

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       snake_case_ (__A	:		Union[str, Any]	)     ->      int:
   __lowerCAmelCase    :     Tuple          =		MobileViTConfig()

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

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

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

   return config





def       snake_case_ (__A	:		Union[str, Any]   ,  __A	:		str=False	)     ->      Optional[Any]:
   for i in range(1   ,  6	):
      if f'''layer_{i}.''' in name:
         __lowerCAmelCase    :     Dict          =		name.replace(f'''layer_{i}.'''   ,  f'''encoder.layer.{i - 1}.'''	)

   if "conv_1." in name:
      __lowerCAmelCase    :     List[Any]          =		name.replace("""conv_1."""   ,  """conv_stem."""	)
   if ".block." in name:
      __lowerCAmelCase    :     int          =		name.replace(""".block."""   ,  """."""	)

   if "exp_1x1" in name:
      __lowerCAmelCase    :     str          =		name.replace("""exp_1x1"""   ,  """expand_1x1"""	)
   if "red_1x1" in name:
      __lowerCAmelCase    :     Dict          =		name.replace("""red_1x1"""   ,  """reduce_1x1"""	)
   if ".local_rep.conv_3x3." in name:
      __lowerCAmelCase    :     List[str]          =		name.replace(""".local_rep.conv_3x3."""   ,  """.conv_kxk."""	)
   if ".local_rep.conv_1x1." in name:
      __lowerCAmelCase    :     Optional[Any]          =		name.replace(""".local_rep.conv_1x1."""   ,  """.conv_1x1."""	)
   if ".norm." in name:
      __lowerCAmelCase    :     Tuple          =		name.replace(""".norm."""   ,  """.normalization."""	)
   if ".conv." in name:
      __lowerCAmelCase    :     Any          =		name.replace(""".conv."""   ,  """.convolution."""	)
   if ".conv_proj." in name:
      __lowerCAmelCase    :     Optional[Any]          =		name.replace(""".conv_proj."""   ,  """.conv_projection."""	)

   for i in range(0   ,  2	):
      for j in range(0   ,  4	):
         if f'''.{i}.{j}.''' in name:
            __lowerCAmelCase    :     int          =		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:
            __lowerCAmelCase    :     Dict          =		name.replace(f'''.{i}.{j}.'''   ,  f'''.{i}.'''	)
            if "expand_1x1" in name:
               __lowerCAmelCase    :     Optional[int]          =		name.replace("""expand_1x1"""   ,  """downsampling_layer.expand_1x1"""	)
            if "conv_3x3" in name:
               __lowerCAmelCase    :     Optional[int]          =		name.replace("""conv_3x3"""   ,  """downsampling_layer.conv_3x3"""	)
            if "reduce_1x1" in name:
               __lowerCAmelCase    :     Any          =		name.replace("""reduce_1x1"""   ,  """downsampling_layer.reduce_1x1"""	)

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

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

   if ".aspp_layer." in name:
      __lowerCAmelCase    :     List[Any]          =		name.replace(""".aspp_layer."""   ,  """."""	)
   if ".aspp_pool." in name:
      __lowerCAmelCase    :     List[Any]          =		name.replace(""".aspp_pool."""   ,  """."""	)
   if "seg_head." in name:
      __lowerCAmelCase    :     Tuple          =		name.replace("""seg_head."""   ,  """segmentation_head."""	)
   if "segmentation_head.classifier.classifier." in name:
      __lowerCAmelCase    :     Dict          =		name.replace("""segmentation_head.classifier.classifier."""   ,  """segmentation_head.classifier."""	)

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

   return name





def       snake_case_ (__A	:		Union[str, Any]   ,  __A	:		int   ,  __A	:		int=False	)     ->      str:
   if base_model:
      __lowerCAmelCase    :     Any          =		""""""
   else:
      __lowerCAmelCase    :     Tuple          =		"""mobilevit."""

   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase    :     Dict          =		orig_state_dict.pop(__A	)

      if key[:8] == "encoder.":
         __lowerCAmelCase    :     Optional[int]          =		key[8:]

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

   return orig_state_dict





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





@torch.no_grad()
def       snake_case_ (__A	:		Dict   ,  __A	:		Optional[Any]   ,  __A	:		Tuple   ,  __A	:		int=False	)     ->      Dict:
   __lowerCAmelCase    :     Optional[int]          =		get_mobilevit_config(__A	)

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

   # load 🤗 model
   if mobilevit_name.startswith("""deeplabv3_"""	):
      __lowerCAmelCase    :     Union[str, Any]          =		MobileViTForSemanticSegmentation(__A	).eval()
   else:
      __lowerCAmelCase    :     Tuple          =		MobileViTForImageClassification(__A	).eval()

   __lowerCAmelCase    :     Dict          =		convert_state_dict(__A   ,  __A	)
   model.load_state_dict(__A	)

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

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

      if mobilevit_name == "deeplabv3_mobilevit_s":
         __lowerCAmelCase    :     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":
         __lowerCAmelCase    :     Dict          =		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":
         __lowerCAmelCase    :     Dict          =		torch.tensor(
             [
                 [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
                 [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8624, -9.5964], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                 [[-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_0_0_0)

      if mobilevit_name == "mobilevit_s":
         __lowerCAmelCase    :     List[str]          =		torch.tensor([-0.9866, 0.2392, -1.1241]	)
      elif mobilevit_name == "mobilevit_xs":
         __lowerCAmelCase    :     Dict          =		torch.tensor([-2.4761, -0.9399, -1.9587]	)
      elif mobilevit_name == "mobilevit_xxs":
         __lowerCAmelCase    :     Optional[Any]          =		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:
      __lowerCAmelCase    :     List[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..."""	)
      __lowerCAmelCase    :     str          =		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
   )








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




