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()
SCREAMING_SNAKE_CASE			:						Dict				  =		logging.get_logger(__name__)



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	MobileViTConfig()

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

 if mobilevit_name.startswith("deeplabv3_"       ):
  __a							=	512
  __a							=	16
  __a							=	21
  __a							=	"pascal-voc-id2label.json"
 else:
  __a							=	1000
  __a							=	"imagenet-1k-id2label.json"

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

 return config



def 	__A		(      _A				,					_A=False       ):



 """simple docstring"""


 for i in range(1				,					6       ):
  if f"""layer_{i}.""" in name:
   __a							=	name.replace(f"""layer_{i}."""				,					f"""encoder.layer.{i - 1}."""       )

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

 if "exp_1x1" in name:
  __a							=	name.replace("exp_1x1"				,					"expand_1x1"       )
 if "red_1x1" in name:
  __a							=	name.replace("red_1x1"				,					"reduce_1x1"       )
 if ".local_rep.conv_3x3." in name:
  __a							=	name.replace(".local_rep.conv_3x3."				,					".conv_kxk."       )
 if ".local_rep.conv_1x1." in name:
  __a							=	name.replace(".local_rep.conv_1x1."				,					".conv_1x1."       )
 if ".norm." in name:
  __a							=	name.replace(".norm."				,					".normalization."       )
 if ".conv." in name:
  __a							=	name.replace(".conv."				,					".convolution."       )
 if ".conv_proj." in name:
  __a							=	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							=	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							=	name.replace(f""".{i}.{j}."""				,					f""".{i}."""       )
    if "expand_1x1" in name:
     __a							=	name.replace("expand_1x1"				,					"downsampling_layer.expand_1x1"       )
    if "conv_3x3" in name:
     __a							=	name.replace("conv_3x3"				,					"downsampling_layer.conv_3x3"       )
    if "reduce_1x1" in name:
     __a							=	name.replace("reduce_1x1"				,					"downsampling_layer.reduce_1x1"       )

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

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

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

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

 return name



def 	__A		(      _A				,					_A				,					_A=False       ):



 """simple docstring"""


 if base_model:
  __a							=	""
 else:
  __a							=	"mobilevit."

 for key in orig_state_dict.copy().keys():
  __a							=	orig_state_dict.pop(_A       )

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

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

 return orig_state_dict



def 	__A		(      ):



 """simple docstring"""


 __a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
 __a							=	Image.open(requests.get(_A				,					stream=_A       ).raw       )
 return im



@torch.no_grad()
def 	__A		(      _A				,					_A				,					_A				,					_A=False       ):



 """simple docstring"""


 __a							=	get_mobilevit_config(_A       )

 # load original state_dict
 __a							=	torch.load(_A				,					map_location="cpu"       )

 # load 🤗 model
 if mobilevit_name.startswith("deeplabv3_"       ):
  __a							=	MobileViTForSemanticSegmentation(_A       ).eval()
 else:
  __a							=	MobileViTForImageClassification(_A       ).eval()

 __a							=	convert_state_dict(_A				,					_A       )
 model.load_state_dict(_A       )

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

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

  if mobilevit_name == "deeplabv3_mobilevit_s":
   __a							=	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							=	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							=	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, 1000)

  if mobilevit_name == "mobilevit_s":
   __a							=	torch.tensor([-0.9866, 0.2392, -1.1241]       )
  elif mobilevit_name == "mobilevit_xs":
   __a							=	torch.tensor([-2.4761, -0.9399, -1.9587]       )
  elif mobilevit_name == "mobilevit_xxs":
   __a							=	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:
  __a							=	{
      "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							=	model_mapping[mobilevit_name]
  image_processor.push_to_hub(_A				,					organization="apple"       )
  model.push_to_hub(_A				,					organization="apple"       )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE			:						int				  =		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."""
  )

