





"""simple docstring"""




import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
   from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=1_3					,      snake_case_=3_2					,      snake_case_=2					,      snake_case_=3					,      snake_case_=1_6					,      snake_case_=[1, 2, 1]					,      snake_case_=[2, 2, 4]					,      snake_case_=2					,      snake_case_=2.0					,      snake_case_=True					,      snake_case_=0.0					,      snake_case_=0.0					,      snake_case_=0.1					,      snake_case_="gelu"					,      snake_case_=False					,      snake_case_=True					,      snake_case_=0.02					,      snake_case_=1E-5					,      snake_case_=True					,      snake_case_=None					,      snake_case_=True					,      snake_case_=1_0					,      snake_case_=8					,      ):

        """simple docstring"""

        A_  :       int										=  parent
        A_  :       Dict										=  batch_size
        A_  :       Tuple										=  image_size
        A_  :       List[Any]										=  patch_size
        A_  :       Optional[int]										=  num_channels
        A_  :       List[str]										=  embed_dim
        A_  :       Optional[int]										=  depths
        A_  :       Dict										=  num_heads
        A_  :       List[Any]										=  window_size
        A_  :       Union[str, Any]										=  mlp_ratio
        A_  :       Optional[int]										=  qkv_bias
        A_  :       Union[str, Any]										=  hidden_dropout_prob
        A_  :       Any										=  attention_probs_dropout_prob
        A_  :       Union[str, Any]										=  drop_path_rate
        A_  :       Dict										=  hidden_act
        A_  :       Union[str, Any]										=  use_absolute_embeddings
        A_  :       Union[str, Any]										=  patch_norm
        A_  :       Union[str, Any]										=  layer_norm_eps
        A_  :       List[str]										=  initializer_range
        A_  :       Optional[int]										=  is_training
        A_  :       Optional[int]										=  scope
        A_  :       List[str]										=  use_labels
        A_  :       Any										=  type_sequence_label_size
        A_  :       List[Any]										=  encoder_stride

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        A_  :       List[str]										=  None
        if self.use_labels:
          A_  :       int										=  ids_tensor([self.batch_size]					,      self.type_sequence_label_size  )

        A_  :       List[Any]										=  self.get_config()

        return config, pixel_values, labels

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return SwinvaConfig(
            image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      embed_dim=self.embed_dim					,      depths=self.depths					,      num_heads=self.num_heads					,      window_size=self.window_size					,      mlp_ratio=self.mlp_ratio					,      qkv_bias=self.qkv_bias					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      drop_path_rate=self.drop_path_rate					,      hidden_act=self.hidden_act					,      use_absolute_embeddings=self.use_absolute_embeddings					,      path_norm=self.patch_norm					,      layer_norm_eps=self.layer_norm_eps					,      initializer_range=self.initializer_range					,      encoder_stride=self.encoder_stride					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Any										=  SwinvaModel(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       List[Any]										=  model(snake_case_  )

        A_  :       str										=  ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
        A_  :       Union[str, Any]										=  int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

        self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, expected_seq_len, expected_dim)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       str										=  SwinvaForMaskedImageModeling(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       str										=  model(snake_case_  )
        self.parent.assertEqual(
            result.logits.shape					,      (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

        # test greyscale images
        A_  :       Optional[int]										=  1
        A_  :       str										=  SwinvaForMaskedImageModeling(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        A_  :       List[str]										=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        A_  :       Optional[Any]										=  model(snake_case_  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, 1, self.image_size, self.image_size)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  self.type_sequence_label_size
        A_  :       Dict										=  SwinvaForImageClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       Optional[Any]										=  model(snake_case_					,      labels=snake_case_  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self.prepare_config_and_inputs()
        A_						,       A_						,       A_  :       List[Any]										=  config_and_inputs
        A_  :       List[Any]										=  {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : Tuple	    =   (
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      lowercase_      : Dict	    =   (
          {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      lowercase_      : Dict	    =   False
      lowercase_      : int	    =   False
      lowercase_      : Union[str, Any]	    =   False
      lowercase_      : Dict	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  SwinvaModelTester(self  )
        A_  :       Any										=  ConfigTester(self					,      config_class=snake_case_					,      embed_dim=3_7  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_  )

      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      @unittest.skip(reason='Swinv2 does not use inputs_embeds'  )
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Optional[Any]										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       Optional[Any]										=  model_class(snake_case_  )
          self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)  )
          A_  :       str										=  model.get_output_embeddings()
          self.assertTrue(x is None or isinstance(snake_case_					,      nn.Linear  )  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       List[str]										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       Optional[int]										=  model_class(snake_case_  )
          A_  :       List[str]										=  inspect.signature(model.forward  )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          A_  :       List[str]										=  [*signature.parameters.keys()]

