


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__							:						Tuple							     =   """\
@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\",
}
"""

lowerCamelCase__							:						List[str]							     =   """\
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
"""

lowerCamelCase__							:						Optional[int]							     =   """
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 		__magic_name__						(datasets.Metric				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Union[str, Any]     ,		_a:List[Any]     ,		_a:str=None     ,		_a:Optional[int]=True     ,		_a:Optional[Any]=False   ):
       if rouge_types is None:
         snake_case__						= ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

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

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

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

       return result





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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__							:						int							     =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase=False				)    ->				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"""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'''),
            ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
            ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
        ]				)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    snake_case__						= [(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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=False				)    ->				Dict:
  for i in range(config.num_hidden_layers				):
    if base_model:
      snake_case__						= ''''''
    else:
      snake_case__						= '''vit.'''
    # 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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Union[str, Any]:
  snake_case__						= dct.pop(__lowerCAmelCase				)
  snake_case__						= val







def SCREAMING_SNAKE_CASE	(			)    ->				str:
  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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Dict:
  snake_case__						= ViTConfig()
  snake_case__						= False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    snake_case__						= True
    snake_case__						= int(vit_name[-12:-10]				)
    snake_case__						= int(vit_name[-9:-6]				)
  else:
    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(vit_name[-6:-4]				)
    snake_case__						= int(vit_name[-3:]				)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith('''tiny'''				):
      snake_case__						= 192
      snake_case__						= 768
      snake_case__						= 12
      snake_case__						= 3
    elif vit_name[9:].startswith('''small'''				):
      snake_case__						= 384
      snake_case__						= 1536
      snake_case__						= 12
      snake_case__						= 6
    else:
      pass
  else:
    if vit_name[4:].startswith('''small'''				):
      snake_case__						= 768
      snake_case__						= 2304
      snake_case__						= 8
      snake_case__						= 8
    elif vit_name[4:].startswith('''base'''				):
      pass
    elif vit_name[4:].startswith('''large'''				):
      snake_case__						= 1024
      snake_case__						= 4096
      snake_case__						= 24
      snake_case__						= 16
    elif vit_name[4:].startswith('''huge'''				):
      snake_case__						= 1280
      snake_case__						= 5120
      snake_case__						= 32
      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()
  if base_model:
    remove_classification_head_(__lowerCAmelCase				)
  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
  if vit_name[-5:] == "in21k":
    snake_case__						= ViTModel(__lowerCAmelCase				).eval()
  else:
    snake_case__						= ViTForImageClassification(__lowerCAmelCase				).eval()
  model.load_state_dict(__lowerCAmelCase				)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    snake_case__						= DeiTImageProcessor(size=config.image_size				)
  else:
    snake_case__						= ViTImageProcessor(size=config.image_size				)
  snake_case__						= image_processor(images=prepare_img()							,   return_tensors='''pt'''				)
  snake_case__						= encoding['''pixel_values''']
  snake_case__						= model(__lowerCAmelCase				)

  if base_model:
    snake_case__						= timm_model.forward_features(__lowerCAmelCase				)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(__lowerCAmelCase							,   outputs.pooler_output							,   atol=1e-3				)
  else:
    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 {vit_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__":
   lowerCamelCase__							:						Optional[Any]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--vit_name""",
       default="""vit_base_patch16_224""",
       type=str,
       help="""Name of the ViT 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."""
   )

   lowerCamelCase__							:						str							     =   parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

