
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()
lowercase  : Union[str, Any]						   =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
							lowercase							:					Optional[Any]	     =				MobileViTConfig()

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

							if mobilevit_name.startswith("""deeplabv3_"""    ):
														lowercase							:					Optional[Any]	     =				512
														lowercase							:					Union[str, Any]	     =				16
														lowercase							:					List[str]	     =				21
														lowercase							:					Union[str, Any]	     =				"""pascal-voc-id2label.json"""
							else:
														lowercase							:					Union[str, Any]	     =				1_000
														lowercase							:					Optional[int]	     =				"""imagenet-1k-id2label.json"""

							lowercase							:					Tuple	     =				"""huggingface/label-files"""
							lowercase							:					Dict	     =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     repo_type="""dataset"""    ) ,     """r"""    )    )
							lowercase							:					List[Any]	     =				{int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
							lowercase							:					Tuple	     =				idalabel
							lowercase							:					List[str]	     =				{v: k for k, v in idalabel.items()}

							return config







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   List[Any]:
							for i in range(1 ,     6    ):
														if f"layer_{i}." in name:
																					lowercase							:					Optional[Any]	     =				name.replace(f"layer_{i}." ,     f"encoder.layer.{i - 1}."    )

							if "conv_1." in name:
														lowercase							:					str	     =				name.replace("""conv_1.""" ,     """conv_stem."""    )
							if ".block." in name:
														lowercase							:					Optional[Any]	     =				name.replace(""".block.""" ,     """."""    )

							if "exp_1x1" in name:
														lowercase							:					Union[str, Any]	     =				name.replace("""exp_1x1""" ,     """expand_1x1"""    )
							if "red_1x1" in name:
														lowercase							:					int	     =				name.replace("""red_1x1""" ,     """reduce_1x1"""    )
							if ".local_rep.conv_3x3." in name:
														lowercase							:					Tuple	     =				name.replace(""".local_rep.conv_3x3.""" ,     """.conv_kxk."""    )
							if ".local_rep.conv_1x1." in name:
														lowercase							:					str	     =				name.replace(""".local_rep.conv_1x1.""" ,     """.conv_1x1."""    )
							if ".norm." in name:
														lowercase							:					Any	     =				name.replace(""".norm.""" ,     """.normalization."""    )
							if ".conv." in name:
														lowercase							:					Dict	     =				name.replace(""".conv.""" ,     """.convolution."""    )
							if ".conv_proj." in name:
														lowercase							:					List[str]	     =				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							:					Optional[Any]	     =				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							:					str	     =				name.replace(f".{i}.{j}." ,     f".{i}."    )
																												if "expand_1x1" in name:
																																			lowercase							:					List[Any]	     =				name.replace("""expand_1x1""" ,     """downsampling_layer.expand_1x1"""    )
																												if "conv_3x3" in name:
																																			lowercase							:					Union[str, Any]	     =				name.replace("""conv_3x3""" ,     """downsampling_layer.conv_3x3"""    )
																												if "reduce_1x1" in name:
																																			lowercase							:					Tuple	     =				name.replace("""reduce_1x1""" ,     """downsampling_layer.reduce_1x1"""    )

							for i in range(2 ,     5    ):
														if f".global_rep.{i}.weight" in name:
																					lowercase							:					Union[str, Any]	     =				name.replace(f".global_rep.{i}.weight" ,     """.layernorm.weight"""    )
														if f".global_rep.{i}.bias" in name:
																					lowercase							:					Union[str, Any]	     =				name.replace(f".global_rep.{i}.bias" ,     """.layernorm.bias"""    )

