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()
a_		       =   logging.get_logger(__name__)




def    __lowerCAmelCase   (    A_ :			List[str]       )     ->	Optional[Any]:
  __UpperCAmelCase         =							MobileViTConfig()

  # size of the architecture
  if "mobilevit_s" in mobilevit_name:
    __UpperCAmelCase         =							[1_44, 1_92, 2_40]
    __UpperCAmelCase         =							[16, 32, 64, 96, 1_28, 1_60, 6_40]
  elif "mobilevit_xs" in mobilevit_name:
    __UpperCAmelCase         =							[96, 1_20, 1_44]
    __UpperCAmelCase         =							[16, 32, 48, 64, 80, 96, 3_84]
  elif "mobilevit_xxs" in mobilevit_name:
    __UpperCAmelCase         =							[64, 80, 96]
    __UpperCAmelCase         =							[16, 16, 24, 48, 64, 80, 3_20]
    __UpperCAmelCase         =							0.05
    __UpperCAmelCase         =							2.0

  if mobilevit_name.startswith("deeplabv3_"       ):
    __UpperCAmelCase         =							5_12
    __UpperCAmelCase         =							16
    __UpperCAmelCase         =							21
    __UpperCAmelCase         =							"pascal-voc-id2label.json"
  else:
    __UpperCAmelCase         =							10_00
    __UpperCAmelCase         =							"imagenet-1k-id2label.json"

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

  return config




def    __lowerCAmelCase   (    A_ :			Optional[int]    ,						A_ :			Dict=False       )     ->	Dict:


  for i in range(1    ,						6       ):
    if F'''layer_{i}.''' in name:
      __UpperCAmelCase         =							name.replace(F'''layer_{i}.'''    ,						F'''encoder.layer.{i - 1}.'''       )

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

  if "exp_1x1" in name:
    __UpperCAmelCase         =							name.replace("exp_1x1"    ,						"expand_1x1"       )
  if "red_1x1" in name:
    __UpperCAmelCase         =							name.replace("red_1x1"    ,						"reduce_1x1"       )
  if ".local_rep.conv_3x3." in name:
    __UpperCAmelCase         =							name.replace(".local_rep.conv_3x3."    ,						".conv_kxk."       )
  if ".local_rep.conv_1x1." in name:
    __UpperCAmelCase         =							name.replace(".local_rep.conv_1x1."    ,						".conv_1x1."       )
  if ".norm." in name:
    __UpperCAmelCase         =							name.replace(".norm."    ,						".normalization."       )
  if ".conv." in name:
    __UpperCAmelCase         =							name.replace(".conv."    ,						".convolution."       )
  if ".conv_proj." in name:
    __UpperCAmelCase         =							name.replace(".conv_proj."    ,						".conv_projection."       )

  for i in range(0    ,						2       ):
    for j in range(0    ,						4       ):
      if F'''.{i}.{j}.''' in name:
        __UpperCAmelCase         =							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:
        __UpperCAmelCase         =							name.replace(F'''.{i}.{j}.'''    ,						F'''.{i}.'''       )
        if "expand_1x1" in name:
          __UpperCAmelCase         =							name.replace("expand_1x1"    ,						"downsampling_layer.expand_1x1"       )
        if "conv_3x3" in name:
          __UpperCAmelCase         =							name.replace("conv_3x3"    ,						"downsampling_layer.conv_3x3"       )
        if "reduce_1x1" in name:
          __UpperCAmelCase         =							name.replace("reduce_1x1"    ,						"downsampling_layer.reduce_1x1"       )

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

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

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

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

  return name




def    __lowerCAmelCase   (    A_ :			Any    ,						A_ :			Union[str, Any]    ,						A_ :			Optional[Any]=False       )     ->	Tuple:


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

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

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

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

  return orig_state_dict




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




@torch.no_grad()
def    __lowerCAmelCase   (    A_ :			str    ,						A_ :			int    ,						A_ :			int    ,						A_ :			str=False       )     ->	Union[str, Any]:
  __UpperCAmelCase         =							get_mobilevit_config(_A       )

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

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

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

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

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

    if mobilevit_name == "deeplabv3_mobilevit_s":
      __UpperCAmelCase         =							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":
      __UpperCAmelCase         =							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":
      __UpperCAmelCase         =							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]    ,						_A    ,						atol=1e-4       )
  else:
    assert logits.shape == (1, 10_00)

    if mobilevit_name == "mobilevit_s":
      __UpperCAmelCase         =							torch.tensor([-0.98_66, 0.23_92, -1.12_41]       )
    elif mobilevit_name == "mobilevit_xs":
      __UpperCAmelCase         =							torch.tensor([-2.47_61, -0.93_99, -1.95_87]       )
    elif mobilevit_name == "mobilevit_xxs":
      __UpperCAmelCase         =							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]    ,						_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:
    __UpperCAmelCase         =							{
        "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..."       )
    __UpperCAmelCase         =							model_mapping[mobilevit_name]
    image_processor.push_to_hub(_A    ,						organization="apple"       )
    model.push_to_hub(_A    ,						organization="apple"       )


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

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




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE			:						List[Any]				  =		get_logger(__name__)
class  A_			(							enum.Enum			):
  _SCREAMING_SNAKE_CASE     					=      """all_checks"""
  _SCREAMING_SNAKE_CASE     					=      """basic_checks"""
  _SCREAMING_SNAKE_CASE     					=      """no_checks"""
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A				,					_A=None       ):



 """simple docstring"""


 if expected_checksums is None:
  logger.info("Unable to verify checksums."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreDownloadedFiles(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedDownloadedFile(str(set(_A       ) - set(_A       )       )       )
 __a							=	[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
 __a							=	" for " + verification_name if verification_name is not None else ""
 if len(_A       ) > 0:
  raise NonMatchingChecksumError(
      f"""Checksums didn't match{for_verification_name}:\n"""
      f"""{bad_urls}\n"""
      "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"       )
 logger.info("All the checksums matched successfully" + for_verification_name       )
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass
class  A_			(							a_			):
  pass



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 if expected_splits is None:
  logger.info("Unable to verify splits sizes."       )
  return
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise ExpectedMoreSplits(str(set(_A       ) - set(_A       )       )       )
 if len(set(_A       ) - set(_A       )       ) > 0:
  raise UnexpectedSplits(str(set(_A       ) - set(_A       )       )       )
 __a							=	[
     {"expected": expected_splits[name], "recorded": recorded_splits[name]}
     for name in expected_splits
     if expected_splits[name].num_examples != recorded_splits[name].num_examples
 ]
 if len(_A       ) > 0:
  raise NonMatchingSplitsSizesError(str(_A       )       )
 logger.info("All the splits matched successfully."       )



def 	__A		(      _A				,					_A = True       ):



 """simple docstring"""


 if record_checksum:
  __a							=	shaaaa()
  with open(_A				,					"rb"       ) as f:
   for chunk in iter(lambda: f.read(1 << 20       )				,					B""       ):
    m.update(_A       )
   __a							=	m.hexdigest()
 else:
  __a							=	None
 return {"num_bytes": os.path.getsize(_A       ), "checksum": checksum}






def 	__A		(      _A       ):



 """simple docstring"""


 if dataset_size and config.IN_MEMORY_MAX_SIZE:
  return dataset_size < config.IN_MEMORY_MAX_SIZE
 else:
  return False


