'''simple docstring'''





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


def     __lowerCAmelCase						(       a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       SwinConfig(
         embed_dim=192       ,      depths=(2, 2, 18, 2)       ,      num_heads=(6, 12, 24, 48)       ,      window_size=12       ,      out_features=['stage2', 'stage3', 'stage4']       ,      )

     SCREAMING_SNAKE_CASE					:			int    	=       DetaConfig(
         backbone_config=a_       ,      num_queries=900       ,      encoder_ffn_dim=2048       ,      decoder_ffn_dim=2048       ,      num_feature_levels=5       ,      assign_first_stage=a_       ,      with_box_refine=a_       ,      two_stage=a_       ,      )

     # set labels
     SCREAMING_SNAKE_CASE					:			List[str]    	=       'huggingface/label-files'
     if "o365" in model_name:
          SCREAMING_SNAKE_CASE					:			int    	=       366
          SCREAMING_SNAKE_CASE					:			Dict    	=       'object365-id2label.json'
     else:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       91
          SCREAMING_SNAKE_CASE					:			int    	=       'coco-detection-id2label.json'

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       num_labels
     SCREAMING_SNAKE_CASE					:			Any    	=       json.load(open(cached_download(hf_hub_url(a_       ,      a_       ,      repo_type='dataset'		)		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			str    	=       {int(a_		): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE					:			int    	=       idalabel
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {v: k for k, v in idalabel.items()}

     return config


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       []

     # 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     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       dct.pop(a_		)
     SCREAMING_SNAKE_CASE					:			str    	=       val


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			str    	=       [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					:			Optional[Any]    	=       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					:			Dict    	=       state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""		)
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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					:			Tuple    	=       in_proj_weight[:dim, :]
               SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_bias[: dim]
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_weight[
                   dim : dim * 2, :
               ]
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       in_proj_bias[
                   dim : dim * 2
               ]
               SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_weight[
                   -dim :, :
               ]
               SCREAMING_SNAKE_CASE					:			Any    	=       in_proj_bias[-dim :]
               # fmt: on


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       config.d_model
     for i in range(config.decoder_layers		):
          # read in weights + bias of input projection layer of self-attention
          SCREAMING_SNAKE_CASE					:			Tuple    	=       state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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					:			Optional[int]    	=       in_proj_weight[:hidden_size, :]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_proj_bias[:hidden_size]
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_weight[
              hidden_size : hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE					:			str    	=       in_proj_bias[hidden_size : hidden_size * 2]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_weight[-hidden_size:, :]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_bias[-hidden_size:]


def     __lowerCAmelCase						(       )      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE					:			Dict    	=       Image.open(requests.get(a_       ,      stream=a_		).raw		)

     return im


@torch.no_grad()
def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			str    	=       get_deta_config(a_		)

     # load original state dict
     if model_name == "deta-swin-large":
          SCREAMING_SNAKE_CASE					:			Dict    	=       hf_hub_download(repo_id='nielsr/deta-checkpoints'       ,      filename='adet_swin_ft.pth'		)
     elif model_name == "deta-swin-large-o365":
          SCREAMING_SNAKE_CASE					:			Tuple    	=       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					:			Dict    	=       torch.load(a_       ,      map_location='cpu'		)['model']

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

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

     # 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					:			List[str]    	=       state_dict.pop(a_		)
               SCREAMING_SNAKE_CASE					:			Any    	=       val
          if "input_proj" in key:
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       state_dict.pop(a_		)
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       val
          if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
               SCREAMING_SNAKE_CASE					:			str    	=       state_dict.pop(a_		)
               SCREAMING_SNAKE_CASE					:			Any    	=       val

    # finally, create HuggingFace model and load state dict
     SCREAMING_SNAKE_CASE					:			str    	=       DetaForObjectDetection(a_		)
     model.load_state_dict(a_		)
     model.eval()

     SCREAMING_SNAKE_CASE					:			str    	=       'cuda' if torch.cuda.is_available() else 'cpu'
     model.to(a_		)

     # load image processor
     SCREAMING_SNAKE_CASE					:			Any    	=       DetaImageProcessor(format='coco_detection'		)

     # verify our conversion on image
     SCREAMING_SNAKE_CASE					:			Tuple    	=       prepare_img()
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       processor(images=a_       ,      return_tensors='pt'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       encoding['pixel_values']
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(pixel_values.to(a_		)		)

     # 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					:			List[str]    	=       torch.tensor(
              [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]		)
          SCREAMING_SNAKE_CASE					:			Any    	=       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					:			str    	=       torch.tensor(
              [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]		)
          SCREAMING_SNAKE_CASE					:			Any    	=       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(a_		)       ,      atol=1e-4		)
     assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,      expected_boxes.to(a_		)       ,      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(a_		).mkdir(exist_ok=a_		)
          model.save_pretrained(a_		)
          processor.save_pretrained(a_		)

     # 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__":
 _lowerCAmelCase  :Optional[Any]  				=    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."""
 )
 _lowerCAmelCase  :Optional[int]  				=    parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()    ,							{} )
         self.assertDictEqual(MockClass(a=2 ).to_kwargs()    ,							{'a': 2} )
         self.assertDictEqual(MockClass(a=2    ,							b=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         self.assertEqual(scaler._growth_factor    ,							2.0 )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor    ,							0.5 )
         self.assertEqual(scaler._growth_interval    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


if __name__ == "__main__":
 _lowerCAmelCase  :int  				=    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 _lowerCAmelCase  :int  				=    Accelerator(kwargs_handlers=[ddp_scaler])
 _lowerCAmelCase  :str  				=    torch.nn.Linear(100, 200)
 _lowerCAmelCase  :Optional[Any]  				=    accelerator.prepare(model)

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)



