




"""simple docstring"""






import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		snake_case			:					str    ):
					_lowerCAmelCase:str									=  SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224'''      ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
					_lowerCAmelCase:int									=  MaskFormerConfig(backbone_config=snake_case    )

					_lowerCAmelCase:Optional[int]									=  '''huggingface/label-files'''
					if "ade20k-full" in model_name:
										# this should be ok
										_lowerCAmelCase:Optional[Any]									=  847
										_lowerCAmelCase:List[str]									=  '''maskformer-ade20k-full-id2label.json'''
					elif "ade" in model_name:
										# this should be ok
										_lowerCAmelCase:List[str]									=  150
										_lowerCAmelCase:Optional[int]									=  '''ade20k-id2label.json'''
					elif "coco-stuff" in model_name:
										# this should be ok
										_lowerCAmelCase:int									=  171
										_lowerCAmelCase:List[str]									=  '''maskformer-coco-stuff-id2label.json'''
					elif "coco" in model_name:
										# TODO
										_lowerCAmelCase:str									=  133
										_lowerCAmelCase:Dict									=  '''coco-panoptic-id2label.json'''
					elif "cityscapes" in model_name:
										# this should be ok
										_lowerCAmelCase:Tuple									=  19
										_lowerCAmelCase:int									=  '''cityscapes-id2label.json'''
					elif "vistas" in model_name:
										# this should be ok
										_lowerCAmelCase:int									=  65
										_lowerCAmelCase:int									=  '''mapillary-vistas-id2label.json'''

					_lowerCAmelCase:Union[str, Any]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Optional[int]									=  {int(snake_case    ): v for k, v in idalabel.items()}

					return config





def 							UpperCAmelCase		(		snake_case			:					List[Any]    ):
					_lowerCAmelCase:int									=  []
					# stem
					# fmt: off
					rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')    )
					rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')    )
					rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')    )
					rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')    )
															rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')    )

										if i < 3:
															rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')    )
															rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')    )
															rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')    )
										rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')    )
										rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')    )

					# FPN
					rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')    )
					rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')    )
					rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')    )
					for source_index, target_index in zip(range(3      ,       0      ,       -1    )      ,       range(0      ,       3    )    ):
										rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')    )
										rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')    )
										rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')    )
										rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')    )
										rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')    )
										rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')    )
					rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')    )
					rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')    )

					# Transformer decoder
					for idx in range(config.decoder_config.decoder_layers    ):
										# self-attention out projection
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')    )
										# cross-attention out projection
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')    )
										# MLP 1
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')    )
										# MLP 2
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')    )
										# layernorm 1 (self-attention layernorm)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')    )
										# layernorm 2 (cross-attention layernorm)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')    )
										# layernorm 3 (final layernorm)
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')    )

					rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')    )
					rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')    )

					# heads on top
					rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')    )

					rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')    )
					rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')    )

					rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')    )
					rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')    )

					for i in range(3    ):
										rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')    )
										rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')    )
					# fmt: on

					return rename_keys





def 							UpperCAmelCase		(		snake_case			:					Dict      ,       snake_case			:					Tuple      ,       snake_case			:					Dict    ):
					_lowerCAmelCase:List[Any]									=  dct.pop(snake_case    )
					_lowerCAmelCase:Tuple									=  val





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					List[Any]    ):
					_lowerCAmelCase:Union[str, Any]									=  [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
					for i in range(len(backbone_config.depths    )    ):
										_lowerCAmelCase: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)
															_lowerCAmelCase:str									=  state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'    )
															_lowerCAmelCase:Any									=  state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'    )
															# next, add query, keys and values (in that order) to the state dict
															_lowerCAmelCase:Optional[int]									=  in_proj_weight[:dim, :]
															_lowerCAmelCase:List[Any]									=  in_proj_bias[: dim]
															_lowerCAmelCase:Optional[Any]									=  in_proj_weight[
															    dim : dim * 2, :
															]
															_lowerCAmelCase:List[Any]									=  in_proj_bias[
															    dim : dim * 2
															]
															_lowerCAmelCase:List[str]									=  in_proj_weight[
															    -dim :, :
															]
															_lowerCAmelCase:Optional[int]									=  in_proj_bias[-dim :]
															# fmt: on





