



'''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()
snake_case_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
    # initialize config
    if "resnet-50" in model_name:
        SCREAMING_SNAKE_CASE  :						str      					= ResNetConfig.from_pretrained('''microsoft/resnet-50'''       )
    elif "resnet-101" in model_name:
        SCREAMING_SNAKE_CASE  :						str      					= ResNetConfig.from_pretrained('''microsoft/resnet-101'''       )
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''       )

    SCREAMING_SNAKE_CASE  :						Any      					= DetrConfig(use_timm_backbone=_SCREAMING_SNAKE_CASE    ,	backbone_config=_SCREAMING_SNAKE_CASE       )

    # set label attributes
    SCREAMING_SNAKE_CASE  :						List[Any]      					= '''panoptic''' in model_name
    if is_panoptic:
        SCREAMING_SNAKE_CASE  :						Any      					= 2_50
    else:
        SCREAMING_SNAKE_CASE  :						str      					= 91
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''huggingface/label-files'''
        SCREAMING_SNAKE_CASE  :						List[str]      					= '''coco-detection-id2label.json'''
        SCREAMING_SNAKE_CASE  :						List[str]      					= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE  :						List[str]      					= idalabel
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= {v: k for k, v in idalabel.items()}

    return config, is_panoptic






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
    # here we list all keys to be renamed (original name on the left, our name on the right)
    SCREAMING_SNAKE_CASE  :						int      					= []

    # 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       __lowercase  (_SCREAMING_SNAKE_CASE      :Union[str, Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Any       ):
    SCREAMING_SNAKE_CASE  :						Tuple      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE  :						Any      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=False       ):
    SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ''''''
    if is_panoptic:
        SCREAMING_SNAKE_CASE  :						List[Any]      					= '''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)
        SCREAMING_SNAKE_CASE  :						Tuple      					= state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''       )
        SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 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
        SCREAMING_SNAKE_CASE  :						str      					= in_proj_weight[:2_56, :]
        SCREAMING_SNAKE_CASE  :						str      					= in_proj_bias[:2_56]
        SCREAMING_SNAKE_CASE  :						int      					= in_proj_weight[2_56:5_12, :]
        SCREAMING_SNAKE_CASE  :						List[str]      					= in_proj_bias[2_56:5_12]
        SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight[-2_56:, :]
        SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias[-2_56:]
    # 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
        SCREAMING_SNAKE_CASE  :						Optional[int]      					= state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''       )
        SCREAMING_SNAKE_CASE  :						Dict      					= 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
        SCREAMING_SNAKE_CASE  :						str      					= in_proj_weight[:2_56, :]
        SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_bias[:2_56]
        SCREAMING_SNAKE_CASE  :						List[str]      					= in_proj_weight[2_56:5_12, :]
        SCREAMING_SNAKE_CASE  :						int      					= in_proj_bias[2_56:5_12]
        SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_weight[-2_56:, :]
        SCREAMING_SNAKE_CASE  :						Optional[Any]      					= in_proj_bias[-2_56:]
        # read in weights + bias of input projection layer of cross-attention
        SCREAMING_SNAKE_CASE  :						List[str]      					= state_dict.pop(
            F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''       )
        SCREAMING_SNAKE_CASE  :						Tuple      					= 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
        SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight_cross_attn[:2_56, :]
        SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_bias_cross_attn[:2_56]
        SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight_cross_attn[2_56:5_12, :]
        SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= in_proj_bias_cross_attn[2_56:5_12]
        SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight_cross_attn[-2_56:, :]
        SCREAMING_SNAKE_CASE  :						Tuple      					= in_proj_bias_cross_attn[-2_56:]






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

    return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=None    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=False       ):
    SCREAMING_SNAKE_CASE  :						Any      					= get_detr_config(_SCREAMING_SNAKE_CASE       )

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

    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE  :						Optional[int]      					= DetrForSegmentation(_SCREAMING_SNAKE_CASE       ) if is_panoptic else DetrForObjectDetection(_SCREAMING_SNAKE_CASE       )
    model.load_state_dict(_SCREAMING_SNAKE_CASE       )
    model.eval()

    # verify our conversion on an image
    SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    SCREAMING_SNAKE_CASE  :						Optional[Any]      					= DetrImageProcessor(format=_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE  :						str      					= processor(images=prepare_img()    ,	return_tensors='''pt'''       )
    SCREAMING_SNAKE_CASE  :						Optional[Any]      					= encoding['''pixel_values''']

    SCREAMING_SNAKE_CASE  :						Dict      					= detr(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model(_SCREAMING_SNAKE_CASE       )

    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(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
        model.save_pretrained(_SCREAMING_SNAKE_CASE       )
        processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

    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__":
 snake_case_			=     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.""")
 snake_case_			=     parser.parse_args()
 convert_detr_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 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     0
	__magic_name__		:    bool	    =     False
	__magic_name__		:    float	    =     3.0



class 	a__     (				unittest.TestCase	):


	def 				lowercase__  (self : str      )     ->			Union[str, Any]:

					"""simple docstring"""

					self.assertDictEqual(MockClass().to_kwargs(),					{}      )
					self.assertDictEqual(MockClass(a=2      ).to_kwargs(),					{'''a''': 2}      )
					self.assertDictEqual(MockClass(a=2,					b=__UpperCAmelCase      ).to_kwargs(),					{'''a''': 2, '''b''': True}      )
					self.assertDictEqual(MockClass(a=2,					c=2.25      ).to_kwargs(),					{'''a''': 2, '''c''': 2.25}      )


	@require_cuda
	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= GradScalerKwargs(init_scale=1024,					growth_factor=2      )
					AcceleratorState._reset_state()
					SCREAMING_SNAKE_CASE  :						Tuple      					= Accelerator(mixed_precision='''fp16''',					kwargs_handlers=[scaler_handler]      )
					print(accelerator.use_fpaa      )
					SCREAMING_SNAKE_CASE  :						str      					= accelerator.scaler

					# Check the kwargs have been applied
					self.assertEqual(scaler._init_scale,					1024.0      )
					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,					2000      )
					self.assertEqual(scaler._enabled,					__UpperCAmelCase      )






	@require_multi_gpu
	def 				lowercase__  (self : Optional[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
					execute_subprocess_async(__UpperCAmelCase,					env=os.environ.copy()      )


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

	# Check the values changed in kwargs
	snake_case_			=     """"""
	snake_case_			=     model.bucket_bytes_cap // (1024 * 1024)
	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)




