






"""simple docstring"""







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					for param, grad_param in zip(model_a.parameters() ,     model_b.parameters()):
										if not param.requires_grad:
															continue
										if not did_step:
															# Grads should not be in sync
															assert (
															    torch.allclose(param.grad ,     grad_param.grad) is False
															), f'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
										else:
															# Grads should be in sync
															assert (
															    torch.allclose(param.grad ,     grad_param.grad) is True
															), f'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=True):
					model.train()
					a__          =  model(lowerCamelCase_)
					a__          =  F.mse_loss(lowerCamelCase_ ,     target.to(output.device))
					if not do_backward:
										loss /= accelerator.gradient_accumulation_steps
										loss.backward()
					else:
										accelerator.backward(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					set_seed(42)
					a__          =  RegressionModel()
					a__          =  deepcopy(lowerCamelCase_)
					a__          =  RegressionDataset(length=80)
					a__          =  DataLoader(lowerCamelCase_ ,     batch_size=16)
					model.to(accelerator.device)
					if sched:
										a__          =  AdamW(params=model.parameters() ,     lr=1E-3)
										a__          =  AdamW(params=ddp_model.parameters() ,     lr=1E-3)
										a__          =  LambdaLR(lowerCamelCase_ ,     lr_lambda=lambda lowerCamelCase_: epoch**0.65)
										a__          =  LambdaLR(lowerCamelCase_ ,     lr_lambda=lambda lowerCamelCase_: epoch**0.65)
					# Make a copy of `model`
					if sched:
										a__       ,a__       ,a__       ,a__          =  accelerator.prepare(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					else:
										a__       ,a__          =  accelerator.prepare(lowerCamelCase_ ,     lowerCamelCase_)
					if sched:
										return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
					return model, ddp_model, dataloader





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					# Test when on a single CPU or GPU that the context manager does nothing
					a__       ,a__       ,a__          =  get_training_setup(lowerCamelCase_)
					# Use a single batch
					a__       ,a__          =  next(iter(lowerCamelCase_)).values()
					for iteration in range(3):
										# Gather the distributed inputs and targs for the base model
										a__       ,a__          =  accelerator.gather((ddp_input, ddp_target))
										a__       ,a__          =  input.to(accelerator.device), target.to(accelerator.device)
										# Perform our initial ground truth step in non "DDP"
										step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(lowerCamelCase_):
																				step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										else:
															# Sync grads
															step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

										# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
										check_model_parameters(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										for param, ddp_param in zip(model.parameters() ,     ddp_model.parameters()):
															if not param.requires_grad:
																				continue
															assert torch.allclose(
															    param.grad ,     ddp_param.grad), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration)
										a__          =  ddp_input[torch.randperm(len(lowerCamelCase_))]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					# Test on distributed setup that context manager behaves properly
					a__       ,a__       ,a__          =  get_training_setup(lowerCamelCase_)
					# Use a single batch
					a__       ,a__          =  next(iter(lowerCamelCase_)).values()
					for iteration in range(3):
										# Gather the distributed inputs and targs for the base model
										a__       ,a__          =  accelerator.gather((ddp_input, ddp_target))
										a__       ,a__          =  input.to(accelerator.device), target.to(accelerator.device)
										# Perform our initial ground truth step in non "DDP"
										step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										# Do "gradient accumulation" (noop)
										if iteration % 2 == 0:
															# Accumulate grads locally
															with accelerator.no_sync(lowerCamelCase_):
																				step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										else:
															# Sync grads
															step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters() ,     ddp_model.parameters()):
															if not param.requires_grad:
																				continue
															if iteration % 2 == 0:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad ,     ddp_param.grad) is False
																				), f'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
															else:
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad ,     ddp_param.grad) is True
																				), f'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration)
										a__          =  ddp_input[torch.randperm(len(lowerCamelCase_))]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_=False ,     lowerCamelCase_=False):
					a__          =  Accelerator(
					    split_batches=lowerCamelCase_ ,     dispatch_batches=lowerCamelCase_ ,     gradient_accumulation_steps=2)
					# Test that context manager behaves properly
					a__       ,a__       ,a__          =  get_training_setup(lowerCamelCase_)
					for iteration, batch in enumerate(lowerCamelCase_):
										a__       ,a__          =  batch.values()
										# Gather the distributed inputs and targs for the base model
										a__       ,a__          =  accelerator.gather((ddp_input, ddp_target))
										a__       ,a__          =  input.to(accelerator.device), target.to(accelerator.device)
										# Perform our initial ground truth step in non "DDP"
										step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										# Do "gradient accumulation" (noop)
										with accelerator.accumulate(lowerCamelCase_):
															step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)