def 							UpperCAmelCase		(		snake_case			:					List[Any]      ,       snake_case			:					Any    ):
					# fmt: off
					_lowerCAmelCase:List[Any]									=  config.decoder_config.hidden_size
					for idx in range(config.decoder_config.decoder_layers    ):
										# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
										_lowerCAmelCase:Tuple									=  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'    )
										_lowerCAmelCase:Optional[Any]									=  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'    )
										# next, add query, keys and values (in that order) to the state dict
										_lowerCAmelCase:int									=  in_proj_weight[: hidden_size, :]
										_lowerCAmelCase:Optional[int]									=  in_proj_bias[:config.hidden_size]
										_lowerCAmelCase:Optional[Any]									=  in_proj_weight[hidden_size : hidden_size * 2, :]
										_lowerCAmelCase:Optional[int]									=  in_proj_bias[hidden_size : hidden_size * 2]
										_lowerCAmelCase:Tuple									=  in_proj_weight[-hidden_size :, :]
										_lowerCAmelCase:str									=  in_proj_bias[-hidden_size :]
										# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
										_lowerCAmelCase:Optional[Any]									=  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'    )
										_lowerCAmelCase:Dict									=  state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'    )
										# next, add query, keys and values (in that order) to the state dict
										_lowerCAmelCase:Optional[int]									=  in_proj_weight[: hidden_size, :]
										_lowerCAmelCase:Any									=  in_proj_bias[:config.hidden_size]
										_lowerCAmelCase:List[str]									=  in_proj_weight[hidden_size : hidden_size * 2, :]
										_lowerCAmelCase:int									=  in_proj_bias[hidden_size : hidden_size * 2]
										_lowerCAmelCase:int									=  in_proj_weight[-hidden_size :, :]
										_lowerCAmelCase:List[Any]									=  in_proj_bias[-hidden_size :]
					# fmt: on





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Union[str, Any]									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:List[str]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )
					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str      ,       snake_case			:					str      ,       snake_case			:					bool = False    ):
					_lowerCAmelCase:str									=  get_maskformer_config(snake_case    )

					# load original state_dict
					with open(snake_case      ,       '''rb'''    ) as f:
										_lowerCAmelCase:Optional[int]									=  pickle.load(snake_case    )
					_lowerCAmelCase:Dict									=  data['''model''']

					# for name, param in state_dict.items():
					#     print(name, param.shape)

					# rename keys
					_lowerCAmelCase:List[Any]									=  create_rename_keys(snake_case    )
					for src, dest in rename_keys:
										rename_key(snake_case      ,       snake_case      ,       snake_case    )
					read_in_swin_q_k_v(snake_case      ,       config.backbone_config    )
					read_in_decoder_q_k_v(snake_case      ,       snake_case    )

					# update to torch tensors
					for key, value in state_dict.items():
										_lowerCAmelCase:Any									=  torch.from_numpy(snake_case    )

					# load 🤗 model
					_lowerCAmelCase:Dict									=  MaskFormerForInstanceSegmentation(snake_case    )
					model.eval()

					for name, param in model.named_parameters():
										print(snake_case      ,       param.shape    )

					_lowerCAmelCase							,   _lowerCAmelCase:List[str]									=  model.load_state_dict(snake_case      ,       strict=snake_case    )
					assert missing_keys == [
					    "model.pixel_level_module.encoder.model.layernorm.weight",
					    "model.pixel_level_module.encoder.model.layernorm.bias",
					]
					assert len(snake_case    ) == 0, F'Unexpected keys: {unexpected_keys}'

					# verify results
					_lowerCAmelCase:List[Any]									=  prepare_img()
					if "vistas" in model_name:
										_lowerCAmelCase:Union[str, Any]									=  65
					elif "cityscapes" in model_name:
										_lowerCAmelCase:Dict									=  65535
					else:
										_lowerCAmelCase:Tuple									=  255
					_lowerCAmelCase:Optional[int]									=  True if '''ade''' in model_name else False
					_lowerCAmelCase:Union[str, Any]									=  MaskFormerImageProcessor(ignore_index=snake_case      ,       reduce_labels=snake_case    )

