




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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[Any]:



       """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__       =    1_000
              A__       =    '''imagenet-1k-id2label.json'''

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

       return config







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False						)   ->       Tuple:



       """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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=False						)   ->       Union[str, Any]:



       """simple docstring"""

       if base_model:
              A__       =    ''''''
       else:
              A__       =    '''mobilevit.'''

       for key in orig_state_dict.copy().keys():
              A__       =    orig_state_dict.pop(lowercase_						)

              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 SCREAMING_SNAKE_CASE				(       )   ->       List[Any]:



       """simple docstring"""

       A__       =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__       =    Image.open(requests.get(lowercase_ ,				stream=lowercase_						).raw						)
       return im







@torch.no_grad()
def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_=False						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    get_mobilevit_config(lowercase_						)

       # load original state_dict
       A__       =    torch.load(lowercase_ ,				map_location='''cpu'''						)

       # load 🤗 model
       if mobilevit_name.startswith('''deeplabv3_'''						):
              A__       =    MobileViTForSemanticSegmentation(lowercase_						).eval()
       else:
              A__       =    MobileViTForImageClassification(lowercase_						).eval()

       A__       =    convert_state_dict(lowercase_ ,				lowercase_						)
       model.load_state_dict(lowercase_						)

       # 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(**lowercase_						)
       A__       =    outputs.logits

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

              if mobilevit_name == "deeplabv3_mobilevit_s":
                     A__       =    torch.tensor(
                         [
                             [[6.20_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":
                     A__       =    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":
                     A__       =    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] ,				lowercase_ ,				atol=1E-4						)
       else:
              assert logits.shape == (1, 1_000)

              if mobilevit_name == "mobilevit_s":
                     A__       =    torch.tensor([-0.98_66, 0.23_92, -1.12_41]						)
              elif mobilevit_name == "mobilevit_xs":
                     A__       =    torch.tensor([-2.47_61, -0.93_99, -1.95_87]						)
              elif mobilevit_name == "mobilevit_xxs":
                     A__       =    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] ,				lowercase_ ,				atol=1E-4						)

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

       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(lowercase_ ,				organization='''apple'''						)
              model.push_to_hub(lowercase_ ,				organization='''apple'''						)


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

  _lowerCamelCase	:		str							       =   parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase	:		int							       =   {
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Tuple							       =   ["""BlipImageProcessor"""]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		List[Any]							       =   [
      """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BlipModel""",
      """BlipPreTrainedModel""",
      """BlipForConditionalGeneration""",
      """BlipForQuestionAnswering""",
      """BlipVisionModel""",
      """BlipTextModel""",
      """BlipForImageTextRetrieval""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase	:		Optional[Any]							       =   [
      """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBlipModel""",
      """TFBlipPreTrainedModel""",
      """TFBlipForConditionalGeneration""",
      """TFBlipForQuestionAnswering""",
      """TFBlipVisionModel""",
      """TFBlipTextModel""",
      """TFBlipForImageTextRetrieval""",
  ]

if TYPE_CHECKING:
  from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
  from .processing_blip import BlipProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_blip import BlipImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_blip import (
        BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        BlipForConditionalGeneration,
        BlipForImageTextRetrieval,
        BlipForQuestionAnswering,
        BlipModel,
        BlipPreTrainedModel,
        BlipTextModel,
        BlipVisionModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_blip import (
        TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBlipForConditionalGeneration,
        TFBlipForImageTextRetrieval,
        TFBlipForQuestionAnswering,
        TFBlipModel,
        TFBlipPreTrainedModel,
        TFBlipTextModel,
        TFBlipVisionModel,
    )

else:
  import sys

  _lowerCamelCase	:		List[Any]							       =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


