





'''simple docstring'''







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       						=			logging.get_logger(__name__)






def    __UpperCamelCase	(	a    : Optional[Any]					,   a    : str=False     ) ->Optional[int]:
 snake_case				=			[]
 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"
  snake_case				=			[(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    __UpperCamelCase	(	a    : int					,   a    : List[str]					,   a    : Optional[int]=False     ) ->Optional[Any]:





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






def    __UpperCamelCase	(	a    : List[Any]					,   a    : Any					,   a    : List[str]     ) ->List[str]:
 snake_case				=			dct.pop(_lowerCamelCase     )
 snake_case				=			val






def    __UpperCamelCase	(	) ->Optional[Any]:
 snake_case				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
 snake_case				=			Image.open(requests.get(_lowerCamelCase					,   stream=_lowerCamelCase     ).raw     )
 return im






@torch.no_grad()
def    __UpperCamelCase	(	a    : Any					,   a    : Dict     ) ->str:
 snake_case				=			DeiTConfig()
 # all deit models have fine-tuned heads
 snake_case				=			False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 snake_case				=			1000
 snake_case				=			"huggingface/label-files"
 snake_case				=			"imagenet-1k-id2label.json"
 snake_case				=			json.load(open(hf_hub_download(_lowerCamelCase					,   _lowerCamelCase					,   repo_type='''dataset'''     )					,   '''r'''     )     )
 snake_case				=			{int(_lowerCamelCase     ): v for k, v in idalabel.items()}
 snake_case				=			idalabel
 snake_case				=			{v: k for k, v in idalabel.items()}
 snake_case				=			int(deit_name[-6:-4]     )
 snake_case				=			int(deit_name[-3:]     )
 # size of the architecture
 if deit_name[9:].startswith('''tiny'''     ):
  snake_case				=			192
  snake_case				=			768
  snake_case				=			12
  snake_case				=			3
 elif deit_name[9:].startswith('''small'''     ):
  snake_case				=			384
  snake_case				=			1536
  snake_case				=			12
  snake_case				=			6
 if deit_name[9:].startswith('''base'''     ):
  pass
 elif deit_name[4:].startswith('''large'''     ):
  snake_case				=			1024
  snake_case				=			4096
  snake_case				=			24
  snake_case				=			16

 # load original model from timm
 snake_case				=			timm.create_model(_lowerCamelCase					,   pretrained=_lowerCamelCase     )
 timm_model.eval()

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

 # load HuggingFace model
 snake_case				=			DeiTForImageClassificationWithTeacher(_lowerCamelCase     ).eval()
 model.load_state_dict(_lowerCamelCase     )

 # Check outputs on an image, prepared by DeiTImageProcessor
 snake_case				=			int(
     (256 / 224) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
 snake_case				=			DeiTImageProcessor(size=_lowerCamelCase					,   crop_size=config.image_size     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     )
 snake_case				=			encoding["pixel_values"]
 snake_case				=			model(_lowerCamelCase     )

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

 Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
 print(f"""Saving model {deit_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__":
   _lowercase       						=			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       						=			parser.parse_args()
   convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)








'''simple docstring'''







from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 			_lowercase						(       yaml.SafeLoader					):







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      snake_case				=			[self.constructed_objects[key_node] for key_node, _ in node.value]
      snake_case				=			[tuple(A__    ) if isinstance(A__    ,   A__    ) else key for key in keys]
      snake_case				=			Counter(A__    )
      snake_case				=			[key for key in counter if counter[key] > 1]
      if duplicate_keys:
       raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=False    ) ->	List[Any]:
      snake_case				=			super().construct_mapping(A__    ,   deep=A__    )
      self._check_no_duplicates_on_constructed_node(A__    )
      return mapping




def    __UpperCamelCase	(	a    : str     ) ->Tuple[Optional[str], str]:
 snake_case				=			list(readme_content.splitlines()     )
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  snake_case				=			full_content[1:].index('''---'''     ) + 1
  snake_case				=			'''\n'''.join(full_content[1:sep_idx]     )
  return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

 return None, "\n".join(a     )
class 			_lowercase						(       __a					):
     # class attributes
     _UpperCAmelCase										=					{'''train_eval_index'''}  # train-eval-index in the YAML metadata







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
       snake_case				,		snake_case				=			_split_yaml_from_readme(readme_file.read()    )
      if yaml_string is not None:
       return cls.from_yaml_string(A__    )
      else:
       return cls()







     def     UpperCamelCase			( self    ,   A__    ) ->	str:
      if path.exists():
       with open(A__    ,   encoding='''utf-8'''    ) as readme_file:
        snake_case				=			readme_file.read()
      else:
       snake_case				=			None
      snake_case				=			self._to_readme(A__    )
      with open(A__    ,   '''w'''    ,   encoding='''utf-8'''    ) as readme_file:
       readme_file.write(A__    )







     def     UpperCamelCase			( self    ,   A__ = None    ) ->	str:
      if readme_content is not None:
       snake_case				,		snake_case				=			_split_yaml_from_readme(A__    )
       snake_case				=			'''---\n''' + self.to_yaml_string() + '''---\n''' + content
      else:
       snake_case				=			'''---\n''' + self.to_yaml_string() + '''---\n'''
      return full_content







     @classmethod
     def     UpperCamelCase			( cls    ,   A__    ) ->	"DatasetMetadata":
      snake_case				=			yaml.load(A__    ,   Loader=_NoDuplicateSafeLoader    ) or {}

      # Convert the YAML keys to DatasetMetadata fields
      snake_case				=			{
          (key.replace('''-'''    ,   '''_'''    ) if key.replace('''-'''    ,   '''_'''    ) in cls._FIELDS_WITH_DASHES else key): value
          for key, value in metadata_dict.items()
      }
      return cls(**A__    )







     def     UpperCamelCase			( self    ) ->	str:
      return yaml.safe_dump(
          {
              (key.replace('''_'''    ,   '''-'''    ) if key in self._FIELDS_WITH_DASHES else key): value
              for key, value in self.items()
          }    ,   sort_keys=A__    ,   allow_unicode=A__    ,   encoding='''utf-8'''    ,   ).decode('''utf-8'''    )




_lowercase       						=			{
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _lowercase       						=			ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
   ap.add_argument('readme_filepath')
   _lowercase       						=			ap.parse_args()

   _lowercase       						=			Path(args.readme_filepath)
   _lowercase       						=			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)