					_lowerCAmelCase:Any									=  image_processor(snake_case      ,       return_tensors='''pt'''    )

					_lowerCAmelCase:Any									=  model(**snake_case    )

					print('''Logits:'''      ,       outputs.class_queries_logits[0, :3, :3]    )

					if model_name == "maskformer-swin-tiny-ade":
										_lowerCAmelCase:str									=  torch.tensor(
										    [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]    )
					assert torch.allclose(outputs.class_queries_logits[0, :3, :3]      ,       snake_case      ,       atol=1e-4    )
					print('''Looks ok!'''    )

					if pytorch_dump_folder_path is not None:
										print(F'Saving model and image processor to {pytorch_dump_folder_path}'    )
										Path(snake_case    ).mkdir(exist_ok=snake_case    )
										model.save_pretrained(snake_case    )
										image_processor.save_pretrained(snake_case    )

					if push_to_hub:
										print('''Pushing model and image processor to the hub...'''    )
										model.push_to_hub(F'nielsr/{model_name}'    )
										image_processor.push_to_hub(F'nielsr/{model_name}'    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''maskformer-swin-tiny-ade''',
				    type=str,
				    help=('''Name of the MaskFormer model you\'d like to convert''',),
				)
				parser.add_argument(
				    '''--checkpoint_path''',
				    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
				    type=str,
				    help='''Path to the original state dict (.pth file).''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, 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_maskformer_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)












"""simple docstring"""






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:List[Any]									=  MobileNetVaConfig(layer_norm_eps=0.0_01    )

					if "_quant" in model_name:
										raise ValueError('''Quantized models are not supported.'''    )

					_lowerCAmelCase:List[Any]									=  re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,       snake_case    )
					if matches:
										_lowerCAmelCase:str									=  float(matches[1]    )
										_lowerCAmelCase:str									=  int(matches[2]    )

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					_lowerCAmelCase:int									=  1001
					_lowerCAmelCase:Union[str, Any]									=  '''imagenet-1k-id2label.json'''
					_lowerCAmelCase:Optional[int]									=  '''huggingface/label-files'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Union[str, Any]									=  {int(snake_case    ) + 1: v for k, v in idalabel.items()}
					_lowerCAmelCase:Optional[Any]									=  '''background'''
					_lowerCAmelCase:Optional[Any]									=  idalabel
					_lowerCAmelCase:List[Any]									=  {v: k for k, v in idalabel.items()}

					return config





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:Optional[Any]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )
					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[Any]      ,       snake_case			:					Any=False    ):
					_lowerCAmelCase:List[str]									=  get_mobilenet_va_config(snake_case    )

					# Load 🤗 model
					_lowerCAmelCase:Any									=  MobileNetVaForImageClassification(snake_case    ).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(snake_case      ,       snake_case      ,       snake_case    )

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					_lowerCAmelCase:Dict									=  MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,       size={'''shortest_edge''': config.image_size + 32}      ,       )
					_lowerCAmelCase:List[str]									=  image_processor(images=prepare_img()      ,       return_tensors='''pt'''    )
					_lowerCAmelCase:List[str]									=  model(**snake_case    )
					_lowerCAmelCase:int									=  outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor([-4.17_39, -1.12_33, 3.12_05]    )
					elif model_name == "mobilenet_v1_0.75_192":
										_lowerCAmelCase:Any									=  torch.tensor([-3.94_40, -2.31_41, -0.33_33]    )
					else:
										_lowerCAmelCase:Dict									=  None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]      ,       snake_case      ,       atol=1e-4    )

					Path(snake_case    ).mkdir(exist_ok=snake_case    )
					print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
					model.save_pretrained(snake_case    )
					print(F'Saving image processor to {pytorch_dump_folder_path}'    )
					image_processor.save_pretrained(snake_case    )

					if push_to_hub:
										print('''Pushing to the hub...'''    )
										_lowerCAmelCase:List[str]									=  '''google/''' + model_name
										image_processor.push_to_hub(snake_case    )
										model.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''mobilenet_v1_1.0_224''',
				    type=str,
				    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
				)
				parser.add_argument(
				    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt 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.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






