




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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	  = logging.get_logger(__name__)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=False)      ->					Dict:
     a     =				[]
     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'''deit.encoder.layer.{i}.layernorm_before.weight'''))
          rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias'''))
          rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight'''))
          rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias'''))
          rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight'''))
          rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias'''))
          rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight'''))
          rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias'''))
          rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight'''))
          rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias'''))

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

     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 "deit" from all keys that start with "deit"
          a     =				[(pair[0], pair[1][4:]) if pair[1].startswith("deit") else pair for pair in rename_keys]
     else:
          # layernorm + classification heads
          rename_keys.extend(
              [
                  ("norm.weight", "deit.layernorm.weight"),
                  ("norm.bias", "deit.layernorm.bias"),
                  ("head.weight", "cls_classifier.weight"),
                  ("head.bias", "cls_classifier.bias"),
                  ("head_dist.weight", "distillation_classifier.weight"),
                  ("head_dist.bias", "distillation_classifier.bias"),
              ])

     return rename_keys

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=False)      ->					Optional[Any]:



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

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Any:
     a     =				dct.pop(A__)
     a     =				val

def  SCREAMING_SNAKE_CASE							(       )      ->					str:
     a     =				"http://images.cocodataset.org/val2017/000000039769.jpg"
     a     =				Image.open(requests.get(A__		, stream=A__).raw)
     return im

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:
     a     =				DeiTConfig()
     # all deit models have fine-tuned heads
     a     =				False
     # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
     a     =				10_00
     a     =				"huggingface/label-files"
     a     =				"imagenet-1k-id2label.json"
     a     =				json.load(open(hf_hub_download(A__		, A__		, repo_type="dataset")		, "r"))
     a     =				{int(A__): v for k, v in idalabel.items()}
     a     =				idalabel
     a     =				{v: k for k, v in idalabel.items()}
     a     =				int(deit_name[-6:-4])
     a     =				int(deit_name[-3:])
     # size of the architecture
     if deit_name[9:].startswith("tiny"):
          a     =				1_92
          a     =				7_68
          a     =				12
          a     =				3
     elif deit_name[9:].startswith("small"):
          a     =				3_84
          a     =				15_36
          a     =				12
          a     =				6
     if deit_name[9:].startswith("base"):
          pass
     elif deit_name[4:].startswith("large"):
          a     =				10_24
          a     =				40_96
          a     =				24
          a     =				16

     # load original model from timm
     a     =				timm.create_model(A__		, pretrained=A__)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     a     =				timm_model.state_dict()
     a     =				create_rename_keys(A__		, A__)
     for src, dest in rename_keys:
          rename_key(A__		, A__		, A__)
     read_in_q_k_v(A__		, A__		, A__)

     # load HuggingFace model
     a     =				DeiTForImageClassificationWithTeacher(A__).eval()
     model.load_state_dict(A__)

     # Check outputs on an image, prepared by DeiTImageProcessor
     a     =				int(
         (2_56 / 2_24) * config.image_size)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
     a     =				DeiTImageProcessor(size=A__		, crop_size=config.image_size)
     a     =				image_processor(images=prepare_img()		, return_tensors="pt")
     a     =				encoding["pixel_values"]
     a     =				model(A__)

     a     =				timm_model(A__)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(A__		, outputs.logits		, atol=1e-3)

     Path(A__).mkdir(exist_ok=A__)
     print(f'''Saving model {deit_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__":
   lowercase__				:      List[Any]	  = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--deit_name",
       default="vit_deit_base_distilled_patch16_224",
       type=str,
       help="Name of the DeiT 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."
   )

   lowercase__				:      Tuple	  = parser.parse_args()
   convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(UpperCamelCase__				)       , """Tatoeba directory does not exist."""				)
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          return TatoebaConverter(save_dir=A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.resolver.convert_models(["heb-eng"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				self.resolver.write_model_card("opus-mt-he-en"  ,		dry_run=A			)
          assert mmeta["long_pair"] == "heb-eng"

