



'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> List[str]:
      snake_case__  :    Optional[int]    						=      []
      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")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """vit.embeddings.cls_token"""),
              ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """vit.embeddings.position_embeddings"""),
          ]		)

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

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            snake_case__  :    List[Any]    						=      [(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"""),
                ]		)

      return rename_keys





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





def 						__snake_case(				_lowerCAmelCase		)		-> List[str]:
      snake_case__  :    Optional[Any]    						=      ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    List[str]    						=      dct.pop(SCREAMING_SNAKE_CASE_		)
      snake_case__  :    List[Any]    						=      val





def 						__snake_case(				)		-> Union[str, Any]:
      snake_case__  :    str    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[int]    						=      Image.open(requests.get(SCREAMING_SNAKE_CASE_     ,   stream=SCREAMING_SNAKE_CASE_		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=True		)		-> List[Any]:
      snake_case__  :    str    						=      ViTConfig()
      # patch_size
      if model_name[-1] == "8":
            snake_case__  :    Dict    						=      8
      # set labels if required
      if not base_model:
            snake_case__  :    Dict    						=      1_000
            snake_case__  :    Any    						=      """huggingface/label-files"""
            snake_case__  :    Dict    						=      """imagenet-1k-id2label.json"""
            snake_case__  :    Optional[Any]    						=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
            snake_case__  :    int    						=      {int(SCREAMING_SNAKE_CASE_		): v for k, v in idalabel.items()}
            snake_case__  :    List[str]    						=      idalabel
            snake_case__  :    int    						=      {v: k for k, v in idalabel.items()}
      # size of the architecture
      if model_name in ["dino_vits8", "dino_vits16"]:
            snake_case__  :    List[Any]    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Optional[int]    						=      6

      # load original model from torch hub
      snake_case__  :    Optional[int]    						=      torch.hub.load("""facebookresearch/dino:main"""     ,   SCREAMING_SNAKE_CASE_		)
      original_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    List[str]    						=      original_model.state_dict()
      if base_model:
            remove_classification_head_(SCREAMING_SNAKE_CASE_		)
      snake_case__  :    Any    						=      create_rename_keys(SCREAMING_SNAKE_CASE_     ,   base_model=SCREAMING_SNAKE_CASE_		)
      for src, dest in rename_keys:
            rename_key(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_		)
      read_in_q_k_v(SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_     ,   SCREAMING_SNAKE_CASE_		)

      # load HuggingFace model
      if base_model:
            snake_case__  :    Optional[int]    						=      ViTModel(SCREAMING_SNAKE_CASE_     ,   add_pooling_layer=SCREAMING_SNAKE_CASE_		).eval()
      else:
            snake_case__  :    int    						=      ViTForImageClassification(SCREAMING_SNAKE_CASE_		).eval()
      model.load_state_dict(SCREAMING_SNAKE_CASE_		)

      # Check outputs on an image, prepared by ViTImageProcessor
      snake_case__  :    List[str]    						=      ViTImageProcessor()
      snake_case__  :    List[str]    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Any    						=      encoding["""pixel_values"""]
      snake_case__  :    int    						=      model(SCREAMING_SNAKE_CASE_		)

      if base_model:
            snake_case__  :    Any    						=      original_model(SCREAMING_SNAKE_CASE_		)
            assert torch.allclose(SCREAMING_SNAKE_CASE_     ,   outputs.last_hidden_state[:, 0, :]     ,   atol=1e-1		)
      else:
            snake_case__  :    int    						=      original_model(SCREAMING_SNAKE_CASE_		)
            assert logits.shape == outputs.logits.shape
            assert torch.allclose(SCREAMING_SNAKE_CASE_     ,   outputs.logits     ,   atol=1e-3		)

      Path(SCREAMING_SNAKE_CASE_		).mkdir(exist_ok=SCREAMING_SNAKE_CASE_		)
      print(f"Saving model {model_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(SCREAMING_SNAKE_CASE_		)
      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="dino_vitb16",
           type=str,
           help="Name of the model trained with DINO 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(
           "--base_model",
           action="store_true",
           help="Whether to only convert the base model (no projection head weights).",
       )

       parser.set_defaults(base_model=True)
       __a          =		parser.parse_args()
       convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    			=			{"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