  SCREAMING_SNAKE_CASE			:						Tuple				  =		parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )



import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType

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


SCREAMING_SNAKE_CASE			:						Dict				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Tuple				  =		{
    """google/owlvit-base-patch32""": """https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json""",
    """google/owlvit-base-patch16""": """https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json""",
    """google/owlvit-large-patch14""": """https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json""",
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_text_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=4_94_08  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=5_12  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=20_48  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=12  ,				__SCREAMING_SNAKE_CASE			:   Tuple=8  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   Tuple="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Any=0.0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.02  ,				__SCREAMING_SNAKE_CASE			:   int=1.0  ,				__SCREAMING_SNAKE_CASE			:   int=0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_06  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4_94_07  ,				**__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	max_position_embeddings
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	attention_dropout
   __a							=	initializer_range
   __a							=	initializer_factor

  @classmethod
  def 		_UpperCAmelCase (		cls			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the text config dict if we are loading from OwlViTConfig
   if config_dict.get("model_type"							) == "owlvit":
    __a							=	config_dict["text_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit_vision_model"""

  def __init__(		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=30_72  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=12  ,				__SCREAMING_SNAKE_CASE			:   str=3  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=7_68  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=32  ,				__SCREAMING_SNAKE_CASE			:   Dict="quick_gelu"  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   int=0.0  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Dict=1.0  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	hidden_size
   __a							=	intermediate_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	num_channels
   __a							=	image_size
   __a							=	patch_size
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	attention_dropout
   __a							=	initializer_range
   __a							=	initializer_factor

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   Tuple							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # get the vision config dict if we are loading from OwlViTConfig
   if config_dict.get("model_type"							) == "owlvit":
    __a							=	config_dict["vision_config"]

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """owlvit"""
  _SCREAMING_SNAKE_CASE     					=      True

  def __init__(		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   Tuple=None  ,				__SCREAMING_SNAKE_CASE			:   str=5_12  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=2.65_92  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				**__SCREAMING_SNAKE_CASE			:   List[Any]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   if text_config is None:
    __a							=	{}
    logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."							)

   if vision_config is None:
    __a							=	{}
    logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."							)

   __a							=	OwlViTTextConfig(**__SCREAMING_SNAKE_CASE							)
   __a							=	OwlViTVisionConfig(**__SCREAMING_SNAKE_CASE							)

   __a							=	projection_dim
   __a							=	logit_scale_init_value
   __a							=	return_dict
   __a							=	1.0

  @classmethod
  def 		_UpperCAmelCase (		cls			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, os.PathLike]  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE							)

   __a							,  __a							=	cls.get_config_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if "model_type" in config_dict and hasattr(cls  ,				"model_type"							) and config_dict["model_type"] != cls.model_type:
    logger.warning(
        f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
        f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  @classmethod
  def 		_UpperCAmelCase (		cls			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	{}
   __a							=	text_config
   __a							=	vision_config

   return cls.from_dict(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	copy.deepcopy(self.__dict__							)
   __a							=	self.text_config.to_dict()
   __a							=	self.vision_config.to_dict()
   __a							=	self.__class__.model_type
   return output
class  A_			(							a_			):

  @property
  def 		_UpperCAmelCase (		self			:   Any							):
   return OrderedDict(
       [
           ("input_ids", {0: "batch", 1: "sequence"}),
           ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ("attention_mask", {0: "batch", 1: "sequence"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return OrderedDict(
       [
           ("logits_per_image", {0: "batch"}),
           ("logits_per_text", {0: "batch"}),
           ("text_embeds", {0: "batch"}),
           ("image_embeds", {0: "batch"}),
       ]							)

  @property
  def 		_UpperCAmelCase (		self			:   str							):
   return 1E-4

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   "ProcessorMixin"  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   int = -1  ,				__SCREAMING_SNAKE_CASE			:   Optional["TensorType"] = None  ,				):
   __a							=	super().generate_dummy_inputs(
       processor.tokenizer  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				seq_length=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   __a							=	super().generate_dummy_inputs(
       processor.image_processor  ,				batch_size=__SCREAMING_SNAKE_CASE  ,				framework=__SCREAMING_SNAKE_CASE							)
   return {**text_input_dict, **image_input_dict}

  @property
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   return 14


