
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__		:     int     			=      logging.get_logger(__name__)






def   lowerCamelCase__							(					a		,		a=False      )      ->   Union[str, Any]:
   _A:  List[Any]					=       []
   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:  Tuple					=       [(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      )      ->   List[str]:



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






def   lowerCamelCase__							(					a      )      ->   Optional[Any]:
   _A:  Any					=       ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(__lowerCAmelCase		,		__lowerCAmelCase      )






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   int:
   _A:  List[Any]					=       dct.pop(__lowerCAmelCase      )
   _A:  Optional[Any]					=       val






def   lowerCamelCase__							(					)      ->   str:
   _A:  Union[str, Any]					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   _A:  Dict					=       Image.open(requests.get(__lowerCAmelCase		,		stream=__lowerCAmelCase      ).raw      )
   return im






@torch.no_grad()
def   lowerCamelCase__							(					a		,		a		,		a=True      )      ->   List[str]:
   _A:  List[str]					=       ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      _A:  Tuple					=       8
   # set labels if required
   if not base_model:
      _A:  int					=       10_00
      _A:  Any					=       '''huggingface/label-files'''
      _A:  Optional[int]					=       '''imagenet-1k-id2label.json'''
      _A:  Optional[int]					=       json.load(open(hf_hub_download(__lowerCAmelCase		,		__lowerCAmelCase		,		repo_type='''dataset'''      )		,		'''r'''      )      )
      _A:  Tuple					=       {int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
      _A:  int					=       idalabel
      _A:  Any					=       {v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      _A:  Any					=       3_84
      _A:  Any					=       15_36
      _A:  Dict					=       12
      _A:  Any					=       6

   # load original model from torch hub
   _A:  Optional[Any]					=       torch.hub.load('''facebookresearch/dino:main'''		,		__lowerCAmelCase      )
   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_(__lowerCAmelCase      )
   _A:  str					=       create_rename_keys(__lowerCAmelCase		,		base_model=__lowerCAmelCase      )
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase      )
   read_in_q_k_v(__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase      )

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

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

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

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


if __name__ == "__main__":
   UpperCAmelCase__		:     int     			=      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__		:     Union[str, Any]     			=      parser.parse_args()
   convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)











import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      TransfoXLTokenizer
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase									=			[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							_UpperCAmelCase									=			os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES['vocab_file']    )
							with open(self.vocab_file       ,    'w'       ,    encoding='utf-8'    ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

	def        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<unk> UNwanted , running'
							_UpperCAmelCase									=			'<unk> unwanted, running'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(vocab_file=self.vocab_file       ,    lower_case=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('<unk> UNwanted , running'    )
							self.assertListEqual(UpperCAmelCase       ,    ['<unk>', 'unwanted', ',', 'running']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [0, 4, 8, 7]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['hello', '!', 'how', 'are', 'you', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )
							_UpperCAmelCase									=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							_UpperCAmelCase									=			[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(UpperCAmelCase    )       ,    UpperCAmelCase    )

							self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			len(UpperCAmelCase    )

							tokenizer.add_tokens(['new1', 'new2']    )
							tokenizer.move_added_token('new1'       ,    1    )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(UpperCAmelCase    )       ,    original_len + 2    )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'    )       ,    [1]    )
							self.assertEqual(tokenizer.decode([1]    )       ,    'new1'    )
