



'''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()
A__    :   List[str]           =					logging.get_logger(__name__)





def   a_   (		_UpperCAmelCase    :      Dict				)    ->  Tuple:
      __snake_case :   str													=    MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            __snake_case :   List[Any]													=    [1_44, 1_92, 2_40]
            __snake_case :   int													=    [16, 32, 64, 96, 1_28, 1_60, 6_40]
      elif "mobilevit_xs" in mobilevit_name:
            __snake_case :   int													=    [96, 1_20, 1_44]
            __snake_case :   int													=    [16, 32, 48, 64, 80, 96, 3_84]
      elif "mobilevit_xxs" in mobilevit_name:
            __snake_case :   int													=    [64, 80, 96]
            __snake_case :   int													=    [16, 16, 24, 48, 64, 80, 3_20]
            __snake_case :   List[Any]													=    0.0_5
            __snake_case :   str													=    2.0

      if mobilevit_name.startswith('deeplabv3_'				):
            __snake_case :   List[Any]													=    5_12
            __snake_case :   Any													=    16
            __snake_case :   str													=    21
            __snake_case :   Optional[int]													=    'pascal-voc-id2label.json'
      else:
            __snake_case :   Union[str, Any]													=    10_00
            __snake_case :   Dict													=    'imagenet-1k-id2label.json'

      __snake_case :   Optional[int]													=    'huggingface/label-files'
      __snake_case :   Union[str, Any]													=    json.load(open(hf_hub_download(_UpperCAmelCase ,_UpperCAmelCase ,repo_type='dataset'				) ,'r'				)				)
      __snake_case :   List[Any]													=    {int(_UpperCAmelCase				): v for k, v in idalabel.items()}
      __snake_case :   Optional[int]													=    idalabel
      __snake_case :   Dict													=    {v: k for k, v in idalabel.items()}

      return config





def   a_   (		_UpperCAmelCase    :      int ,_UpperCAmelCase    :      str=False				)    ->  Any:
      for i in range(1 ,6				):
            if f'''layer_{i}.''' in name:
                  __snake_case :   Dict													=    name.replace(f'''layer_{i}.''' ,f'''encoder.layer.{i - 1}.'''				)

      if "conv_1." in name:
            __snake_case :   List[str]													=    name.replace('conv_1.' ,'conv_stem.'				)
      if ".block." in name:
            __snake_case :   Tuple													=    name.replace('.block.' ,'.'				)

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

      for i in range(2 ,5				):
            if f'''.global_rep.{i}.weight''' in name:
                  __snake_case :   List[Any]													=    name.replace(f'''.global_rep.{i}.weight''' ,'.layernorm.weight'				)
            if f'''.global_rep.{i}.bias''' in name:
                  __snake_case :   str													=    name.replace(f'''.global_rep.{i}.bias''' ,'.layernorm.bias'				)

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

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

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

      return name





def   a_   (		_UpperCAmelCase    :      Dict ,_UpperCAmelCase    :      Optional[Any] ,_UpperCAmelCase    :      List[Any]=False				)    ->  str:
      if base_model:
            __snake_case :   Any													=    ''
      else:
            __snake_case :   List[str]													=    'mobilevit.'

      for key in orig_state_dict.copy().keys():
            __snake_case :   Union[str, Any]													=    orig_state_dict.pop(_UpperCAmelCase				)

            if key[:8] == "encoder.":
                  __snake_case :   str													=    key[8:]

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

      return orig_state_dict





def   a_   (		)    ->  List[Any]:
      __snake_case :   Optional[Any]													=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      __snake_case :   Any													=    Image.open(requests.get(_UpperCAmelCase ,stream=_UpperCAmelCase				).raw				)
      return im





@torch.no_grad()
def   a_   (		_UpperCAmelCase    :      Union[str, Any] ,_UpperCAmelCase    :      List[Any] ,_UpperCAmelCase    :      str ,_UpperCAmelCase    :      Union[str, Any]=False				)    ->  Optional[int]:
      __snake_case :   Any													=    get_mobilevit_config(_UpperCAmelCase				)

      # load original state_dict
      __snake_case :   str													=    torch.load(_UpperCAmelCase ,map_location='cpu'				)

      # load 🤗 model
      if mobilevit_name.startswith('deeplabv3_'				):
            __snake_case :   Union[str, Any]													=    MobileViTForSemanticSegmentation(_UpperCAmelCase				).eval()
      else:
            __snake_case :   Union[str, Any]													=    MobileViTForImageClassification(_UpperCAmelCase				).eval()

      __snake_case :   int													=    convert_state_dict(_UpperCAmelCase ,_UpperCAmelCase				)
      model.load_state_dict(_UpperCAmelCase				)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      __snake_case :   Dict													=    MobileViTImageProcessor(crop_size=config.image_size ,size=config.image_size + 32				)
      __snake_case :   int													=    image_processor(images=prepare_img() ,return_tensors='pt'				)
      __snake_case :   Optional[Any]													=    model(**_UpperCAmelCase				)
      __snake_case :   Optional[Any]													=    outputs.logits

