggingface/label-files"
      lowerCamelCase__						=			"coco-detection-id2label.json"
      lowerCamelCase__						=			json.load(open(hf_hub_download(A__	,    A__	,    repo_type="dataset"   )	,    "r"   )   )
      lowerCamelCase__						=			{int(A__   ): v for k, v in idalabel.items()}
      lowerCamelCase__						=			idalabel
      lowerCamelCase__						=			{v: k for k, v in idalabel.items()}

   return config, is_panoptic



def 	UpperCAmelCase__   (				A__   )      ->      Union[str, Any]:




   """simple docstring"""
   lowerCamelCase__						=			[]

   # stem
   # fmt: off
   rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")   )
   rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")   )
   rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")   )
   rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")   )
   rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var")   )
   # stages
   for stage_idx in range(len(config.backbone_config.depths   )   ):
      for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
         # shortcut
         if layer_idx == 0:
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                )   )
         # 3 convs
         for i in range(3   ):
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                )   )
            rename_keys.append(
                (
                    f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                    f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                )   )
    # fmt: on

   for i in range(config.encoder_layers   ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append(
          (
              f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
              f'encoder.layers.{i}.self_attn.out_proj.weight',
          )   )
      rename_keys.append(
          (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')   )
      rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight')   )
      rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias')   )
      rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight')   )
      rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias')   )
      rename_keys.append(
          (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')   )
      rename_keys.append(
          (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias')   )
      rename_keys.append(
          (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight')   )
      rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias')   )
      # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
      rename_keys.append(
          (
              f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
              f'decoder.layers.{i}.self_attn.out_proj.weight',
          )   )
      rename_keys.append(
          (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')   )
      rename_keys.append(
          (
              f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
              f'decoder.layers.{i}.encoder_attn.out_proj.weight',
          )   )
      rename_keys.append(
          (
              f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
              f'decoder.layers.{i}.encoder_attn.out_proj.bias',
          )   )
      rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight')   )
      rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias')   )
      rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight')   )
      rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias')   )
      rename_keys.append(
          (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')   )
      rename_keys.append(
          (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias')   )
      rename_keys.append(
          (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')   )
      rename_keys.append(
          (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')   )
      rename_keys.append(
          (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight')   )
      rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias')   )

   # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
   rename_keys.extend(
       [
           ("input_proj.weight", "input_projection.weight"),
           ("input_proj.bias", "input_projection.bias"),
           ("query_embed.weight", "query_position_embeddings.weight"),
           ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
           ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
           ("class_embed.weight", "class_labels_classifier.weight"),
           ("class_embed.bias", "class_labels_classifier.bias"),
           ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
           ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
           ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
           ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
           ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
           ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
       ]   )

   return rename_keys



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			state_dict.pop(A__   )
   lowerCamelCase__						=			val



def 	UpperCAmelCase__   (				A__	,    A__=False   )      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			""
   if is_panoptic:
      lowerCamelCase__						=			"detr."

   # first: transformer encoder
   for i in range(6   ):
      # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
      lowerCamelCase__						=			state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'   )
      lowerCamelCase__						=			state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'   )
      # next, add query, keys and values (in that order) to the state dict
      lowerCamelCase__						=			in_proj_weight[:256, :]
      lowerCamelCase__						=			in_proj_bias[:256]
      lowerCamelCase__						=			in_proj_weight[256:512, :]
      lowerCamelCase__						=			in_proj_bias[256:512]
      lowerCamelCase__						=			in_proj_weight[-256:, :]
      lowerCamelCase__						=			in_proj_bias[-256:]
   # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
   for i in range(6   ):
      # read in weights + bias of input projection layer of self-attention
      lowerCamelCase__						=			state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'   )
      lowerCamelCase__						=			state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'   )
      # next, add query, keys and values (in that order) to the state dict
      lowerCamelCase__						=			in_proj_weight[:256, :]
      lowerCamelCase__						=			in_proj_bias[:256]
      lowerCamelCase__						=			in_proj_weight[256:512, :]
      lowerCamelCase__						=			in_proj_bias[256:512]
      lowerCamelCase__						=			in_proj_weight[-256:, :]
      lowerCamelCase__						=			in_proj_bias[-256:]
      # read in weights + bias of input projection layer of cross-attention
      lowerCamelCase__						=			state_dict.pop(
          f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'   )
      lowerCamelCase__						=			state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'   )
      # next, add query, keys and values (in that order) of cross-attention to the state dict
      lowerCamelCase__						=			in_proj_weight_cross_attn[:256, :]
      lowerCamelCase__						=			in_proj_bias_cross_attn[:256]
      lowerCamelCase__						=			in_proj_weight_cross_attn[256:512, :]
      lowerCamelCase__						=			in_proj_bias_cross_attn[256:512]
      lowerCamelCase__						=			in_proj_weight_cross_attn[-256:, :]
      lowerCamelCase__						=			in_proj_bias_cross_attn[-256:]



