



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						:		int			= logging.get_logger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Tuple:







  '''simple docstring'''


  lowercase_								= MobileViTConfig()

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

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

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

  return config


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=False							) ->						Dict:







  '''simple docstring'''


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

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

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

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

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

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

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

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

  return name


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=False							) ->						Any:







  '''simple docstring'''


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

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

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

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

  return orig_state_dict


def 			_SCREAMING_SNAKE_CASE						() ->						List[str]:







  '''simple docstring'''


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


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=False							) ->						Tuple:







  '''simple docstring'''


  lowercase_								= get_mobilevit_config(__lowerCAmelCase							)

  # load original state_dict
  lowercase_								= torch.load(__lowerCAmelCase ,  map_location="""cpu"""							)

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

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

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

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

    if mobilevit_name == "deeplabv3_mobilevit_s":
      lowercase_								= torch.tensor(
          [
              [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
              [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
              [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
          ]							)
    elif mobilevit_name == "deeplabv3_mobilevit_xs":
      lowercase_								= torch.tensor(
          [
              [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
              [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
              [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
          ]							)
    elif mobilevit_name == "deeplabv3_mobilevit_xxs":
      lowercase_								= torch.tensor(
          [
              [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
              [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
              [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
          ]							)
    else:
      raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''							)

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

    if mobilevit_name == "mobilevit_s":
      lowercase_								= torch.tensor([-0.9_866, 0.2_392, -1.1_241]							)
    elif mobilevit_name == "mobilevit_xs":
      lowercase_								= torch.tensor([-2.4_761, -0.9_399, -1.9_587]							)
    elif mobilevit_name == "mobilevit_xxs":
      lowercase_								= torch.tensor([-1.9_364, -1.2_327, -0.4_653]							)
    else:
      raise ValueError(F'''Unknown mobilevit_name: {mobilevit_name}'''							)

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

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

  if push_to_hub:
    lowercase_								= {
        """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..."""							)
    lowercase_								= model_mapping[mobilevit_name]
    image_processor.push_to_hub(__lowerCAmelCase ,  organization="""apple"""							)
    model.push_to_hub(__lowerCAmelCase ,  organization="""apple"""							)


if __name__ == "__main__":
       UpperCAmelCase						:		Tuple			= 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						:		Optional[Any]			= parser.parse_args()
       convert_movilevit_checkpoint(
           args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
       )






"""simple docstring"""
from typing import TYPE_CHECKING

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


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

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

