



"""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()
lowercase__			   =  logging.get_logger(__name__)





def       __magic_name__    (   _lowerCamelCase				: Dict					):
       __a		:		List[str]									=       MobileViTConfig()

       # size of the architecture
       if "mobilevit_s" in mobilevit_name:
              __a		:		Union[str, Any]									=       [1_4_4, 1_9_2, 2_4_0]
              __a		:		int									=       [1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
       elif "mobilevit_xs" in mobilevit_name:
              __a		:		List[str]									=       [9_6, 1_2_0, 1_4_4]
              __a		:		Any									=       [1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
       elif "mobilevit_xxs" in mobilevit_name:
              __a		:		Any									=       [6_4, 8_0, 9_6]
              __a		:		List[str]									=       [1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
              __a		:		Any									=       0.05
              __a		:		List[Any]									=       2.0

       if mobilevit_name.startswith("""deeplabv3_"""					):
              __a		:		int									=       5_1_2
              __a		:		Optional[int]									=       1_6
              __a		:		List[Any]									=       2_1
              __a		:		List[str]									=       """pascal-voc-id2label.json"""
       else:
              __a		:		str									=       1_0_0_0
              __a		:		Any									=       """imagenet-1k-id2label.json"""

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

       return config





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: int=False					):
       for i in range(1  ,       6					):
              if F'''layer_{i}.''' in name:
                     __a		:		Tuple									=       name.replace(F'''layer_{i}.'''  ,       F'''encoder.layer.{i - 1}.'''					)

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

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

       for i in range(2  ,       5					):
              if F'''.global_rep.{i}.weight''' in name:
                     __a		:		List[Any]									=       name.replace(F'''.global_rep.{i}.weight'''  ,       """.layernorm.weight"""					)
              if F'''.global_rep.{i}.bias''' in name:
                     __a		:		Optional[int]									=       name.replace(F'''.global_rep.{i}.bias'''  ,       """.layernorm.bias"""					)

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

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

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

       return name





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[int]=False					):
       if base_model:
              __a		:		Dict									=       """"""
       else:
              __a		:		Any									=       """mobilevit."""

       for key in orig_state_dict.copy().keys():
              __a		:		List[Any]									=       orig_state_dict.pop(_lowerCamelCase					)

              if key[:8] == "encoder.":
                     __a		:		int									=       key[8:]

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

       return orig_state_dict





def       __magic_name__    (   ):
       __a		:		List[Any]									=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
       __a		:		Dict									=       Image.open(requests.get(_lowerCamelCase  ,       stream=_lowerCamelCase					).raw					)
       return im





@torch.no_grad()
def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: int=False					):
       __a		:		Optional[Any]									=       get_mobilevit_config(_lowerCamelCase					)

       # load original state_dict
       __a		:		List[Any]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)

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

       __a		:		str									=       convert_state_dict(_lowerCamelCase  ,       _lowerCamelCase					)
       model.load_state_dict(_lowerCamelCase					)

       # Check outputs on an image, prepared by MobileViTImageProcessor
       __a		:		Optional[Any]									=       MobileViTImageProcessor(crop_size=config.image_size  ,       size=config.image_size + 3_2					)
       __a		:		Any									=       image_processor(images=prepare_img()  ,       return_tensors="""pt"""					)
       __a		:		List[Any]									=       model(**_lowerCamelCase					)
       __a		:		Dict									=       outputs.logits

       if mobilevit_name.startswith("""deeplabv3_"""					):
              assert logits.shape == (1, 2_1, 3_2, 3_2)

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     __a		:		int									=       torch.tensor(
                         [
                             [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                             [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                             [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                         ]					)
              elif mobilevit_name == "deeplabv3_mobilevit_xs":
                     __a		:		Tuple									=       torch.tensor(
                         [
                             [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                             [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                             [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                         ]					)
              elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                     __a		:		Tuple									=       torch.tensor(
                         [
                             [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                             [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                             [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                         ]					)
              else:
                     raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''					)

              assert torch.allclose(logits[0, :3, :3, :3]  ,       _lowerCamelCase  ,       atol=1E-4					)
       else:
              assert logits.shape == (1, 1_0_0_0)

              if mobilevit_name == "mobilevit_s":
                     __a		:		Tuple									=       torch.tensor([-0.98_66, 0.23_92, -1.12_41]					)
              elif mobilevit_name == "mobilevit_xs":
                     __a		:		Any									=       torch.tensor([-2.47_61, -0.93_99, -1.95_87]					)
              elif mobilevit_name == "mobilevit_xxs":
                     __a		:		Union[str, Any]									=       torch.tensor([-1.93_64, -1.23_27, -0.46_53]					)
              else:
                     raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''					)

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

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

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


if __name__ == "__main__":
 lowercase__			   =  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__			   =  parser.parse_args()
 convert_movilevit_checkpoint(
     args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )






"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


lowercase__			   =  {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ErnieForCausalLM",
	    "ErnieForMaskedLM",
	    "ErnieForMultipleChoice",
	    "ErnieForNextSentencePrediction",
	    "ErnieForPreTraining",
	    "ErnieForQuestionAnswering",
	    "ErnieForSequenceClassification",
	    "ErnieForTokenClassification",
	    "ErnieModel",
	    "ErniePreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

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

