


'''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()
a_  :				Tuple						= logging.get_logger(__name__)





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





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_      =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      ):
 lowerCamelCase_      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_      =  Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_		).raw		)
 return im





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

 # load original model from torch hub
 lowerCamelCase_      =  torch.hub.load("facebookresearch/dino:main"					,				UpperCAmelCase_		)
 original_model.eval()

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

 # Check outputs on an image, prepared by ViTImageProcessor
 lowerCamelCase_      =  ViTImageProcessor()
 lowerCamelCase_      =  image_processor(images=prepare_img()					,				return_tensors="pt"		)
 lowerCamelCase_      =  encoding["pixel_values"]
 lowerCamelCase_      =  model(UpperCAmelCase_		)

 if base_model:
  lowerCamelCase_      =  original_model(UpperCAmelCase_		)
  assert torch.allclose(UpperCAmelCase_					,				outputs.last_hidden_state[:, 0, :]					,				atol=1E-1		)
 else:
  lowerCamelCase_      =  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__":
     a_  :				Optional[Any]						= 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)
     a_  :				Optional[Any]						= 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


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

a_  :				int						= """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_  :				Tuple						= """
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    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))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

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

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

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

   return result