										# DDP model and model should only be in sync when not (iteration % 2 == 0)
										for param, ddp_param in zip(model.parameters() ,     ddp_model.parameters()):
															if not param.requires_grad:
																				continue
															if ((iteration + 1) % 2 == 0) or (iteration == len(lowerCamelCase_) - 1):
																				# Grads should be in sync
																				assert (
																				    torch.allclose(param.grad ,     ddp_param.grad) is True
																				), f'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
															else:
																				# Grads should not be in sync
																				assert (
																				    torch.allclose(param.grad ,     ddp_param.grad) is False
																				), f'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration)
										a__          =  ddp_input[torch.randperm(len(lowerCamelCase_))]
					GradientState._reset_state()





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_=False ,     lowerCamelCase_=False):
					a__          =  Accelerator(
					    split_batches=lowerCamelCase_ ,     dispatch_batches=lowerCamelCase_ ,     gradient_accumulation_steps=2)
					# Test that context manager behaves properly
					a__       ,a__       ,a__       ,a__       ,a__       ,a__       ,a__          =  get_training_setup(lowerCamelCase_ ,     lowerCamelCase_)
					for iteration, batch in enumerate(lowerCamelCase_):
										a__       ,a__          =  batch.values()
										# Gather the distributed inputs and targs for the base model
										a__       ,a__          =  accelerator.gather((ddp_input, ddp_target))
										a__       ,a__          =  input.to(accelerator.device), target.to(accelerator.device)
										# Perform our initial ground truth step in non "DDP"
										model.train()
										ddp_model.train()
										step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										opt.step()

										if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowerCamelCase_)):
															if split_batches:
																				sched.step()
															else:
																				for _ in range(accelerator.num_processes):
																									sched.step()
										opt.zero_grad()
										# Perform gradient accumulation under wrapper
										with accelerator.accumulate(lowerCamelCase_):
															step_model(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
															ddp_opt.step()
															ddp_sched.step()
															ddp_opt.zero_grad()

										# Learning rates should be the same
										assert (
										    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
										), f'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
										a__          =  (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowerCamelCase_))
										if accelerator.num_processes > 1:
															check_model_parameters(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
										# Shuffle ddp_input on each iteration
										torch.manual_seed(1337 + iteration)
					GradientState._reset_state()





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  Accelerator()

					a__          =  RegressionDataset(length=80)
					a__          =  DataLoader(lowerCamelCase_ ,     batch_size=16)
					a__          =  RegressionDataset(length=96)
					a__          =  DataLoader(lowerCamelCase_ ,     batch_size=16)
					a__       ,a__          =  accelerator.prepare(lowerCamelCase_ ,     lowerCamelCase_)
					assert accelerator.gradient_state.active_dataloader is None
					for iteration, _ in enumerate(lowerCamelCase_):
										assert id(accelerator.gradient_state.active_dataloader) == id(lowerCamelCase_)
										if iteration < len(lowerCamelCase_) - 1:
															assert not accelerator.gradient_state.end_of_dataloader
															if iteration == 1:
																				for batch_num, _ in enumerate(lowerCamelCase_):
																									assert id(accelerator.gradient_state.active_dataloader) == id(lowerCamelCase_)
																									if batch_num < len(lowerCamelCase_) - 1:
																														assert not accelerator.gradient_state.end_of_dataloader
																									else:
																														assert accelerator.gradient_state.end_of_dataloader
										else:
															assert accelerator.gradient_state.end_of_dataloader
					assert accelerator.gradient_state.active_dataloader is None





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  Accelerator()
					a__          =  accelerator.state
					if state.local_process_index == 0:
										print('''**Test `accumulate` gradient accumulation with dataloader break**''')
					test_dataloader_break()
					if state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print('''**Test NOOP `no_sync` context manager**''')
										test_noop_sync(lowerCamelCase_)
					if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
										if state.local_process_index == 0:
															print('''**Test Distributed `no_sync` context manager**''')
										test_distributed_sync(lowerCamelCase_)
					if state.distributed_type == DistributedType.MULTI_GPU:
										for split_batch in [True, False]:
															for dispatch_batches in [True, False]:
																				if state.local_process_index == 0:
																									print(
																									    '''**Test `accumulate` gradient accumulation, ''' ,     f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**' ,     )
																				test_gradient_accumulation(lowerCamelCase_ ,     lowerCamelCase_)