							if ".global_rep." in name:
														lowercase							:					List[Any]	     =				name.replace(""".global_rep.""" ,     """.transformer."""    )
							if ".pre_norm_mha.0." in name:
														lowercase							:					Dict	     =				name.replace(""".pre_norm_mha.0.""" ,     """.layernorm_before."""    )
							if ".pre_norm_mha.1.out_proj." in name:
														lowercase							:					Dict	     =				name.replace(""".pre_norm_mha.1.out_proj.""" ,     """.attention.output.dense."""    )
							if ".pre_norm_ffn.0." in name:
														lowercase							:					List[Any]	     =				name.replace(""".pre_norm_ffn.0.""" ,     """.layernorm_after."""    )
							if ".pre_norm_ffn.1." in name:
														lowercase							:					Optional[int]	     =				name.replace(""".pre_norm_ffn.1.""" ,     """.intermediate.dense."""    )
							if ".pre_norm_ffn.4." in name:
														lowercase							:					Union[str, Any]	     =				name.replace(""".pre_norm_ffn.4.""" ,     """.output.dense."""    )
							if ".transformer." in name:
														lowercase							:					Tuple	     =				name.replace(""".transformer.""" ,     """.transformer.layer."""    )

							if ".aspp_layer." in name:
														lowercase							:					List[str]	     =				name.replace(""".aspp_layer.""" ,     """."""    )
							if ".aspp_pool." in name:
														lowercase							:					int	     =				name.replace(""".aspp_pool.""" ,     """."""    )
							if "seg_head." in name:
														lowercase							:					str	     =				name.replace("""seg_head.""" ,     """segmentation_head."""    )
							if "segmentation_head.classifier.classifier." in name:
														lowercase							:					Optional[int]	     =				name.replace("""segmentation_head.classifier.classifier.""" ,     """segmentation_head.classifier."""    )

							if "classifier.fc." in name:
														lowercase							:					Optional[int]	     =				name.replace("""classifier.fc.""" ,     """classifier."""    )
							elif (not base_model) and ("segmentation_head." not in name):
														lowercase							:					Dict	     =				"""mobilevit.""" + name

							return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   int:
							if base_model:
														lowercase							:					Union[str, Any]	     =				""""""
							else:
														lowercase							:					Optional[Any]	     =				"""mobilevit."""

							for key in orig_state_dict.copy().keys():
														lowercase							:					Optional[int]	     =				orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

														if key[:8] == "encoder.":
																					lowercase							:					Union[str, Any]	     =				key[8:]

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

							return orig_state_dict







def 						_snake_case(  )			->   Union[str, Any]:
							lowercase							:					List[str]	     =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowercase							:					Tuple	     =				Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
							return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=False    )			->   Any:
							lowercase							:					str	     =				get_mobilevit_config(SCREAMING_SNAKE_CASE__    )

							# load original state_dict
							lowercase							:					List[str]	     =				torch.load(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )

							# load 🤗 model
							if mobilevit_name.startswith("""deeplabv3_"""    ):
														lowercase							:					Dict	     =				MobileViTForSemanticSegmentation(SCREAMING_SNAKE_CASE__    ).eval()
							else:
														lowercase							:					List[str]	     =				MobileViTForImageClassification(SCREAMING_SNAKE_CASE__    ).eval()

							lowercase							:					Tuple	     =				convert_state_dict(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
							model.load_state_dict(SCREAMING_SNAKE_CASE__    )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							lowercase							:					int	     =				MobileViTImageProcessor(crop_size=config.image_size ,     size=config.image_size + 32    )
							lowercase							:					List[str]	     =				image_processor(images=prepare_img() ,     return_tensors="""pt"""    )
							lowercase							:					List[Any]	     =				model(**SCREAMING_SNAKE_CASE__    )
							lowercase							:					Optional[int]	     =				outputs.logits

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					lowercase							:					Any	     =				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":
																					lowercase							:					Tuple	     =				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":
																					lowercase							:					List[str]	     =				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] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    )
							else:
														assert logits.shape == (1, 1_000)

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

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

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

							if push_to_hub:
														lowercase							:					Union[str, Any]	     =				{
														    """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							:					Dict	     =				model_mapping[mobilevit_name]
														image_processor.push_to_hub(SCREAMING_SNAKE_CASE__ ,     organization="""apple"""    )
														model.push_to_hub(SCREAMING_SNAKE_CASE__ ,     organization="""apple"""    )


if __name__ == "__main__":
		lowercase  : 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."""
		)

		lowercase  : List[Any]						   =							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 packaging import version

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


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

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if depth_multiplier <= 0:
                  raise ValueError("""depth_multiplier must be greater than zero."""				)

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.task == "image-classification":
                  return OrderedDict([("""logits""", {0: """batch"""})]				)
           else:
                  return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
