



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







def 				__lowerCamelCase				(   A__    ,						A__=False    )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase					=  []
   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"
      UpperCamelCase					=  [(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    )       ->	Tuple:






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







def 				__lowerCamelCase				(   A__    )       ->	Union[str, Any]:






   """simple docstring"""
   UpperCamelCase					=  ['head.weight', 'head.bias']
   for k in ignore_keys:
      state_dict.pop(A__    ,						A__    )







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[int]:






   """simple docstring"""
   UpperCamelCase					=  dct.pop(A__    )
   UpperCamelCase					=  val







def 				__lowerCamelCase				(   )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase					=  Image.open(requests.get(A__    ,						stream=A__    ).raw    )
   return im







@torch.no_grad()
def 				__lowerCamelCase				(   A__    ,						A__    ,						A__=True    )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase					=  ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      UpperCamelCase					=  8
   # set labels if required
   if not base_model:
      UpperCamelCase					=  1_000
      UpperCamelCase					=  'huggingface/label-files'
      UpperCamelCase					=  'imagenet-1k-id2label.json'
      UpperCamelCase					=  json.load(open(hf_hub_download(A__    ,						A__    ,						repo_type='dataset'    )    ,						'r'    )    )
      UpperCamelCase					=  {int(A__    ): v for k, v in idalabel.items()}
      UpperCamelCase					=  idalabel
      UpperCamelCase					=  {v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      UpperCamelCase					=  384
      UpperCamelCase					=  1_536
      UpperCamelCase					=  12
      UpperCamelCase					=  6

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

   # load state_dict of original model, remove and rename some keys
   UpperCamelCase					=  original_model.state_dict()
   if base_model:
      remove_classification_head_(A__    )
   UpperCamelCase					=  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:
      UpperCamelCase					=  ViTModel(A__    ,						add_pooling_layer=A__    ).eval()
   else:
      UpperCamelCase					=  ViTForImageClassification(A__    ).eval()
   model.load_state_dict(A__    )

   # Check outputs on an image, prepared by ViTImageProcessor
   UpperCamelCase					=  ViTImageProcessor()
   UpperCamelCase					=  image_processor(images=prepare_img()    ,						return_tensors='pt'    )
   UpperCamelCase					=  encoding['pixel_values']
   UpperCamelCase					=  model(A__    )

   if base_model:
      UpperCamelCase					=  original_model(A__    )
      assert torch.allclose(A__    ,						outputs.last_hidden_state[:, 0, :]    ,						atol=1e-1    )
   else:
      UpperCamelCase					=  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	:       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)
   _lowerCamelCase	:       Optional[int]      	=	parser.parse_args()
   convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






'''simple docstring'''


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowerCamelCase	:       List[str]      	=	"\\n@inproceedings{lin-2004-rouge,\n    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",\n    author = \"Lin, Chin-Yew\",\n    booktitle = \"Text Summarization Branches Out\",\n    month = jul,\n    year = \"2004\",\n    address = \"Barcelona, Spain\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W04-1013\",\n    pages = \"74--81\",\n}\n"

_lowerCamelCase	:       Optional[int]      	=	"\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

_lowerCamelCase	:       str      	=	"\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION		)
class  SCREAMING_SNAKE_CASE (  datasets.Metric		):






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION  ,						citation=_CITATION  ,						inputs_description=_KWARGS_DESCRIPTION  ,						features=datasets.Features(
               {
                   'predictions': datasets.Value('string'  ,						id='sequence' ),
                   'references': datasets.Value('string'  ,						id='sequence' ),
               } )  ,						codebase_urls=['https://github.com/google-research/google-research/tree/master/rouge']  ,						reference_urls=[
               'https://en.wikipedia.org/wiki/ROUGE_(metric)',
               'https://github.com/google-research/google-research/tree/master/rouge',
           ]  ,						)






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					List[str]=None  ,						UpperCamelCase__      :					List[Any]=True  ,						UpperCamelCase__      :					Optional[Any]=False ):


       """simple docstring"""







       if rouge_types is None:
          UpperCamelCase					=  ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

       UpperCamelCase					=  rouge_scorer.RougeScorer(rouge_types=UpperCamelCase__  ,						use_stemmer=UpperCamelCase__ )
       if use_aggregator:
          UpperCamelCase					=  scoring.BootstrapAggregator()
       else:
          UpperCamelCase					=  []

       for ref, pred in zip(UpperCamelCase__  ,						UpperCamelCase__ ):
          UpperCamelCase					=  scorer.score(UpperCamelCase__  ,						UpperCamelCase__ )
          if use_aggregator:
             aggregator.add_scores(UpperCamelCase__ )
          else:
             scores.append(UpperCamelCase__ )

       if use_aggregator:
          UpperCamelCase					=  aggregator.aggregate()
       else:
          UpperCamelCase					=  {}
          for key in scores[0]:
             UpperCamelCase					=  [score[key] for score in scores]

       return result

