


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


def    _snake_case  (	A			)		->       str:
							lowerCAmelCase__			  =      MobileViTConfig()

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

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

							lowerCAmelCase__			  =      '''huggingface/label-files'''
							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


def    _snake_case  (	A					,       A=False			)		->       str:
							for i in range(1					,       6			):
														if F"""layer_{i}.""" in name:
																					lowerCAmelCase__			  =      name.replace(F"""layer_{i}."""					,       F"""encoder.layer.{i - 1}."""			)

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

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

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

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

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

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

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

							return name


def    _snake_case  (	A					,       A					,       A=False			)		->       Tuple:
							if base_model:
														lowerCAmelCase__			  =      ''''''
							else:
														lowerCAmelCase__			  =      '''mobilevit.'''

							for key in orig_state_dict.copy().keys():
														lowerCAmelCase__			  =      orig_state_dict.pop(A			)

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

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

							return orig_state_dict


def    _snake_case  (	)		->       Dict:
							lowerCAmelCase__			  =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__			  =      Image.open(requests.get(A					,       stream=A			).raw			)
							return im


@torch.no_grad()
def    _snake_case  (	A					,       A					,       A					,       A=False			)		->       int:
							lowerCAmelCase__			  =      get_mobilevit_config(A			)

							# load original state_dict
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)

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

							lowerCAmelCase__			  =      convert_state_dict(A					,       A			)
							model.load_state_dict(A			)

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

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																					        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																					        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																					    ]			)
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																					        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																					        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																					    ]			)
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																					        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																					        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																					    ]			)
														else:
																					raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""			)

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

														if mobilevit_name == "mobilevit_s":
																					lowerCAmelCase__			  =      torch.tensor([-0.9_866, 0.2_392, -1.1_241]			)
														elif mobilevit_name == "mobilevit_xs":
																					lowerCAmelCase__			  =      torch.tensor([-2.4_761, -0.9_399, -1.9_587]			)
														elif mobilevit_name == "mobilevit_xxs":
																					lowerCAmelCase__			  =      torch.tensor([-1.9_364, -1.2_327, -0.4_653]			)
														else:
																					raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""			)

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

							Path(A			).mkdir(exist_ok=A			)
							print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""			)
							model.save_pretrained(A			)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
							image_processor.save_pretrained(A			)

							if push_to_hub:
														lowerCAmelCase__			  =      {
														    '''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...'''			)
														lowerCAmelCase__			  =      model_mapping[mobilevit_name]
														image_processor.push_to_hub(A					,       organization='''apple'''			)
														model.push_to_hub(A					,       organization='''apple'''			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				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.'''
		)

		__UpperCAmelCase       							=				parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)



'''simple docstring'''






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory


def    _snake_case  (	)		->       Union[str, Any]:
							raise RuntimeError('''CUDA out of memory.'''			)




class 						a__							(				nn.Module    ):





							'''simple docstring'''






							def __init__(						self  )			->				int:
														super().__init__()
														lowerCAmelCase__			  =      nn.Linear(3   ,       4  )
														lowerCAmelCase__			  =      nn.BatchNormad(4  )
														lowerCAmelCase__			  =      nn.Linear(4   ,       5  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				Optional[Any]:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_  )  )  )




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()

														mock_training_loop_function()
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      []

														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_  ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_  )
																					if batch_size != 8:
																												raise_fake_out_of_memory()
																					return batch_size, arga

														lowerCAmelCase__  ,   lowerCAmelCase__			  =      mock_training_loop_function('''hello'''  )
														self.assertListEqual(lowerCamelCase_   ,       [1_28, 64, 32, 16, 8]  )
														self.assertListEqual([bs, arga]   ,       [8, '''hello''']  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														@find_executable_batch_size(starting_batch_size=0  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					if batch_size > 0:
																												raise_fake_out_of_memory()
																					pass

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''No executable batch size found, reached zero.'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														@find_executable_batch_size(starting_batch_size=1_28  )
														def mock_training_loop_function(lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  ):
																					if batch_size != 8:
																												raise raise_fake_out_of_memory()

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function(1_28   ,       '''hello'''   ,       '''world'''  )
																					self.assertIn('''Batch size was passed into `f`'''   ,       cm.exception.args[0]  )
																					self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''   ,       cm.exception.args[0]  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16  )
														def mock_training_loop_function(lowerCamelCase_  ):
																					raise ValueError('''Oops, we had an error!'''  )

														with self.assertRaises(lowerCamelCase_  ) as cm:
																					mock_training_loop_function()
																					self.assertIn('''Oops, we had an error!'''   ,       cm.exception.args[0]  )







							@require_cuda
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      torch.cuda.memory_allocated()
														lowerCAmelCase__			  =      ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )
														lowerCAmelCase__			  =      release_memory(lowerCamelCase_  )
														self.assertEqual(torch.cuda.memory_allocated()   ,       lowerCamelCase_  )