


"""simple docstring"""







import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
 from transformers.models.mobilevitva.modeling_mobilevitva import (
     MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
     make_divisible,
 )


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor






class 						_A  (  _a  ):







    """simple docstring"""




    def      __snake_case							( self    :   Union[str, Any]):
      a						:		List[str]											=      self.config_class(**self.inputs_dict)
      self.parent.assertTrue(hasattr(__UpperCAmelCase    ,			"width_multiplier"))






class 						_A  :







    """simple docstring"""




    def __init__( self    :   int    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   str=13    ,			__UpperCAmelCase    :   str=64    ,			__UpperCAmelCase    :   Any=2    ,			__UpperCAmelCase    :   int=3    ,			__UpperCAmelCase    :   Optional[Any]="swish"    ,			__UpperCAmelCase    :   str=3    ,			__UpperCAmelCase    :   int=32    ,			__UpperCAmelCase    :   List[str]=0.1    ,			__UpperCAmelCase    :   int=0.02    ,			__UpperCAmelCase    :   List[str]=True    ,			__UpperCAmelCase    :   Tuple=True    ,			__UpperCAmelCase    :   List[str]=10    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			__UpperCAmelCase    :   Optional[Any]=0.25    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Tuple=0.0    ,			):
      a						:		Tuple											=      parent
      a						:		Optional[Any]											=      batch_size
      a						:		List[Any]											=      image_size
      a						:		Tuple											=      patch_size
      a						:		Any											=      num_channels
      a						:		Union[str, Any]											=      make_divisible(512 * width_multiplier    ,			divisor=8)
      a						:		Any											=      hidden_act
      a						:		List[Any]											=      conv_kernel_size
      a						:		Tuple											=      output_stride
      a						:		int											=      classifier_dropout_prob
      a						:		int											=      use_labels
      a						:		Dict											=      is_training
      a						:		Any											=      num_labels
      a						:		Any											=      initializer_range
      a						:		int											=      scope
      a						:		Any											=      width_multiplier
      a						:		Optional[int]											=      ffn_dropout
      a						:		Optional[int]											=      attn_dropout






    def      __snake_case							( self    :   Any):
      a						:		List[Any]											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      a						:		List[Any]											=      None
      a						:		Any											=      None
      if self.use_labels:
        a						:		Optional[int]											=      ids_tensor([self.batch_size]    ,			self.num_labels)
        a						:		List[str]											=      ids_tensor([self.batch_size, self.image_size, self.image_size]    ,			self.num_labels)

      a						:		Tuple											=      self.get_config()

      return config, pixel_values, labels, pixel_labels






    def      __snake_case							( self    :   Dict):
      return MobileViTVaConfig(
          image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			hidden_act=self.hidden_act    ,			conv_kernel_size=self.conv_kernel_size    ,			output_stride=self.output_stride    ,			classifier_dropout_prob=self.classifier_dropout_prob    ,			initializer_range=self.initializer_range    ,			width_multiplier=self.width_multiplier    ,			ffn_dropout=self.ffn_dropout_prob    ,			attn_dropout=self.attn_dropout_prob    ,			)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   Optional[int]    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		Union[str, Any]											=      MobileViTVaModel(config=__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a						:		Tuple											=      model(__UpperCAmelCase)
      self.parent.assertEqual(
          result.last_hidden_state.shape    ,			(
              self.batch_size,
              self.last_hidden_size,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )    ,			)






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Tuple):
      a						:		List[Any]											=      self.num_labels
      a						:		List[str]											=      MobileViTVaForImageClassification(__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a						:		List[str]											=      model(__UpperCAmelCase    ,			labels=__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels))






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		Union[str, Any]											=      self.num_labels
      a						:		List[str]											=      MobileViTVaForSemanticSegmentation(__UpperCAmelCase)
      model.to(__UpperCAmelCase)
      model.eval()
      a						:		List[str]											=      model(__UpperCAmelCase)
      self.parent.assertEqual(
          result.logits.shape    ,			(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )    ,			)
      a						:		List[Any]											=      model(__UpperCAmelCase    ,			labels=__UpperCAmelCase)
      self.parent.assertEqual(
          result.logits.shape    ,			(
              self.batch_size,
              self.num_labels,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )    ,			)






    def      __snake_case							( self    :   Any):
      a						:		Any											=      self.prepare_config_and_inputs()
      a							,		a							,		a							,		a						:		Optional[int]											=      config_and_inputs
      a						:		Optional[Any]											=      {"pixel_values": pixel_values}
      return config, inputs_dict