def 	UpperCAmelCase__   (				)      ->      str:




   """simple docstring"""
   lowerCamelCase__						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowerCamelCase__						=			Image.open(requests.get(A__	,    stream=A__   ).raw   )

   return im



@torch.no_grad()
def 	UpperCAmelCase__   (				A__	,    A__=None	,    A__=False   )      ->      str:




   """simple docstring"""
   lowerCamelCase__					,       lowerCamelCase__						=			get_detr_config(A__   )

   # load original model from torch hub
   lowerCamelCase__						=			{
       "detr-resnet-50": "detr_resnet50",
       "detr-resnet-101": "detr_resnet101",
   }
   logger.info(f'Converting model {model_name}...'   )
   lowerCamelCase__						=			torch.hub.load("facebookresearch/detr"	,    model_name_to_original_name[model_name]	,    pretrained=A__   ).eval()
   lowerCamelCase__						=			detr.state_dict()
   # rename keys
   for src, dest in create_rename_keys(A__   ):
      if is_panoptic:
         lowerCamelCase__						=			"detr." + src
      rename_key(A__	,    A__	,    A__   )
   # query, key and value matrices need special treatment
   read_in_q_k_v(A__	,    is_panoptic=A__   )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   lowerCamelCase__						=			"detr.model." if is_panoptic else "model."
   for key in state_dict.copy().keys():
      if is_panoptic:
         if (
             key.startswith("detr"   )
             and not key.startswith("class_labels_classifier"   )
             and not key.startswith("bbox_predictor"   )
         ):
            lowerCamelCase__						=			state_dict.pop(A__   )
            lowerCamelCase__						=			val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            lowerCamelCase__						=			state_dict.pop(A__   )
            lowerCamelCase__						=			val
         elif key.startswith("bbox_attention"   ) or key.startswith("mask_head"   ):
            continue
         else:
            lowerCamelCase__						=			state_dict.pop(A__   )
            lowerCamelCase__						=			val
      else:
         if not key.startswith("class_labels_classifier"   ) and not key.startswith("bbox_predictor"   ):
            lowerCamelCase__						=			state_dict.pop(A__   )
            lowerCamelCase__						=			val

    # finally, create HuggingFace model and load state dict
   lowerCamelCase__						=			DetrForSegmentation(A__   ) if is_panoptic else DetrForObjectDetection(A__   )
   model.load_state_dict(A__   )
   model.eval()

   # verify our conversion on an image
   lowerCamelCase__						=			"coco_panoptic" if is_panoptic else "coco_detection"
   lowerCamelCase__						=			DetrImageProcessor(format=A__   )

   lowerCamelCase__						=			processor(images=prepare_img()	,    return_tensors="pt"   )
   lowerCamelCase__						=			encoding["pixel_values"]

   lowerCamelCase__						=			detr(A__   )
   lowerCamelCase__						=			model(A__   )

   assert torch.allclose(outputs.logits	,    original_outputs["pred_logits"]	,    atol=1E-3   )
   assert torch.allclose(outputs.pred_boxes	,    original_outputs["pred_boxes"]	,    atol=1E-3   )
   if is_panoptic:
      assert torch.allclose(outputs.pred_masks	,    original_outputs["pred_masks"]	,    atol=1E-4   )
   print("Looks ok!"   )

