



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 							A     (					_UpperCAmelCase    :       str   )    ->			str:







			'''simple docstring'''
			_UpperCAmelCase			=       MobileViTConfig()

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

			if mobilevit_name.startswith('deeplabv3_'   ):
						_UpperCAmelCase			=       512
						_UpperCAmelCase			=       16
						_UpperCAmelCase			=       21
						_UpperCAmelCase			=       "pascal-voc-id2label.json"
			else:
						_UpperCAmelCase			=       1_000
						_UpperCAmelCase			=       "imagenet-1k-id2label.json"

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

			return config





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Union[str, Any]=False   )    ->			List[str]:







			'''simple docstring'''
			for i in range(1							,       6   ):
						if F"layer_{i}." in name:
									_UpperCAmelCase			=       name.replace(F"layer_{i}."							,       F"encoder.layer.{i - 1}."   )

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

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

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

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

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

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

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

			return name





def 							A     (					_UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]							,       _UpperCAmelCase    :       int=False   )    ->			Tuple:







			'''simple docstring'''
			if base_model:
						_UpperCAmelCase			=       ""
			else:
						_UpperCAmelCase			=       "mobilevit."

			for key in orig_state_dict.copy().keys():
						_UpperCAmelCase			=       orig_state_dict.pop(a_   )

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

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

			return orig_state_dict





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







			'''simple docstring'''
			_UpperCAmelCase			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
			_UpperCAmelCase			=       Image.open(requests.get(a_							,       stream=a_   ).raw   )
			return im





@torch.no_grad()
def 							A     (					_UpperCAmelCase    :       Optional[Any]							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[str]=False   )    ->			Optional[int]:







			'''simple docstring'''
			_UpperCAmelCase			=       get_mobilevit_config(a_   )

			# load original state_dict
			_UpperCAmelCase			=       torch.load(a_							,       map_location='cpu'   )

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

			_UpperCAmelCase			=       convert_state_dict(a_							,       a_   )
			model.load_state_dict(a_   )

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

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

						if mobilevit_name == "deeplabv3_mobilevit_s":
									_UpperCAmelCase			=       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":
									_UpperCAmelCase			=       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":
									_UpperCAmelCase			=       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]							,       a_							,       atol=1E-4   )
			else:
						assert logits.shape == (1, 1_000)

						if mobilevit_name == "mobilevit_s":
									_UpperCAmelCase			=       torch.tensor([-0.9866, 0.2392, -1.1241]   )
						elif mobilevit_name == "mobilevit_xs":
									_UpperCAmelCase			=       torch.tensor([-2.4761, -0.9399, -1.9587]   )
						elif mobilevit_name == "mobilevit_xxs":
									_UpperCAmelCase			=       torch.tensor([-1.9364, -1.2327, -0.4653]   )
						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:
						_UpperCAmelCase			=       {
						    "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...'   )
						_UpperCAmelCase			=       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
							)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__       =	{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPTokenizerFast"]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["CLIPFeatureExtractor"]
							UpperCAmelCase__       =	["CLIPImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CLIPModel",
							    "CLIPPreTrainedModel",
							    "CLIPTextModel",
							    "CLIPTextModelWithProjection",
							    "CLIPVisionModel",
							    "CLIPVisionModelWithProjection",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCLIPModel",
							    "TFCLIPPreTrainedModel",
							    "TFCLIPTextModel",
							    "TFCLIPVisionModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "FlaxCLIPModel",
							    "FlaxCLIPPreTrainedModel",
							    "FlaxCLIPTextModel",
							    "FlaxCLIPTextPreTrainedModel",
							    "FlaxCLIPVisionModel",
							    "FlaxCLIPVisionPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