@require_torch
class 						_A  (  _a   ,_a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							int     =	(
        (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
        if is_torch_available()
        else ()
    )

    UpperCAmelCase	:							Dict     =	(
        {
            """feature-extraction""": MobileViTVaModel,
            """image-classification""": MobileViTVaForImageClassification,
            """image-segmentation""": MobileViTVaForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase	:							Any     =	False
    UpperCAmelCase	:							int     =	False
    UpperCAmelCase	:							int     =	False
    UpperCAmelCase	:							Optional[Any]     =	False






    def      __snake_case							( self    :   List[Any]):
      a						:		Tuple											=      MobileViTVaModelTester(self)
      a						:		Dict											=      MobileViTVaConfigTester(self    ,			config_class=__UpperCAmelCase    ,			has_text_modality=__UpperCAmelCase)






    def      __snake_case							( self    :   Dict):
      self.config_tester.run_common_tests()






    @unittest.skip(reason="MobileViTV2 does not use inputs_embeds")
    def      __snake_case							( self    :   Optional[int]):
      pass






    @unittest.skip(reason="MobileViTV2 does not support input and output embeddings")
    def      __snake_case							( self    :   Dict):
      pass






    @unittest.skip(reason="MobileViTV2 does not output attentions")
    def      __snake_case							( self    :   List[str]):
      pass






    @require_torch_multi_gpu
    @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.")
    def      __snake_case							( self    :   str):
      pass






    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
    def      __snake_case							( self    :   Dict):
      pass






    def      __snake_case							( self    :   Any):
      a							,		a						:		Any											=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						:		str											=      model_class(__UpperCAmelCase)
        a						:		str											=      inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a						:		Tuple											=      [*signature.parameters.keys()]

        a						:		List[Any]											=      ["pixel_values"]
        self.assertListEqual(arg_names[:1]    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   str):
      a						:		int											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase)






    def      __snake_case							( self    :   Dict):
      def check_hidden_states_output(__UpperCAmelCase    :   Union[str, Any]    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   List[Any]):
        a						:		Dict											=      model_class(__UpperCAmelCase)
        model.to(__UpperCAmelCase)
        model.eval()

        with torch.no_grad():
          a						:		Optional[int]											=      model(**self._prepare_for_class(__UpperCAmelCase    ,			__UpperCAmelCase))

        a						:		Tuple											=      outputs.hidden_states

        a						:		str											=      5
        self.assertEqual(len(__UpperCAmelCase)    ,			__UpperCAmelCase)

        # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
        # with the width and height being successively divided by 2.
        a						:		int											=      2
        for i in range(len(__UpperCAmelCase)):
          self.assertListEqual(
              list(hidden_states[i].shape[-2:])    ,			[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,			)
          divisor *= 2

        self.assertEqual(self.model_tester.output_stride    ,			divisor // 2)

      a							,		a						:		Union[str, Any]											=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						:		Optional[Any]											=      True
        check_hidden_states_output(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a						:		Any											=      True

        check_hidden_states_output(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   Tuple):
      a						:		List[str]											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase)






    def      __snake_case							( self    :   Optional[int]):
      a						:		Dict											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase)






    @slow
    def      __snake_case							( self    :   Optional[int]):
      for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						:		Optional[Any]											=      MobileViTVaModel.from_pretrained(__UpperCAmelCase)
        self.assertIsNotNone(__UpperCAmelCase)






def 					lowercase      (    )->				Dict:
  '''simple docstring'''







  a						:		Optional[Any]											=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
  return image







@require_torch
@require_vision
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @cached_property
    def      __snake_case							( self    :   Tuple):
      return (
          MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256")
          if is_vision_available()
          else None
      )






