"""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()
__lowerCAmelCase					: Tuple        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            lowercase														=			[1_4_4, 1_9_2, 2_4_0]
            lowercase														=			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
      elif "mobilevit_xs" in mobilevit_name:
            lowercase														=			[9_6, 1_2_0, 1_4_4]
            lowercase														=			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
      elif "mobilevit_xxs" in mobilevit_name:
            lowercase														=			[6_4, 8_0, 9_6]
            lowercase														=			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
            lowercase														=			0.05
            lowercase														=			2.0

      if mobilevit_name.startswith("""deeplabv3_"""	):
            lowercase														=			5_1_2
            lowercase														=			1_6
            lowercase														=			2_1
            lowercase														=			"""pascal-voc-id2label.json"""
      else:
            lowercase														=			1_0_0_0
            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     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Tuple=False	)				->					str:
      '''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     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :List[Any]      ,	lowerCAmelCase__  :str=False	)				->					List[str]:
      '''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     UpperCAmelCase__ (       )				->					Optional[int]:
      '''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     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Any=False	)				->					int:
      '''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 + 3_2	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	)
      lowercase														=			model(**lowerCAmelCase__	)
      lowercase														=			outputs.logits

      if mobilevit_name.startswith("""deeplabv3_"""	):
            assert logits.shape == (1, 2_1, 3_2, 3_2)

            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, 1_0_0_0)

            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__":
       __lowerCAmelCase					: 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."""
       )

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




"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					: Any        =logging.get_logger(__name__)

__lowerCAmelCase					: Union[str, Any]        ={
    """facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Optional[Any]													=			'timesformer'

      def __init__(		self	, __lowerCAmelCase=224	, __lowerCAmelCase=16	, __lowerCAmelCase=3	, __lowerCAmelCase=8	, __lowerCAmelCase=768	, __lowerCAmelCase=12	, __lowerCAmelCase=12	, __lowerCAmelCase=3072	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1E-6	, __lowerCAmelCase=True	, __lowerCAmelCase="divided_space_time"	, __lowerCAmelCase=0	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)

            lowercase														=			image_size
            lowercase														=			patch_size
            lowercase														=			num_channels
            lowercase														=			num_frames

            lowercase														=			hidden_size
            lowercase														=			num_hidden_layers
            lowercase														=			num_attention_heads
            lowercase														=			intermediate_size
            lowercase														=			hidden_act
            lowercase														=			hidden_dropout_prob
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			initializer_range
            lowercase														=			layer_norm_eps
            lowercase														=			qkv_bias

            lowercase														=			attention_type
            lowercase														=			drop_path_rate