          A_  :       List[str]										=  ['pixel_values']
          self.assertListEqual(arg_names[:1]					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       List[Any]										=  self.model_tester.prepare_config_and_inputs_for_common()
        A_  :       Union[str, Any]										=  True

        for model_class in self.all_model_classes:
          A_  :       str										=  True
          A_  :       Dict										=  False
          A_  :       Tuple										=  True
          A_  :       Union[str, Any]										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          with torch.no_grad():
            A_  :       Dict										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )
          A_  :       str										=  outputs.attentions
          A_  :       List[Any]										=  len(self.model_tester.depths  )
          self.assertEqual(len(snake_case_  )					,      snake_case_  )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          A_  :       int										=  True
          A_  :       List[Any]										=  config.window_size**2
          A_  :       str										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          with torch.no_grad():
            A_  :       Union[str, Any]										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )
          A_  :       List[str]										=  outputs.attentions
          self.assertEqual(len(snake_case_  )					,      snake_case_  )

          self.assertListEqual(
              list(attentions[0].shape[-3:]  )					,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,      )
          A_  :       Dict										=  len(snake_case_  )

          # Check attention is always last and order is fine
          A_  :       Optional[Any]										=  True
          A_  :       Union[str, Any]										=  True
          A_  :       int										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()
          with torch.no_grad():
            A_  :       List[Any]										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )

          if hasattr(self.model_tester					,      'num_hidden_states_types'  ):
            A_  :       int										=  self.model_tester.num_hidden_states_types
          else:
            # also another +1 for reshaped_hidden_states
            A_  :       str										=  2
          self.assertEqual(out_len + added_hidden_states					,      len(snake_case_  )  )

          A_  :       List[str]										=  outputs.attentions

          self.assertEqual(len(snake_case_  )					,      snake_case_  )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:]  )					,      [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  model_class(snake_case_  )
        model.to(snake_case_  )
        model.eval()

        with torch.no_grad():
          A_  :       Optional[Any]										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )

        A_  :       Dict										=  outputs.hidden_states

        A_  :       List[Any]										=  getattr(
            self.model_tester					,      'expected_num_hidden_layers'					,      len(self.model_tester.depths  ) + 1  )
        self.assertEqual(len(snake_case_  )					,      snake_case_  )

        # Swinv2 has a different seq_length
        A_  :       List[Any]										=  (
            config.patch_size
            if isinstance(config.patch_size					,      collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        A_  :       List[Any]										=  (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]  )					,      [num_patches, self.model_tester.embed_dim]					,      )

        A_  :       int										=  outputs.reshaped_hidden_states
        self.assertEqual(len(snake_case_  )					,      snake_case_  )

        A_						,       A_						,       A_						,       A_  :       List[str]										=  reshaped_hidden_states[0].shape
        A_  :       Optional[Any]										=  (
            reshaped_hidden_states[0].view(snake_case_					,      snake_case_					,      height * width  ).permute(0					,      2					,      1  )
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]  )					,      [num_patches, self.model_tester.embed_dim]					,      )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       int										=  self.model_tester.prepare_config_and_inputs_for_common()

        A_  :       int										=  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size					,      collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
          A_  :       int										=  True
          self.check_hidden_states_output(snake_case_					,      snake_case_					,      snake_case_					,      snake_case_  )

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

          self.check_hidden_states_output(snake_case_					,      snake_case_					,      snake_case_					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Dict										=  self.model_tester.prepare_config_and_inputs_for_common()
        A_  :       int										=  3

        A_  :       Any										=  (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size					,      collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        A_  :       Dict										=  (
            config.patch_size
            if isinstance(config.patch_size					,      collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        A_  :       Optional[int]										=  image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        A_  :       Union[str, Any]										=  image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes:
          A_  :       Union[str, Any]										=  True
          self.check_hidden_states_output(snake_case_					,      snake_case_					,      snake_case_					,      (padded_height, padded_width)  )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          A_  :       Union[str, Any]										=  True
          self.check_hidden_states_output(snake_case_					,      snake_case_					,      snake_case_					,      (padded_height, padded_width)  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case_  )

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_  :       str										=  SwinvaModel.from_pretrained(snake_case_  )
          self.assertIsNotNone(snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Dict										=  self.model_tester.prepare_config_and_inputs_for_common()