    @slow
    def      __snake_case							( self    :   List[Any]):
      a						:		Optional[int]											=      MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").to(
          __UpperCAmelCase)

      a						:		Union[str, Any]											=      self.default_image_processor
      a						:		Optional[Any]											=      prepare_img()
      a						:		Union[str, Any]											=      image_processor(images=__UpperCAmelCase    ,			return_tensors="pt").to(__UpperCAmelCase)

      # forward pass
      with torch.no_grad():
        a						:		str											=      model(**__UpperCAmelCase)

      # verify the logits
      a						:		Dict											=      torch.Size((1, 1000))
      self.assertEqual(outputs.logits.shape    ,			__UpperCAmelCase)

      a						:		int											=      torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]).to(__UpperCAmelCase)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,			__UpperCAmelCase    ,			atol=1e-4))






    @slow
    def      __snake_case							( self    :   str):
      a						:		Optional[Any]											=      MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
      a						:		Optional[Any]											=      model.to(__UpperCAmelCase)

      a						:		Optional[Any]											=      MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

      a						:		Optional[Any]											=      prepare_img()
      a						:		Any											=      image_processor(images=__UpperCAmelCase    ,			return_tensors="pt").to(__UpperCAmelCase)

      # forward pass
      with torch.no_grad():
        a						:		Union[str, Any]											=      model(**__UpperCAmelCase)
      a						:		int											=      outputs.logits

      # verify the logits
      a						:		Dict											=      torch.Size((1, 21, 32, 32))
      self.assertEqual(logits.shape    ,			__UpperCAmelCase)

      a						:		Optional[int]											=      torch.tensor(
          [
              [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
              [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
              [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
          ]    ,			device=__UpperCAmelCase    ,			)

      self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,			__UpperCAmelCase    ,			atol=1e-4))






    @slow
    def      __snake_case							( self    :   List[str]):
      a						:		Optional[Any]											=      MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")
      a						:		str											=      model.to(__UpperCAmelCase)

      a						:		Union[str, Any]											=      MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3")

      a						:		List[Any]											=      prepare_img()
      a						:		Dict											=      image_processor(images=__UpperCAmelCase    ,			return_tensors="pt").to(__UpperCAmelCase)

      # forward pass
      with torch.no_grad():
        a						:		str											=      model(**__UpperCAmelCase)

      a						:		int											=      outputs.logits.detach().cpu()

      a						:		Union[str, Any]											=      image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase    ,			target_sizes=[(50, 60)])
      a						:		str											=      torch.Size((50, 60))
      self.assertEqual(segmentation[0].shape    ,			__UpperCAmelCase)

      a						:		Optional[int]											=      image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase)
      a						:		Tuple											=      torch.Size((32, 32))
      self.assertEqual(segmentation[0].shape    ,			__UpperCAmelCase)










"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase    =				logging.get_logger(__name__)


def 					lowercase      (    A_  ,       A_=False	)->				int:
  '''simple docstring'''







  a						:		List[str]											=      []

  # fmt: off
  # stem:
  rename_keys.append(("cls_token", "vit.embeddings.cls_token")	)
  rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")	)

  rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")	)
  rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")	)

  # backbone
  rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")	)
  rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")	)
  rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")	)

  for stage_idx in range(len(config.backbone_config.depths	)	):
    for layer_idx in range(config.backbone_config.depths[stage_idx]	):
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')	)

    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')	)
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')	)
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')	)

  # transformer encoder
  for i in range(config.num_hidden_layers	):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')	)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
            ("pre_logits.fc.weight", "pooler.dense.weight"),
            ("pre_logits.fc.bias", "pooler.dense.bias"),
        ]	)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    a						:		Tuple											=      [(pair[0], pair[1][4:]) if pair[1].startswith("vit"	) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("norm.weight", "vit.layernorm.weight"),
            ("norm.bias", "vit.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]	)
  # fmt: on

  return rename_keys


def 					lowercase      (    A_  ,       A_  ,       A_=False	)->				Optional[Any]:
  '''simple docstring'''







  for i in range(config.num_hidden_layers	):
    if base_model:
      a						:		Tuple											=      ""
    else:
      a						:		Dict											=      "vit."
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    a						:		str											=      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
    a						:		List[Any]											=      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
    # next, add query, keys and values (in that order) to the state dict
    a						:		Optional[int]											=      in_proj_weight[
        : config.hidden_size, :
    ]
    a						:		Tuple											=      in_proj_bias[: config.hidden_size]
    a						:		List[Any]											=      in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a						:		Optional[int]											=      in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    a						:		int											=      in_proj_weight[
        -config.hidden_size :, :
    ]
    a						:		int											=      in_proj_bias[-config.hidden_size :]