   if pytorch_dump_folder_path is not None:
      # Save model and image processor
      logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'   )
      Path(A__   ).mkdir(exist_ok=A__   )
      model.save_pretrained(A__   )
      processor.save_pretrained(A__   )

   if push_to_hub:
      # Upload model and image processor to the hub
      logger.info("Uploading PyTorch model and image processor to the hub..."   )
      model.push_to_hub(f'nielsr/{model_name}'   )
      processor.push_to_hub(f'nielsr/{model_name}'   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			List[Any]	  =			argparse.ArgumentParser()

     parser.add_argument(
         '''--model_name''',
         default='''detr-resnet-50''',
         type=str,
         choices=['''detr-resnet-50''', '''detr-resnet-101'''],
         help='''Name of the DETR 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 to push the model to the hub or not.''')
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""




import json
import os
import tempfile
from unittest.mock import patch

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

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment



def 	UpperCAmelCase__   (				)      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(2	,    4   )
   lowerCamelCase__						=			torch.optim.AdamW(model.parameters()	,    lr=1.0   )
   lowerCamelCase__						=			torch.optim.lr_scheduler.OneCycleLR(A__	,    max_lr=0.01	,    steps_per_epoch=2	,    epochs=1   )
   lowerCamelCase__						=			DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
   lowerCamelCase__						=			DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

   return model, optimizer, scheduler, train_dl, valid_dl



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
   model.load_state_dict(A__   )


class 		_A       (			__a					):





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__						=			GradientState()
         assert state.num_steps == 1
         lowerCamelCase__						=			4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         lowerCamelCase__						=			False
         assert state.sync_gradients is False
         GradientState._reset_state()





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()

         (
             (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       
         )						=			accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         self.assertTrue(prepared_model in accelerator._models		)
         self.assertTrue(prepared_optimizer in accelerator._optimizers		)
         self.assertTrue(prepared_scheduler in accelerator._schedulers		)
         self.assertTrue(prepared_train_dl in accelerator._dataloaders		)
         self.assertTrue(prepared_valid_dl in accelerator._dataloaders		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         accelerator.free_memory()

         self.assertTrue(len(accelerator._models		) == 0		)
         self.assertTrue(len(accelerator._optimizers		) == 0		)
         self.assertTrue(len(accelerator._schedulers		) == 0		)
         self.assertTrue(len(accelerator._dataloaders		) == 0		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         PartialState._reset_state()

         # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
         def noop(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		):
            pass

         with patch("torch.cuda.set_device"    ,						SCREAMING_SNAKE_CASE__		), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"		):
            lowerCamelCase__						=			Accelerator()
            self.assertEqual(str(accelerator.state.device		)    ,						"cuda:64"		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # make sure loaded weights match
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         # saving hook
         def save_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			{"class_name": models[0].__class__.__name__}

            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"w"		) as f:
               json.dump(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

        # loading hook
         def load_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"r"		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			config["class_name"]

         lowerCamelCase__						=			accelerator.register_save_state_pre_hook(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.register_load_state_pre_hook(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match with hooks
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

            # make sure loaded weights match with hooks
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)

            # mode.class_name is loaded from config
            self.assertTrue(model.class_name == model.__class__.__name__		)

         # remove hooks
         save_hook.remove()
         load_hook.remove()

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match with hooks removed
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

            # make sure loaded weights match with hooks removed
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)

            # mode.class_name is NOT loaded from config
            self.assertTrue(model.class_name != model.__class__.__name__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			None

         # This should work
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         self.assertTrue(dummy_obj is None		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			[1, 2, 3]

         # This should work
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					str:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map={"": 0}    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			Accelerator()

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            model.tie_weights()
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			"cpu"

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						device_map=SCREAMING_SNAKE_CASE__    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						llm_inta_enable_fpaa_cpu_offload=SCREAMING_SNAKE_CASE__		)

         # This should not work and get value error
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			{"distributed_type": DistributedType.MULTI_GPU}

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            model.tie_weights()
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should not work and get value error
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)

         PartialState._reset_state()





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			torch.nn.Linear(10    ,						10		)
         lowerCamelCase__						=			torch.optim.SGD(model.parameters()    ,						lr=0.01		)
         lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)