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

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  __snake_case :   Optional[Any]													=    torch.tensor(
                      [
                          [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                          [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                          [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                      ]				)
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  __snake_case :   Any													=    torch.tensor(
                      [
                          [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                          [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                          [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                      ]				)
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  __snake_case :   int													=    torch.tensor(
                      [
                          [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                          [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                          [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                      ]				)
            else:
                  raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''				)

            assert torch.allclose(logits[0, :3, :3, :3] ,_UpperCAmelCase ,atol=1E-4				)
      else:
            assert logits.shape == (1, 10_00)

            if mobilevit_name == "mobilevit_s":
                  __snake_case :   Tuple													=    torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]				)
            elif mobilevit_name == "mobilevit_xs":
                  __snake_case :   List[Any]													=    torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]				)
            elif mobilevit_name == "mobilevit_xxs":
                  __snake_case :   Optional[Any]													=    torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]				)
            else:
                  raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''				)

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

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

      if push_to_hub:
            __snake_case :   Tuple													=    {
                '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...'				)
            __snake_case :   Optional[int]													=    model_mapping[mobilevit_name]
            image_processor.push_to_hub(_UpperCAmelCase ,organization='apple'				)
            model.push_to_hub(_UpperCAmelCase ,organization='apple'				)


if __name__ == "__main__":
    A__    :   Optional[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.'''
    )

    A__    :   int           =					parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )








'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp



@slow
@require_flax
class  snake_case__						(    unittest.TestCase				):
 def A_   (   self						:							int      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]  )      ->   str:







       '''simple docstring'''
       return f'''gaussian_noise_s={seed}_shape={"_".join([str(__a  ) for s in shape]  )}.npy'''
 def A_   (   self						:							Any  )      ->   List[str]:







       '''simple docstring'''
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
 def A_   (   self						:							List[Any]      ,     __a						:							Union[str, Any]=0      ,     __a						:							List[str]=(4, 4, 64, 64)      ,     __a						:							Optional[Any]=False  )      ->   List[str]:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   List[Any]													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return image
 def A_   (   self						:							Any      ,     __a						:							Any=False      ,     __a						:							Dict="CompVis/stable-diffusion-v1-4"  )      ->   Optional[int]:







       '''simple docstring'''
       __snake_case :   Tuple													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   int													=    'bf16' if fpaa else None

       __snake_case							,     __snake_case :   Union[str, Any]													=    FlaxUNetaDConditionModel.from_pretrained(
           __a      ,     subfolder='unet'      ,     dtype=__a      ,     revision=__a  )
       return model, params
 def A_   (   self						:							Any      ,     __a						:							Dict=0      ,     __a						:							Dict=(4, 77, 768)      ,     __a						:							List[str]=False  )      ->   List[Any]:







       '''simple docstring'''
       __snake_case :   List[Any]													=    jnp.bfloataa if fpaa else jnp.floataa
       __snake_case :   Any													=    jnp.array(load_hf_numpy(self.get_file_format(__a      ,     __a  )  )      ,     dtype=__a  )
       return hidden_states
 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
         [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
         [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
         [3, 1000, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
         # fmt: on
     ]  )
 def A_   (   self						:							List[Any]      ,     __a						:							List[str]      ,     __a						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







       '''simple docstring'''
       __snake_case							,     __snake_case :   Tuple													=    self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'      ,     fpaa=__a  )
       __snake_case :   Tuple													=    self.get_latents(__a      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_encoder_hidden_states(__a      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   str													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )





 @parameterized.expand(
     [
         # fmt: off
         [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
         [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
         [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
         [3, 1000, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
         # fmt: on
     ]  )
 def A_   (   self						:							str      ,     __a						:							Optional[int]      ,     __a						:							Union[str, Any]      ,     __a						:							Tuple  )      ->   Optional[Any]:







       '''simple docstring'''
       __snake_case							,     __snake_case :   int													=    self.get_unet_model(model_id='stabilityai/stable-diffusion-2'      ,     fpaa=__a  )
       __snake_case :   int													=    self.get_latents(__a      ,     shape=(4, 4, 96, 96)      ,     fpaa=__a  )
       __snake_case :   Optional[Any]													=    self.get_encoder_hidden_states(__a      ,     shape=(4, 77, 1024)      ,     fpaa=__a  )

       __snake_case :   List[str]													=    model.apply(
           {'params': params}      ,     __a      ,     jnp.array(__a      ,     dtype=jnp.intaa  )      ,     encoder_hidden_states=__a      ,     ).sample

       assert sample.shape == latents.shape

       __snake_case :   List[Any]													=    jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())  )      ,     dtype=jnp.floataa  )
       __snake_case :   int													=    jnp.array(__a      ,     dtype=jnp.floataa  )

       # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
       assert jnp.allclose(__a      ,     __a      ,     atol=1e-2  )



