



'''simple docstring'''


import importlib
from typing import List, Optional, Union

from seqeval.metrics import accuracy_score, classification_report

import datasets


lowercase											=      '''\
@inproceedings{ramshaw-marcus-1995-text,
    title = "Text Chunking using Transformation-Based Learning",
    author = "Ramshaw, Lance  and
      Marcus, Mitch",
    booktitle = "Third Workshop on Very Large Corpora",
    year = "1995",
    url = "https://www.aclweb.org/anthology/W95-0107",
}
@misc{seqeval,
  title={{seqeval}: A Python framework for sequence labeling evaluation},
  url={https://github.com/chakki-works/seqeval},
  note={Software available from https://github.com/chakki-works/seqeval},
  author={Hiroki Nakayama},
  year={2018},
}
'''

lowercase											=      '''\
seqeval is a Python framework for sequence labeling evaluation.
seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.

This is well-tested by using the Perl script conlleval, which can be used for
measuring the performance of a system that has processed the CoNLL-2000 shared task data.

seqeval supports following formats:
IOB1
IOB2
IOE1
IOE2
IOBES

See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
'''

lowercase											=      '''
Produces labelling scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
    references: List of List of reference labels (Ground truth (correct) target values)
    suffix: True if the IOB prefix is after type, False otherwise. default: False
    scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
        default: None
    mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
        If you want to only count exact matches, pass mode="strict". default: None.
    sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
    zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
        "warn". "warn" acts as 0, but the warning is raised.

Returns:
    \'scores\': dict. Summary of the scores for overall and per type
        Overall:
            \'accuracy\': accuracy,
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure,
        Per type:
            \'precision\': precision,
            \'recall\': recall,
            \'f1\': F1 score, also known as balanced F-score or F-measure
Examples:

    >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
    >>> seqeval = datasets.load_metric("seqeval")
    >>> results = seqeval.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
    >>> print(results["overall_f1"])
    0.5
    >>> print(results["PER"]["f1"])
    1.0
'''







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


 '''simple docstring'''




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      return datasets.MetricInfo(
          description=_DESCRIPTION				,	citation=_CITATION				,	homepage="https://github.com/chakki-works/seqeval"				,	inputs_description=_KWARGS_DESCRIPTION				,	features=datasets.Features(
              {
                  "predictions": datasets.Sequence(datasets.Value("string"				,	id="label")				,	id="sequence"),
                  "references": datasets.Sequence(datasets.Value("string"				,	id="label")				,	id="sequence"),
              })				,	codebase_urls=["https://github.com/chakki-works/seqeval"]				,	reference_urls=["https://github.com/chakki-works/seqeval"]				,	)




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_ = False				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = None				,	lowerCAmelCase_ = "warn"				,	)			->   Dict:



      """simple docstring"""







      if scheme is not None:
           try:
                a_									=importlib.import_module("seqeval.scheme")
                a_									=getattr(lowerCAmelCase_				,	lowerCAmelCase_)
           except AttributeError:
                raise ValueError(f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}""")
      a_									=classification_report(
          y_true=lowerCAmelCase_				,	y_pred=lowerCAmelCase_				,	suffix=lowerCAmelCase_				,	output_dict=lowerCAmelCase_				,	scheme=lowerCAmelCase_				,	mode=lowerCAmelCase_				,	sample_weight=lowerCAmelCase_				,	zero_division=lowerCAmelCase_				,	)
      report.pop("macro avg")
      report.pop("weighted avg")
      a_									=report.pop("micro avg")

      a_									={
          type_name: {
              "precision": score["precision"],
              "recall": score["recall"],
              "f1": score["f1-score"],
              "number": score["support"],
          }
          for type_name, score in report.items()
      }
      a_									=overall_score["precision"]
      a_									=overall_score["recall"]
      a_									=overall_score["f1-score"]
      a_									=accuracy_score(y_true=lowerCAmelCase_				,	y_pred=lowerCAmelCase_)

      return scores







'''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 ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase											=      logging.get_logger(__name__)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=ConvNextConfig()

     if "tiny" in checkpoint_url:
          a_									=[3, 3, 9, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "small" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "base" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
     if "large" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
     if "xlarge" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

     if "1k" in checkpoint_url:
          a_									=1_0_0_0
          a_									="imagenet-1k-id2label.json"
          a_									=(1, 1_0_0_0)
     else:
          a_									=2_1_8_4_1
          a_									="imagenet-22k-id2label.json"
          a_									=(1, 2_1_8_4_1)

