


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()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Union[str, Any]=False			):
 __A  :		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"
  __A  :		int       =			[(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 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					List[Any]=False			):
 for i in range(config.num_hidden_layers			):
  if base_model:
   __A  :		str       =			""""""
  else:
   __A  :		Any       =			"""vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __A  :		Optional[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  :		List[Any]       =			in_proj_weight[
      : config.hidden_size, :
  ]
  __A  :		str       =			in_proj_bias[: config.hidden_size]
  __A  :		Tuple       =			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __A  :		Tuple       =			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __A  :		Union[str, Any]       =			in_proj_weight[
      -config.hidden_size :, :
  ]
  __A  :		Tuple       =			in_proj_bias[-config.hidden_size :]


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		Union[str, Any]       =			["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					int			):
 __A  :		List[str]       =			dct.pop(UpperCamelCase__			)
 __A  :		Tuple       =			val


def 			_lowercase       (				):
 __A  :		Any       =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 __A  :		Optional[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Union[str, Any]=True			):
 __A  :		Any       =			ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  __A  :		Tuple       =			8
 # set labels if required
 if not base_model:
  __A  :		str       =			1000
  __A  :		Any       =			"""huggingface/label-files"""
  __A  :		Optional[int]       =			"""imagenet-1k-id2label.json"""
  __A  :		Dict       =			json.load(open(hf_hub_download(UpperCamelCase__,				UpperCamelCase__,				repo_type='dataset'			),				'r'			)			)
  __A  :		Any       =			{int(UpperCamelCase__			): v for k, v in idalabel.items()}
  __A  :		List[str]       =			idalabel
  __A  :		int       =			{v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  __A  :		Any       =			384
  __A  :		Tuple       =			1536
  __A  :		Union[str, Any]       =			12
  __A  :		Union[str, Any]       =			6

 # load original model from torch hub
 __A  :		Tuple       =			torch.hub.load('facebookresearch/dino:main',				UpperCamelCase__			)
 original_model.eval()

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

 # load HuggingFace model
 if base_model:
  __A  :		Tuple       =			ViTModel(UpperCamelCase__,				add_pooling_layer=UpperCamelCase__			).eval()
 else:
  __A  :		Union[str, Any]       =			ViTForImageClassification(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

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

 if base_model:
  __A  :		str       =			original_model(UpperCamelCase__			)
  assert torch.allclose(UpperCamelCase__,				outputs.last_hidden_state[:, 0, :],				atol=1E-1			)
 else:
  __A  :		int       =			original_model(UpperCamelCase__			)
  assert logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase__,				outputs.logits,				atol=1E-3			)

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      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)
 UpperCAmelCase_ :     Tuple								=      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.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						XLMTokenizer
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A  :		Optional[int]       =			[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  __A  :		int       =			dict(zip(__lowercase      ,      range(len(__lowercase						)						)						)						)
  __A  :		str       =			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  __A  :		Dict       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  __A  :		List[Any]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['merges_file']						)
  with open(self.vocab_file      ,      'w'						) as fp:
   fp.write(json.dumps(__lowercase						)						)
  with open(self.merges_file      ,      'w'						) as fp:
   fp.write('\n'.join(__lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			'lower newer'
  __A  :		int       =			'lower newer'
  return input_text, output_text


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			XLMTokenizer(self.vocab_file      ,      self.merges_file						)

  __A  :		Optional[Any]       =			'lower'
  __A  :		Union[str, Any]       =			['low', 'er</w>']
  __A  :		Any       =			tokenizer.tokenize(__lowercase						)
  self.assertListEqual(__lowercase      ,      __lowercase						)

  __A  :		Optional[int]       =			tokens + ['<unk>']
  __A  :		Any       =			[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)      ,      __lowercase						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			XLMTokenizer.from_pretrained('xlm-mlm-en-2048'						)

  __A  :		Tuple       =			tokenizer.encode('sequence builders'      ,      add_special_tokens=__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.encode('multi-sequence build'      ,      add_special_tokens=__lowercase						)

  __A  :		int       =			tokenizer.build_inputs_with_special_tokens(__lowercase						)
  __A  :		Tuple       =			tokenizer.build_inputs_with_special_tokens(__lowercase      ,      __lowercase						)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]






