


'''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()
__magic_name__			=      logging.get_logger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      Dict):
      A_						:  List[str]          =				MobileViTConfig()

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

      if mobilevit_name.startswith("""deeplabv3_"""):
            A_						:  int          =				512
            A_						:  Optional[int]          =				16
            A_						:  List[Any]          =				21
            A_						:  List[str]          =				"""pascal-voc-id2label.json"""
      else:
            A_						:  str          =				1000
            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      lowerCamelCase					(      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      lowerCamelCase					(      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      lowerCamelCase					(      ):
      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      lowerCamelCase					(      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 + 32)
      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, 21, 32, 32)

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  A_						:  int          =				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":
                  A_						:  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":
                  A_						:  Tuple          =				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]							,						lowerCamelCase							,						atol=1E-4)
      else:
            assert logits.shape == (1, 1000)

            if mobilevit_name == "mobilevit_s":
                  A_						:  Tuple          =				torch.tensor([-0.9866, 0.2392, -1.1241])
            elif mobilevit_name == "mobilevit_xs":
                  A_						:  Any          =				torch.tensor([-2.4761, -0.9399, -1.9587])
            elif mobilevit_name == "mobilevit_xxs":
                  A_						:  Union[str, Any]          =				torch.tensor([-1.9364, -1.2327, -0.4653])
            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__":
     __magic_name__			=      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.'
     )

     __magic_name__			=      parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )




'''simple docstring'''


from __future__ import annotations




def      lowerCamelCase					(      lowerCamelCase  :      dict							,						lowerCamelCase  :      str):
      A_   ,					A_						:  List[Any]          =				set(lowerCamelCase), [start]

      while stack:
            A_						:  Optional[Any]          =				stack.pop()
            explored.add(lowerCamelCase)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]):
                  if adj not in explored:
                        stack.append(lowerCamelCase)
      return explored


__magic_name__			=      {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, 'A'))
