


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			:						List[Any]        =  logging.get_logger(__name__)






def 					UpperCamelCase   (      __magic_name__	:				Dict	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						MobileViTConfig()

		# size of the architecture
		if "mobilevit_s" in mobilevit_name:
				lowercase__	     =						[144, 192, 240]
				lowercase__	     =						[16, 32, 64, 96, 128, 160, 640]
		elif "mobilevit_xs" in mobilevit_name:
				lowercase__	     =						[96, 120, 144]
				lowercase__	     =						[16, 32, 48, 64, 80, 96, 384]
		elif "mobilevit_xxs" in mobilevit_name:
				lowercase__	     =						[64, 80, 96]
				lowercase__	     =						[16, 16, 24, 48, 64, 80, 320]
				lowercase__	     =						0.0_5
				lowercase__	     =						2.0

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

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

		return config






def 					UpperCamelCase   (      __magic_name__	:				Tuple       ,       __magic_name__	:				List[Any]=False	)  ->    List[str]:
		"""simple docstring"""




		for i in range(1       ,       6	):
				if f'''layer_{i}.''' in name:
						lowercase__	     =						name.replace(f'''layer_{i}.'''       ,       f'''encoder.layer.{i - 1}.'''	)

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

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

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

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

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

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

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

		return name






def 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				List[Any]=False	)  ->    List[str]:
		"""simple docstring"""




		if base_model:
				lowercase__	     =						""""""
		else:
				lowercase__	     =						"""mobilevit."""

		for key in orig_state_dict.copy().keys():
				lowercase__	     =						orig_state_dict.pop(__magic_name__	)

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

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

		return orig_state_dict






def 					UpperCamelCase   (      )  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		lowercase__	     =						Image.open(requests.get(__magic_name__       ,       stream=__magic_name__	).raw	)
		return im






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				Optional[int]       ,       __magic_name__	:				Any       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]=False	)  ->    Optional[int]:
		"""simple docstring"""




		lowercase__	     =						get_mobilevit_config(__magic_name__	)

		# load original state_dict
		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)

		# load 🤗 model
		if mobilevit_name.startswith("""deeplabv3_"""	):
				lowercase__	     =						MobileViTForSemanticSegmentation(__magic_name__	).eval()
		else:
				lowercase__	     =						MobileViTForImageClassification(__magic_name__	).eval()

		lowercase__	     =						convert_state_dict(__magic_name__       ,       __magic_name__	)
		model.load_state_dict(__magic_name__	)

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

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

				if mobilevit_name == "deeplabv3_mobilevit_s":
						lowercase__	     =						torch.tensor(
						    [
						        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
						        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
						        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
						    ]	)
				elif mobilevit_name == "deeplabv3_mobilevit_xs":
						lowercase__	     =						torch.tensor(
						    [
						        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
						        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
						        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
						    ]	)
				elif mobilevit_name == "deeplabv3_mobilevit_xxs":
						lowercase__	     =						torch.tensor(
						    [
						        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
						        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
						        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
						    ]	)
				else:
						raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

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

				if mobilevit_name == "mobilevit_s":
						lowercase__	     =						torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]	)
				elif mobilevit_name == "mobilevit_xs":
						lowercase__	     =						torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]	)
				elif mobilevit_name == "mobilevit_xxs":
						lowercase__	     =						torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]	)
				else:
						raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

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

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

		if push_to_hub:
				lowercase__	     =						{
				    """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..."""	)
				lowercase__	     =						model_mapping[mobilevit_name]
				image_processor.push_to_hub(__magic_name__       ,       organization="""apple"""	)
				model.push_to_hub(__magic_name__       ,       organization="""apple"""	)


if __name__ == "__main__":
	A			:						List[str]        =  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			:						Optional[int]        =  parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						List[str]        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''ibert'''

					def __init__(self       :		int ,   _UpperCAmelCase       :		Union[str, Any]=3_0522 ,   _UpperCAmelCase       :		Any=768 ,   _UpperCAmelCase       :		Optional[Any]=12 ,   _UpperCAmelCase       :		Union[str, Any]=12 ,   _UpperCAmelCase       :		List[Any]=3072 ,   _UpperCAmelCase       :		List[Any]="gelu" ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-1_2 ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		int="absolute" ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]="none" ,   **_UpperCAmelCase       :		List[Any] ,   )		->  Any:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   bos_token_id=_UpperCAmelCase ,   eos_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						quant_mode
							lowercase__	     =						force_dequant






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]						)