    # Currently will break on torch 2.0 +, need to investigate why
					if is_torch_version('''<''' ,     '''2.0''') or state.distributed_type == DistributedType.NO:
										if state.local_process_index == 0:
															print(
															    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,     '''`split_batches=False`, `dispatch_batches=False`**''' ,     )
										test_gradient_accumulation_with_opt_and_scheduler()
										if state.distributed_type == DistributedType.MULTI_GPU:
															for split_batch in [True, False]:
																				for dispatch_batches in [True, False]:
																									if not split_batch and not dispatch_batches:
																														continue
																									if state.local_process_index == 0:
																														print(
																														    '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,     f'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**' ,     )
																									test_gradient_accumulation_with_opt_and_scheduler(lowerCamelCase_ ,     lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
	main()










"""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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										a__          =  [144, 192, 240]
										a__          =  [16, 32, 64, 96, 128, 160, 640]
					elif "mobilevit_xs" in mobilevit_name:
										a__          =  [96, 120, 144]
										a__          =  [16, 32, 48, 64, 80, 96, 384]
					elif "mobilevit_xxs" in mobilevit_name:
										a__          =  [64, 80, 96]
										a__          =  [16, 16, 24, 48, 64, 80, 320]
										a__          =  0.05
										a__          =  2.0

					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  512
										a__          =  16
										a__          =  21
										a__          =  '''pascal-voc-id2label.json'''
					else:
										a__          =  1000
										a__          =  '''imagenet-1k-id2label.json'''

					a__          =  '''huggingface/label-files'''
					a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
					a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
					a__          =  idalabel
					a__          =  {v: k for k, v in idalabel.items()}

					return config





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					for i in range(1 ,     6):
										if f'layer_{i}.' in name:
															a__          =  name.replace(f'layer_{i}.' ,     f'encoder.layer.{i - 1}.')

					if "conv_1." in name:
										a__          =  name.replace('''conv_1.''' ,     '''conv_stem.''')
					if ".block." in name:
										a__          =  name.replace('''.block.''' ,     '''.''')

					if "exp_1x1" in name:
										a__          =  name.replace('''exp_1x1''' ,     '''expand_1x1''')
					if "red_1x1" in name:
										a__          =  name.replace('''red_1x1''' ,     '''reduce_1x1''')
					if ".local_rep.conv_3x3." in name:
										a__          =  name.replace('''.local_rep.conv_3x3.''' ,     '''.conv_kxk.''')
					if ".local_rep.conv_1x1." in name:
										a__          =  name.replace('''.local_rep.conv_1x1.''' ,     '''.conv_1x1.''')
					if ".norm." in name:
										a__          =  name.replace('''.norm.''' ,     '''.normalization.''')
					if ".conv." in name:
										a__          =  name.replace('''.conv.''' ,     '''.convolution.''')
					if ".conv_proj." in name:
										a__          =  name.replace('''.conv_proj.''' ,     '''.conv_projection.''')

					for i in range(0 ,     2):
										for j in range(0 ,     4):
															if f'.{i}.{j}.' in name:
																				a__          =  name.replace(f'.{i}.{j}.' ,     f'.{i}.layer.{j}.')

					for i in range(2 ,     6):
										for j in range(0 ,     4):
															if f'.{i}.{j}.' in name:
																				a__          =  name.replace(f'.{i}.{j}.' ,     f'.{i}.')
																				if "expand_1x1" in name:
																									a__          =  name.replace('''expand_1x1''' ,     '''downsampling_layer.expand_1x1''')
																				if "conv_3x3" in name:
																									a__          =  name.replace('''conv_3x3''' ,     '''downsampling_layer.conv_3x3''')
																				if "reduce_1x1" in name:
																									a__          =  name.replace('''reduce_1x1''' ,     '''downsampling_layer.reduce_1x1''')

					for i in range(2 ,     5):
										if f'.global_rep.{i}.weight' in name:
															a__          =  name.replace(f'.global_rep.{i}.weight' ,     '''.layernorm.weight''')
										if f'.global_rep.{i}.bias' in name:
															a__          =  name.replace(f'.global_rep.{i}.bias' ,     '''.layernorm.bias''')

					if ".global_rep." in name:
										a__          =  name.replace('''.global_rep.''' ,     '''.transformer.''')
					if ".pre_norm_mha.0." in name:
										a__          =  name.replace('''.pre_norm_mha.0.''' ,     '''.layernorm_before.''')
					if ".pre_norm_mha.1.out_proj." in name:
										a__          =  name.replace('''.pre_norm_mha.1.out_proj.''' ,     '''.attention.output.dense.''')
					if ".pre_norm_ffn.0." in name:
										a__          =  name.replace('''.pre_norm_ffn.0.''' ,     '''.layernorm_after.''')
					if ".pre_norm_ffn.1." in name:
										a__          =  name.replace('''.pre_norm_ffn.1.''' ,     '''.intermediate.dense.''')
					if ".pre_norm_ffn.4." in name:
										a__          =  name.replace('''.pre_norm_ffn.4.''' ,     '''.output.dense.''')
					if ".transformer." in name:
										a__          =  name.replace('''.transformer.''' ,     '''.transformer.layer.''')

