
"""simple docstring"""



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


UpperCAmelCase__          = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

UpperCAmelCase__          = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

UpperCAmelCase__          = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'

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







     def      lowercase	(							self			:			Any			):
       return datasets.MetricInfo(
           description=_DESCRIPTION    ,      citation=_CITATION    ,      inputs_description=_KWARGS_DESCRIPTION    ,      features=datasets.Features(
               {
                   '''predictions''': datasets.Sequence(datasets.Value('''string'''    ,      id='''token'''			)    ,      id='''sequence'''			),
                   '''references''': datasets.Sequence(
                       datasets.Sequence(datasets.Value('''string'''    ,      id='''token'''			)    ,      id='''sequence'''			)    ,      id='''references'''			),
               }			)    ,      codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']    ,      reference_urls=[
               '''https://en.wikipedia.org/wiki/BLEU''',
               '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
           ]    ,      )







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Tuple=4    ,      _lowerCamelCase			:			int=False			):
       _snake_case					     =       compute_bleu(
           reference_corpus=_lowerCamelCase    ,      translation_corpus=_lowerCamelCase    ,      max_order=_lowerCamelCase    ,      smooth=_lowerCamelCase			)
       ((_snake_case)     ,					(_snake_case)     ,					(_snake_case)     ,					(_snake_case)     ,					(_snake_case)     ,					(_snake_case))					     =       score
       return {
           "bleu": bleu,
           "precisions": precisions,
           "brevity_penalty": bp,
           "length_ratio": ratio,
           "translation_length": translation_length,
           "reference_length": reference_length,
       }





"""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()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=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      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  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      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _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      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					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					     =       10_00
  _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					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _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(
      (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
  _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__":
 UpperCAmelCase__          = 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.'
 )

 UpperCAmelCase__          = parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