        A_  :       Optional[int]										=  _config_zero_init(snake_case_  )
        for model_class in self.all_model_classes:
          A_  :       List[str]										=  model_class(config=snake_case_  )
          for name, param in model.named_parameters():
            if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9).round() / 1E9).item()					,      [0.0, 1.0]					,      msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					,      )


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



      '''simple docstring'''

      @cached_property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return (
            AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'  )
            if is_vision_available()
            else None
        )

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       List[str]										=  SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'  ).to(
            snake_case_  )
        A_  :       List[str]										=  self.default_image_processor

        A_  :       Dict										=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
        A_  :       List[Any]										=  image_processor(images=snake_case_					,      return_tensors='pt'  ).to(snake_case_  )

        # forward pass
        with torch.no_grad():
          A_  :       Any										=  model(**snake_case_  )

        # verify the logits
        A_  :       Tuple										=  torch.Size((1, 1_0_0_0)  )
        self.assertEqual(outputs.logits.shape					,      snake_case_  )
        A_  :       Dict										=  torch.tensor([-0.39_47, -0.43_06, 0.00_26]  ).to(snake_case_  )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]					,      snake_case_					,      atol=1E-4  )  )






"""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()
lowerCamelCase_     :						Optional[Any]						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       Optional[Any]										=  []

  # 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_  :       List[str]										=  [(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 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  for i in range(config.num_hidden_layers					):
    if base_model:
      A_  :       List[str]										=  ''
    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_  :       List[Any]										=  in_proj_weight[
        : config.hidden_size, :
    ]
    A_  :       Tuple										=  in_proj_bias[: config.hidden_size]
    A_  :       Union[str, Any]										=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    A_  :       Dict										=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    A_  :       Optional[Any]										=  in_proj_weight[
        -config.hidden_size :, :
    ]
    A_  :       Tuple										=  in_proj_bias[-config.hidden_size :]





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_UpperCAmelCase ,					_UpperCAmelCase					)





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  dct.pop(_UpperCAmelCase					)
  A_  :       Optional[int]										=  val





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





  A_  :       Optional[int]										=  'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_  :       int										=  Image.open(requests.get(_UpperCAmelCase ,					stream=_UpperCAmelCase					).raw					)
  return im





@torch.no_grad()
def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=False					):






  """simple docstring"""





  A_  :       List[Any]										=  BitConfig(
      global_padding='same' ,					layer_type='bottleneck' ,					depths=(3, 4, 9) ,					out_features=['stage3'] ,					embedding_dynamic_padding=_UpperCAmelCase ,					)
  A_  :       Optional[int]										=  ViTHybridConfig(backbone_config=_UpperCAmelCase ,					image_size=384 ,					num_labels=1000					)
  A_  :       Union[str, Any]										=  False

  # load original model from timm
  A_  :       List[Any]										=  timm.create_model(_UpperCAmelCase ,					pretrained=_UpperCAmelCase					)
  timm_model.eval()

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

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

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

  # create image processor
  A_  :       Dict										=  create_transform(**resolve_data_config({} ,					model=_UpperCAmelCase					)					)
  A_  :       List[str]										=  transform.transforms

  A_  :       List[str]										=  {
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

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

  A_  :       Optional[Any]										=  prepare_img()
  A_  :       Any										=  transform(_UpperCAmelCase					).unsqueeze(0					)
  A_  :       Dict										=  processor(_UpperCAmelCase ,					return_tensors='pt'					).pixel_values

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

  # verify logits
  with torch.no_grad():
    A_  :       List[Any]										=  model(_UpperCAmelCase					)
    A_  :       List[str]										=  outputs.logits

  print('Predicted class:' ,					logits.argmax(-1					).item()					)
  if base_model:
    A_  :       Union[str, Any]										=  timm_model.forward_features(_UpperCAmelCase					)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.pooler_output ,					atol=1E-3					)
  else:
    A_  :       Tuple										=  timm_model(_UpperCAmelCase					)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(_UpperCAmelCase ,					outputs.logits ,					atol=1E-3					)
  print('Looks ok!'					)

  if pytorch_dump_folder_path is not None:
    Path(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
    print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""					)
    model.save_pretrained(_UpperCAmelCase					)
    print(f"""Saving processor to {pytorch_dump_folder_path}"""					)
    processor.save_pretrained(_UpperCAmelCase					)

  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__":
   lowerCamelCase_     :						Optional[Any]						=					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.'
   )

   lowerCamelCase_     :						List[str]						=					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)