



'''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()
lowerCAmelCase: Optional[int]							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A			,      _A=False    ):
  a						: str					       =					[]
  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"
    a						: 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'),
        ]    )

  return rename_keys
def 				lowerCamelCase__	(						_A			,      _A			,      _A=False    ):
  for i in range(config.num_hidden_layers    ):
    if base_model:
      a						: List[str]					       =					''
    else:
      a						: Union[str, Any]					       =					'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    a						: int					       =					state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
    a						: Tuple					       =					state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
    # next, add query, keys and values (in that order) to the state dict
    a						: str					       =					in_proj_weight[
        : config.hidden_size, :
    ]
    a						: int					       =					in_proj_bias[: config.hidden_size]
    a						: Union[str, Any]					       =					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a						: List[Any]					       =					in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    a						: Tuple					       =					in_proj_weight[
        -config.hidden_size :, :
    ]
    a						: Dict					       =					in_proj_bias[-config.hidden_size :]
def 				lowerCamelCase__	(						_A    ):
  a						: Optional[Any]					       =					['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_A			,      _A    )
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: List[Any]					       =					dct.pop(_A    )
  a						: Optional[int]					       =					val
def 				lowerCamelCase__	(						):
  a						: Union[str, Any]					       =					'http://images.cocodataset.org/val2017/000000039769.jpg'
  a						: Tuple					       =					Image.open(requests.get(_A			,      stream=_A    ).raw    )
  return im
@torch.no_grad()
def 				lowerCamelCase__	(						_A			,      _A			,      _A=True    ):
  a						: str					       =					ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    a						: Optional[int]					       =					8
  # set labels if required
  if not base_model:
    a						: Optional[int]					       =					1000
    a						: str					       =					'huggingface/label-files'
    a						: int					       =					'imagenet-1k-id2label.json'
    a						: str					       =					json.load(open(hf_hub_download(_A			,      _A			,      repo_type='dataset'    )			,      'r'    )    )
    a						: Dict					       =					{int(_A    ): v for k, v in idalabel.items()}
    a						: List[str]					       =					idalabel
    a						: str					       =					{v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    a						: Any					       =					384
    a						: int					       =					1536
    a						: Tuple					       =					12
    a						: List[str]					       =					6

  # load original model from torch hub
  a						: Any					       =					torch.hub.load('facebookresearch/dino:main'			,      _A    )
  original_model.eval()

  # load state_dict of original model, remove and rename some keys
  a						: int					       =					original_model.state_dict()
  if base_model:
    remove_classification_head_(_A    )
  a						: Tuple					       =					create_rename_keys(_A			,      base_model=_A    )
  for src, dest in rename_keys:
    rename_key(_A			,      _A			,      _A    )
  read_in_q_k_v(_A			,      _A			,      _A    )

  # load HuggingFace model
  if base_model:
    a						: Optional[Any]					       =					ViTModel(_A			,      add_pooling_layer=_A    ).eval()
  else:
    a						: List[Any]					       =					ViTForImageClassification(_A    ).eval()
  model.load_state_dict(_A    )

  # Check outputs on an image, prepared by ViTImageProcessor
  a						: Dict					       =					ViTImageProcessor()
  a						: Union[str, Any]					       =					image_processor(images=prepare_img()			,      return_tensors='pt'    )
  a						: str					       =					encoding['pixel_values']
  a						: Any					       =					model(_A    )

  if base_model:
    a						: List[Any]					       =					original_model(_A    )
    assert torch.allclose(_A			,      outputs.last_hidden_state[:, 0, :]			,      atol=1E-1    )
  else:
    a						: int					       =					original_model(_A    )
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(_A			,      outputs.logits			,      atol=1E-3    )

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


if __name__ == "__main__":
     lowerCAmelCase: List[Any]							=						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)
     lowerCAmelCase: int							=						parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




'''simple docstring'''






import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 							a__( lowerCamelCase__		,			unittest.TestCase     ):
    lowercase__			=   CTRLTokenizer
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  Dict     ):
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      a						: Tuple					       =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
      a						: Union[str, Any]					       =					dict(zip(__snake_case						,      range(len(__snake_case     )     )     )     )
      a						: Union[str, Any]					       =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
      a						: Optional[Any]					       =					{'unk_token': '<unk>'}

      a						: Dict					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']     )
      a						: Optional[Any]					       =					os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['merges_file']     )
      with open(self.vocab_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write(json.dumps(__snake_case     ) + '\n'     )
      with open(self.merges_file						,      'w'						,      encoding='utf-8'     ) as fp:
        fp.write('\n'.join(__snake_case     )     )







    def       lowercase_	(				self			:  int						,      **__snake_case			:  str     ):
      kwargs.update(self.special_tokens_map     )
      return CTRLTokenizer.from_pretrained(self.tmpdirname						,      **__snake_case     )







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Any     ):
      a						: int					       =					'adapt react readapt apt'
      a						: Any					       =					'adapt react readapt apt'
      return input_text, output_text







    def       lowercase_	(				self			:  Dict     ):
      a						: Dict					       =					CTRLTokenizer(self.vocab_file						,      self.merges_file						,      **self.special_tokens_map     )
      a						: List[str]					       =					'adapt react readapt apt'
      a						: Dict					       =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
      a						: Any					       =					tokenizer.tokenize(__snake_case     )
      self.assertListEqual(__snake_case						,      __snake_case     )

      a						: Dict					       =					tokens + [tokenizer.unk_token]

      a						: Optional[Any]					       =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case     )						,      __snake_case     )