     a_									="huggingface/label-files"
     a_									=num_labels
     a_									=json.load(open(hf_hub_download(lowercase__							,						lowercase__							,						repo_type="dataset"							)							,						"r"							)							)
     a_									={int(lowercase__							): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9_2_0_5]
          del idalabel[1_5_0_2_7]
     a_									=idalabel
     a_									={v: k for k, v in idalabel.items()}
     a_									=hidden_sizes
     a_									=depths

     return config, expected_shape






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if "downsample_layers.0.0" in name:
          a_									=name.replace("downsample_layers.0.0"							,						"embeddings.patch_embeddings"							)
     if "downsample_layers.0.1" in name:
          a_									=name.replace("downsample_layers.0.1"							,						"embeddings.norm"							)  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          a_									=name.replace("downsample_layers.1.0"							,						"stages.1.downsampling_layer.0"							)
     if "downsample_layers.1.1" in name:
          a_									=name.replace("downsample_layers.1.1"							,						"stages.1.downsampling_layer.1"							)
     if "downsample_layers.2.0" in name:
          a_									=name.replace("downsample_layers.2.0"							,						"stages.2.downsampling_layer.0"							)
     if "downsample_layers.2.1" in name:
          a_									=name.replace("downsample_layers.2.1"							,						"stages.2.downsampling_layer.1"							)
     if "downsample_layers.3.0" in name:
          a_									=name.replace("downsample_layers.3.0"							,						"stages.3.downsampling_layer.0"							)
     if "downsample_layers.3.1" in name:
          a_									=name.replace("downsample_layers.3.1"							,						"stages.3.downsampling_layer.1"							)
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          a_									=name[: len("stages.0"							)] + ".layers" + name[len("stages.0"							) :]
     if "stages" in name:
          a_									=name.replace("stages"							,						"encoder.stages"							)
     if "norm" in name:
          a_									=name.replace("norm"							,						"layernorm"							)
     if "gamma" in name:
          a_									=name.replace("gamma"							,						"layer_scale_parameter"							)
     if "head" in name:
          a_									=name.replace("head"							,						"classifier"							)

     return name






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									="http://images.cocodataset.org/val2017/000000039769.jpg"
     a_									=Image.open(requests.get(lowercase__							,						stream=lowercase__							).raw							)
     return im






@torch.no_grad()
def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_		,    a_									=get_convnext_config(lowercase__							)
     # load original state_dict from URL
     a_									=torch.hub.load_state_dict_from_url(lowercase__							)["model"]
     # rename keys
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          a_									=val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          if not key.startswith("classifier"							):
               a_									="convnext." + key
          a_									=val

     # load HuggingFace model
     a_									=ConvNextForImageClassification(lowercase__							)
     model.load_state_dict(lowercase__							)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     a_									=2_2_4 if "224" in checkpoint_url else 3_8_4
     a_									=ConvNextImageProcessor(size=lowercase__							)
     a_									=image_processor(images=prepare_img()							,						return_tensors="pt"							).pixel_values

     a_									=model(lowercase__							).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          a_									=torch.tensor([-0.1210, -0.6605, 0.1918]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          a_									=torch.tensor([-0.4473, -0.1847, -0.6365]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          a_									=torch.tensor([0.4525, 0.7539, 0.0308]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          a_									=torch.tensor([0.3561, 0.6350, -0.0384]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          a_									=torch.tensor([0.4174, -0.0989, 0.1489]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          a_									=torch.tensor([0.2513, -0.1349, -0.1613]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          a_									=torch.tensor([1.2980, 0.3631, -0.1198]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          a_									=torch.tensor([1.2963, 0.1227, 0.1723]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          a_									=torch.tensor([1.7956, 0.8390, 0.2820]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          a_									=torch.tensor([-0.2822, -0.0502, -0.0878]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          a_									=torch.tensor([-0.5672, -0.0730, -0.4348]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          a_									=torch.tensor([0.2681, 0.2365, 0.6246]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          a_									=torch.tensor([-0.2642, 0.3931, 0.5116]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          a_									=torch.tensor([-0.6677, -0.1873, -0.8379]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          a_									=torch.tensor([-0.7749, -0.2967, -0.6444]							)
     else:
          raise ValueError(F"""Unknown URL: {checkpoint_url}"""							)

     assert torch.allclose(logits[0, :3]							,						lowercase__							,						atol=1E-3							)
     assert logits.shape == expected_shape

     Path(lowercase__							).mkdir(exist_ok=lowercase__							)
     print(F"""Saving model to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(lowercase__							)
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(lowercase__							)

     print("Pushing model to the hub..."							)
     a_									="convnext"
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(lowercase__							,						lowercase__							)							,						organization="nielsr"							,						commit_message="Add model"							,						)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
      type=str,
      help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the output PyTorch model directory.''',
  )

  lowercase											=      parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