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a						:		Dict											=      ["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(A_  ,       A_	)


def 					lowercase      (    A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		List[Any]											=      dct.pop(A_	)
  a						:		str											=      val


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  a						:		Any											=      "http://images.cocodataset.org/val2017/000000039769.jpg"
  a						:		Tuple											=      Image.open(requests.get(A_  ,       stream=A_	).raw	)
  return im


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_=False	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      BitConfig(
      global_padding="same"  ,       layer_type="bottleneck"  ,       depths=(3, 4, 9)  ,       out_features=["stage3"]  ,       embedding_dynamic_padding=A_  ,       )
  a						:		Union[str, Any]											=      ViTHybridConfig(backbone_config=A_  ,       image_size=384  ,       num_labels=1_000	)
  a						:		Optional[Any]											=      False

  # load original model from timm
  a						:		Any											=      timm.create_model(A_  ,       pretrained=A_	)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  a						:		Optional[Any]											=      timm_model.state_dict()
  if base_model:
    remove_classification_head_(A_	)
  a						:		int											=      create_rename_keys(A_  ,       A_	)
  for src, dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)
  read_in_q_k_v(A_  ,       A_  ,       A_	)

  a						:		Union[str, Any]											=      "huggingface/label-files"
  a						:		Optional[int]											=      "imagenet-1k-id2label.json"
  a						:		str											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		Optional[Any]											=      {int(A_	): v for k, v in idalabel.items()}
  a						:		str											=      idalabel
  a						:		Any											=      {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    a						:		List[Any]											=      ViTHybridModel(A_	).eval()
  else:
    a						:		Optional[int]											=      ViTHybridForImageClassification(A_	).eval()
  model.load_state_dict(A_	)

  # create image processor
  a						:		Tuple											=      create_transform(**resolve_data_config({}  ,       model=A_	)	)
  a						:		List[Any]											=      transform.transforms

  a						:		int											=      {
      "bilinear": PILImageResampling.BILINEAR,
      "bicubic": PILImageResampling.BICUBIC,
      "nearest": PILImageResampling.NEAREST,
  }

  a						:		str											=      ViTHybridImageProcessor(
      do_resize=A_  ,       size={"shortest_edge": timm_transforms[0].size}  ,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,       do_center_crop=A_  ,       crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}  ,       do_normalize=A_  ,       image_mean=timm_transforms[-1].mean.tolist()  ,       image_std=timm_transforms[-1].std.tolist()  ,       )

  a						:		List[Any]											=      prepare_img()
  a						:		Optional[Any]											=      transform(A_	).unsqueeze(0	)
  a						:		str											=      processor(A_  ,       return_tensors="pt"	).pixel_values

  # verify pixel values
  assert torch.allclose(A_  ,       A_	)

  # verify logits
  with torch.no_grad():
    a						:		Dict											=      model(A_	)
    a						:		Tuple											=      outputs.logits

  print("Predicted class:"  ,       logits.argmax(-1	).item()	)
  if base_model:
    a						:		str											=      timm_model.forward_features(A_	)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(A_  ,       outputs.pooler_output  ,       atol=1e-3	)
  else:
    a						:		int											=      timm_model(A_	)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(A_  ,       outputs.logits  ,       atol=1e-3	)
  print("Looks ok!"	)

  if pytorch_dump_folder_path is not None:
    Path(A_	).mkdir(exist_ok=A_	)
    print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(A_	)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''	)
    processor.save_pretrained(A_	)

  if push_to_hub:
    print(F'''Pushing model and processor to the hub {vit_name}'''	)
    model.push_to_hub(F'''ybelkada/{vit_name}'''	)
    processor.push_to_hub(F'''ybelkada/{vit_name}'''	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_r50_s16_384""",
     type=str,
     help="""Name of the hybrid ViT timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
 )

 __lowercase    =				parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