					if ".aspp_layer." in name:
										a__          =  name.replace('''.aspp_layer.''' ,     '''.''')
					if ".aspp_pool." in name:
										a__          =  name.replace('''.aspp_pool.''' ,     '''.''')
					if "seg_head." in name:
										a__          =  name.replace('''seg_head.''' ,     '''segmentation_head.''')
					if "segmentation_head.classifier.classifier." in name:
										a__          =  name.replace('''segmentation_head.classifier.classifier.''' ,     '''segmentation_head.classifier.''')

					if "classifier.fc." in name:
										a__          =  name.replace('''classifier.fc.''' ,     '''classifier.''')
					elif (not base_model) and ("segmentation_head." not in name):
										a__          =  '''mobilevit.''' + name

					return name





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					if base_model:
										a__          =  ''''''
					else:
										a__          =  '''mobilevit.'''

					for key in orig_state_dict.copy().keys():
										a__          =  orig_state_dict.pop(lowerCamelCase_)

										if key[:8] == "encoder.":
															a__          =  key[8:]

										if "qkv" in key:
															a__          =  key.split('''.''')
															a__          =  int(key_split[0][6:]) - 1
															a__          =  int(key_split[3])
															a__          =  model.get_submodule(f'{model_prefix}encoder.layer.{layer_num}')
															a__          =  layer.transformer.layer[transformer_num].attention.attention.all_head_size
															a__          =  (
															    f'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
															)
															if "weight" in key:
																				a__          =  val[:dim, :]
																				a__          =  val[dim : dim * 2, :]
																				a__          =  val[-dim:, :]
															else:
																				a__          =  val[:dim]
																				a__          =  val[dim : dim * 2]
																				a__          =  val[-dim:]
										else:
															a__          =  val

					return orig_state_dict





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  get_mobilevit_config(lowerCamelCase_)

					# load original state_dict
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')

					# load 🤗 model
					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  MobileViTForSemanticSegmentation(lowerCamelCase_).eval()
					else:
										a__          =  MobileViTForImageClassification(lowerCamelCase_).eval()

					a__          =  convert_state_dict(lowerCamelCase_ ,     lowerCamelCase_)
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image, prepared by MobileViTImageProcessor
					a__          =  MobileViTImageProcessor(crop_size=config.image_size ,     size=config.image_size + 32)
					a__          =  image_processor(images=prepare_img() ,     return_tensors='''pt''')
					a__          =  model(**lowerCamelCase_)
					a__          =  outputs.logits

					if mobilevit_name.startswith('''deeplabv3_'''):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															a__          =  torch.tensor(
															    [
															        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
															        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
															        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															a__          =  torch.tensor(
															    [
															        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
															        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
															        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															a__          =  torch.tensor(
															    [
															        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
															        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
															        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
															    ])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

										assert torch.allclose(logits[0, :3, :3, :3] ,     lowerCamelCase_ ,     atol=1E-4)
					else:
										assert logits.shape == (1, 1000)

										if mobilevit_name == "mobilevit_s":
															a__          =  torch.tensor([-0.9866, 0.2392, -1.1241])
										elif mobilevit_name == "mobilevit_xs":
															a__          =  torch.tensor([-2.4761, -0.9399, -1.9587])
										elif mobilevit_name == "mobilevit_xxs":
															a__          =  torch.tensor([-1.9364, -1.2327, -0.4653])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

										assert torch.allclose(logits[0, :3] ,     lowerCamelCase_ ,     atol=1E-4)

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
					model.save_pretrained(lowerCamelCase_)
					print(f'Saving image processor to {pytorch_dump_folder_path}')
					image_processor.save_pretrained(lowerCamelCase_)

					if push_to_hub:
										a__          =  {
										    '''mobilevit_s''': '''mobilevit-small''',
										    '''mobilevit_xs''': '''mobilevit-x-small''',
										    '''mobilevit_xxs''': '''mobilevit-xx-small''',
										    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
										    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
										    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
										}

										print('''Pushing to the hub...''')
										a__          =  model_mapping[mobilevit_name]
										image_processor.push_to_hub(lowerCamelCase_ ,     organization='''apple''')
										model.push_to_hub(lowerCamelCase_ ,     organization='''apple''')


if __name__ == "__main__":
	__a       :	Optional[int]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--mobilevit_name',
	    default='mobilevit_s',
	    type=str,
	    help=(
	        'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
	        ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
	    ),
	)
	parser.add_argument(
	    '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	__a       :	List[str]             = parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


