import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


UpperCamelCase__    							= '\\n@inproceedings{popovic-2015-chrf,\n    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",\n    month = sep,\n    year = "2015",\n    address = "Lisbon, Portugal",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W15-3049",\n    doi = "10.18653/v1/W15-3049",\n    pages = "392--395",\n}\n@inproceedings{popovic-2017-chrf,\n    title = "chr{F}++: words helping character n-grams",\n    author = "Popovi{\'c}, Maja",\n    booktitle = "Proceedings of the Second Conference on Machine Translation",\n    month = sep,\n    year = "2017",\n    address = "Copenhagen, Denmark",\n    publisher = "Association for Computational Linguistics",\n    url = "https://aclanthology.org/W17-4770",\n    doi = "10.18653/v1/W17-4770",\n    pages = "612--618",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

UpperCamelCase__    							= '\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n'

UpperCamelCase__    							= '\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]\n        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]\n        >>> chrf = datasets.load_metric("chrf")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n'





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


			def       lowercase_				(self					:						int		)						->    List[Any]:
									"""simple docstring"""



									if version.parse(scb.__version__		) < version.parse("1.4.12"		):
															raise ImportWarning(
															    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
															    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."		)
									return datasets.MetricInfo(
									    description=_DESCRIPTION	,  citation=_CITATION	,  homepage="https://github.com/mjpost/sacreBLEU#chrf--chrf"	,  inputs_description=_KWARGS_DESCRIPTION	,  features=datasets.Features(
									        {
									            "predictions": datasets.Value("string"	,  id="sequence"		),
									            "references": datasets.Sequence(datasets.Value("string"	,  id="sequence"		)	,  id="references"		),
									        }		)	,  codebase_urls=["https://github.com/mjpost/sacreBLEU#chrf--chrf"]	,  reference_urls=[
									        "https://github.com/m-popovic/chrF",
									    ]	,  )





			def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						int = CHRF.CHAR_ORDER	,  __UpperCAmelCase					:						int = CHRF.WORD_ORDER	,  __UpperCAmelCase					:						int = CHRF.BETA	,  __UpperCAmelCase					:						bool = False	,  __UpperCAmelCase					:						bool = False	,  __UpperCAmelCase					:						bool = False	,  )						->    List[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  len(references[0]		)
									if any(len(__UpperCAmelCase		) != references_per_prediction for refs in references		):
															raise ValueError("Sacrebleu requires the same number of references for each prediction"		)
									UpperCAmelCase__								=  [[refs[i] for refs in references] for i in range(__UpperCAmelCase		)]

									UpperCAmelCase__								=  CHRF(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
									UpperCAmelCase__								=  sb_chrf.corpus_score(__UpperCAmelCase	,  __UpperCAmelCase		)

									return {
									    "score": output.score,
									    "char_order": output.char_order,
									    "word_order": output.word_order,
									    "beta": output.beta,
									}




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        lowerCAmelCase_			(					__A,  __A=False							)					->    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"""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"
            UpperCAmelCase__								=  [(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        lowerCAmelCase_			(					__A,  __A,  __A=False							)					->    Tuple:







      '''simple docstring'''


      for i in range(config.num_hidden_layers							):
            if base_model:
                  UpperCAmelCase__								=  ""
            else:
                  UpperCAmelCase__								=  "deit."
            # 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,  __A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  dct.pop(__A							)
      UpperCAmelCase__								=  val




def        lowerCAmelCase_			(					)					->    Dict:







      '''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							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  DeiTConfig()
      # all deit models have fine-tuned heads
      UpperCAmelCase__								=  False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      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()}
      UpperCAmelCase__								=  int(deit_name[-6:-4]							)
      UpperCAmelCase__								=  int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith("tiny"							):
            UpperCAmelCase__								=  192
            UpperCAmelCase__								=  768
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  3
      elif deit_name[9:].startswith("small"							):
            UpperCAmelCase__								=  384
            UpperCAmelCase__								=  1_536
            UpperCAmelCase__								=  12
            UpperCAmelCase__								=  6
      if deit_name[9:].startswith("base"							):
            pass
      elif deit_name[4:].startswith("large"							):
            UpperCAmelCase__								=  1_024
            UpperCAmelCase__								=  4_096
            UpperCAmelCase__								=  24
            UpperCAmelCase__								=  16

      # load original model from timm
      UpperCAmelCase__								=  timm.create_model(__A,  pretrained=__A							)
      timm_model.eval()

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

      # load HuggingFace model
      UpperCAmelCase__								=  DeiTForImageClassificationWithTeacher(__A							).eval()
      model.load_state_dict(__A							)

      # Check outputs on an image, prepared by DeiTImageProcessor
      UpperCAmelCase__								=  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
      UpperCAmelCase__								=  DeiTImageProcessor(size=__A,  crop_size=config.image_size							)
      UpperCAmelCase__								=  image_processor(images=prepare_img(),  return_tensors="pt"							)
      UpperCAmelCase__								=  encoding["pixel_values"]
      UpperCAmelCase__								=  model(__A							)

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

      Path(__A							).mkdir(exist_ok=__A							)
      print(f"""Saving model {deit_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__":
      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)



