'''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()
SCREAMING_SNAKE_CASE_:	str    =logging.get_logger(__name__)





def       lowerCAmelCase_							(   snake_case_			:					int   ,       snake_case_			:					Dict=False     )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  []

     # 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"
          UpperCAmelCase_			       =  [(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       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					Optional[Any]   ,       snake_case_			:					Optional[Any]=False     )       -> Any:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					Tuple     )       -> List[str]:


     '''simple docstring'''




     UpperCAmelCase_			       =  ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(snake_case_   ,       snake_case_     )





def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					int   ,       snake_case_			:					Tuple     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  dct.pop(snake_case_     )
     UpperCAmelCase_			       =  val





def       lowerCAmelCase_							(   )       -> int:


     '''simple docstring'''




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





@torch.no_grad()
def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					List[str]   ,       snake_case_			:					List[Any]=False     )       -> List[Any]:


     '''simple docstring'''




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

     # load original model from timm
     UpperCAmelCase_			       =  timm.create_model(snake_case_   ,       pretrained=snake_case_     )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase_			       =  timm_model.state_dict()
     if base_model:
          remove_classification_head_(snake_case_     )
     UpperCAmelCase_			       =  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_     )

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

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

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

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

     UpperCAmelCase_			       =  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()   ,       )

     UpperCAmelCase_			       =  prepare_img()
     UpperCAmelCase_			       =  transform(snake_case_     ).unsqueeze(0     )
     UpperCAmelCase_			       =  processor(snake_case_   ,       return_tensors="pt"     ).pixel_values

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

     # verify logits
     with torch.no_grad():
          UpperCAmelCase_			       =  model(snake_case_     )
          UpperCAmelCase_			       =  outputs.logits

     print("Predicted class:"   ,       logits.argmax(-1     ).item()     )
     if base_model:
          UpperCAmelCase_			       =  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:
          UpperCAmelCase_			       =  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__":
    SCREAMING_SNAKE_CASE_:	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.'
    )

    SCREAMING_SNAKE_CASE_:	Optional[int]    =parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCamelCase									=     {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase									=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
