





'''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()
A__    :       Union[str, Any]              =logging.get_logger(__name__)
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase=False  ):





      """simple docstring"""


      _lowerCAmelCase												=							[]

      # 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"
            _lowerCAmelCase												=							[(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__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=False  ):





      """simple docstring"""


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





      """simple docstring"""


      _lowerCAmelCase												=							["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(snake_case__							,     snake_case__  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							dct.pop(snake_case__  )
      _lowerCAmelCase												=							val
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase												=							Image.open(requests.get(snake_case__							,     stream=snake_case__  ).raw  )
      return im
@torch.no_grad()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=False  ):





      """simple docstring"""


      _lowerCAmelCase												=							BitConfig(
          global_padding="""same"""							,     layer_type="""bottleneck"""							,     depths=(3, 4, 9)							,     out_features=["""stage3"""]							,     embedding_dynamic_padding=snake_case__							,     )
      _lowerCAmelCase												=							ViTHybridConfig(backbone_config=snake_case__							,     image_size=3_84							,     num_labels=10_00  )
      _lowerCAmelCase												=							False

      # load original model from timm
      _lowerCAmelCase												=							timm.create_model(snake_case__							,     pretrained=snake_case__  )
      timm_model.eval()

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

      _lowerCAmelCase												=							"""huggingface/label-files"""
      _lowerCAmelCase												=							"""imagenet-1k-id2label.json"""
      _lowerCAmelCase												=							json.load(open(hf_hub_download(snake_case__							,     snake_case__							,     repo_type="""dataset"""  )							,     """r"""  )  )
      _lowerCAmelCase												=							{int(snake_case__  ): v for k, v in idalabel.items()}
      _lowerCAmelCase												=							idalabel
      _lowerCAmelCase												=							{v: k for k, v in idalabel.items()}

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            _lowerCAmelCase												=							ViTHybridModel(snake_case__  ).eval()
      else:
            _lowerCAmelCase												=							ViTHybridForImageClassification(snake_case__  ).eval()
      model.load_state_dict(snake_case__  )

      # create image processor
      _lowerCAmelCase												=							create_transform(**resolve_data_config({}							,     model=snake_case__  )  )
      _lowerCAmelCase												=							transform.transforms

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

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

      _lowerCAmelCase												=							prepare_img()
      _lowerCAmelCase												=							transform(snake_case__  ).unsqueeze(0  )
      _lowerCAmelCase												=							processor(snake_case__							,     return_tensors="""pt"""  ).pixel_values

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

      # verify logits
      with torch.no_grad():
            _lowerCAmelCase												=							model(snake_case__  )
            _lowerCAmelCase												=							outputs.logits

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

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

      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__":
      A__    :       int              =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.'''
      )

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













'''simple docstring'''






import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:




      class 			UpperCAmelCase					:




            @staticmethod
            def     lowercase__   (      *__snake_case      :			Optional[Any]      ,      **__snake_case      :			Any  )    ->				Tuple:
                  pass




@is_pipeline_test
@require_torch
@require_vision
class 			UpperCAmelCase					(      unittest.TestCase       ):
      _lowercase:      Union[str, Any]											=       MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			str      ,      __snake_case      :			List[str]  )    ->				int:
            _lowerCAmelCase												=							pipeline("""visual-question-answering"""      ,      model="""hf-internal-testing/tiny-vilt-random-vqa"""  )
            _lowerCAmelCase												=							[
                {
                    """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  ),
                    """question""": """How many cats are there?""",
                },
                {
                    """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                    """question""": """How many cats are there?""",
                },
            ]
            return vqa_pipeline, examples




      def     lowercase__   (      self      :			Any      ,      __snake_case      :			List[Any]      ,      __snake_case      :			List[Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							vqa_pipeline(__snake_case      ,      top_k=1  )
            self.assertEqual(
                __snake_case      ,      [
                    [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}],
                    [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}],
                ]      ,      )




      @require_torch
      def     lowercase__   (      self      :			str  )    ->				int:
            _lowerCAmelCase												=							pipeline("""visual-question-answering"""      ,      model="""hf-internal-testing/tiny-vilt-random-vqa"""  )
            _lowerCAmelCase												=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
            _lowerCAmelCase												=							"""How many cats are there?"""

            _lowerCAmelCase												=							vqa_pipeline(image=__snake_case      ,      question="""How many cats are there?"""      ,      top_k=2  )
            self.assertEqual(
                __snake_case      ,      [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}, {"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}]  )

            _lowerCAmelCase												=							vqa_pipeline({"""image""": image, """question""": question}      ,      top_k=2  )
            self.assertEqual(
                __snake_case      ,      [{"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}, {"""score""": ANY(__snake_case  ), """answer""": ANY(__snake_case  )}]  )




      @slow
      @require_torch
      def     lowercase__   (      self      :			List[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							pipeline("""visual-question-answering"""      ,      model="""dandelin/vilt-b32-finetuned-vqa"""  )
            _lowerCAmelCase												=							"""./tests/fixtures/tests_samples/COCO/000000039769.png"""
            _lowerCAmelCase												=							"""How many cats are there?"""

            _lowerCAmelCase												=							vqa_pipeline(image=__snake_case      ,      question=__snake_case      ,      top_k=2  )
            self.assertEqual(
                nested_simplify(__snake_case      ,      decimals=4  )      ,      [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]  )

            _lowerCAmelCase												=							vqa_pipeline({"""image""": image, """question""": question}      ,      top_k=2  )
            self.assertEqual(
                nested_simplify(__snake_case      ,      decimals=4  )      ,      [{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]  )

            _lowerCAmelCase												=							vqa_pipeline(
                [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]      ,      top_k=2  )
            self.assertEqual(
                nested_simplify(__snake_case      ,      decimals=4  )      ,      [[{"""score""": 0.87_99, """answer""": """2"""}, {"""score""": 0.2_96, """answer""": """1"""}]] * 2      ,      )




      @require_tf
      @unittest.skip("""Visual question answering not implemented in TF"""  )
      def     lowercase__   (      self      :			List[str]  )    ->				Union[str, Any]:
            pass






