


'''simple docstring'''
import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	List[str]          =					ConvNextVaConfig()

       if "atto" in checkpoint_url:
              snake_case__					:	Any          =					[2, 2, 6, 2]
              snake_case__					:	str          =					[40, 80, 160, 320]
       if "femto" in checkpoint_url:
              snake_case__					:	Optional[Any]          =					[2, 2, 6, 2]
              snake_case__					:	str          =					[48, 96, 192, 384]
       if "pico" in checkpoint_url:
              snake_case__					:	Any          =					[2, 2, 6, 2]
              snake_case__					:	List[str]          =					[64, 128, 256, 512]
       if "nano" in checkpoint_url:
              snake_case__					:	Optional[Any]          =					[2, 2, 8, 2]
              snake_case__					:	Union[str, Any]          =					[80, 160, 320, 640]
       if "tiny" in checkpoint_url:
              snake_case__					:	List[str]          =					[3, 3, 9, 3]
              snake_case__					:	Tuple          =					[96, 192, 384, 768]
       if "base" in checkpoint_url:
              snake_case__					:	List[Any]          =					[3, 3, 27, 3]
              snake_case__					:	Tuple          =					[128, 256, 512, 1024]
       if "large" in checkpoint_url:
              snake_case__					:	str          =					[3, 3, 27, 3]
              snake_case__					:	int          =					[192, 384, 768, 1536]
       if "huge" in checkpoint_url:
              snake_case__					:	Dict          =					[3, 3, 27, 3]
              snake_case__					:	Optional[int]          =					[352, 704, 1408, 2816]

       snake_case__					:	Optional[Any]          =					1000
       snake_case__					:	Tuple          =					"""imagenet-1k-id2label.json"""
       snake_case__					:	Tuple          =					(1, 1000)

       snake_case__					:	List[str]          =					"""huggingface/label-files"""
       snake_case__					:	List[Any]          =					num_labels
       snake_case__					:	List[Any]          =					json.load(open(hf_hub_download(lowercase_    ,       lowercase_    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
       snake_case__					:	str          =					{int(lowercase_     ): v for k, v in idalabel.items()}

       snake_case__					:	Optional[int]          =					idalabel
       snake_case__					:	int          =					{v: k for k, v in idalabel.items()}
       snake_case__					:	Dict          =					hidden_sizes
       snake_case__					:	Optional[Any]          =					depths

       return config, expected_shape



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if "downsample_layers.0.0" in name:
              snake_case__					:	Optional[Any]          =					name.replace("""downsample_layers.0.0"""    ,       """embeddings.patch_embeddings"""     )
       if "downsample_layers.0.1" in name:
              snake_case__					:	List[str]          =					name.replace("""downsample_layers.0.1"""    ,       """embeddings.norm"""     )  # we rename to layernorm later on
       if "downsample_layers.1.0" in name:
              snake_case__					:	Tuple          =					name.replace("""downsample_layers.1.0"""    ,       """stages.1.downsampling_layer.0"""     )
       if "downsample_layers.1.1" in name:
              snake_case__					:	List[Any]          =					name.replace("""downsample_layers.1.1"""    ,       """stages.1.downsampling_layer.1"""     )
       if "downsample_layers.2.0" in name:
              snake_case__					:	List[str]          =					name.replace("""downsample_layers.2.0"""    ,       """stages.2.downsampling_layer.0"""     )
       if "downsample_layers.2.1" in name:
              snake_case__					:	Any          =					name.replace("""downsample_layers.2.1"""    ,       """stages.2.downsampling_layer.1"""     )
       if "downsample_layers.3.0" in name:
              snake_case__					:	List[str]          =					name.replace("""downsample_layers.3.0"""    ,       """stages.3.downsampling_layer.0"""     )
       if "downsample_layers.3.1" in name:
              snake_case__					:	Tuple          =					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.
              snake_case__					:	Any          =					name[: len("""stages.0"""     )] + """.layers""" + name[len("""stages.0"""     ) :]
       if "gamma" in name:
              snake_case__					:	Optional[int]          =					name.replace("""gamma"""    ,       """weight"""     )
       if "beta" in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""beta"""    ,       """bias"""     )
       if "stages" in name:
              snake_case__					:	int          =					name.replace("""stages"""    ,       """encoder.stages"""     )
       if "norm" in name:
              snake_case__					:	int          =					name.replace("""norm"""    ,       """layernorm"""     )
       if "head" in name:
              snake_case__					:	Optional[Any]          =					name.replace("""head"""    ,       """classifier"""     )

       return name



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	str          =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       snake_case__					:	Any          =					Image.open(requests.get(lowercase_    ,       stream=lowercase_     ).raw     )
       return im



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if "224" in checkpoint_url:
              snake_case__					:	int          =					224
              snake_case__					:	str          =					224 / 256
       elif "384" in checkpoint_url:
              snake_case__					:	Tuple          =					384
              snake_case__					:	int          =					None
       else:
              snake_case__					:	List[Any]          =					512
              snake_case__					:	str          =					None

       return ConvNextImageProcessor(
           size=lowercase_    ,       crop_pct=lowercase_    ,       image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]    ,       image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    ,       resample=PILImageResampling.BICUBIC    ,       )



@torch.no_grad()
def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       print("""Downloading original model from checkpoint..."""     )
       # define ConvNeXTV2 configuration based on URL
       snake_case__       ,   snake_case__					:	Tuple          =					get_convnextva_config(lowercase_     )
       # load original state_dict from URL
       snake_case__					:	Dict          =					torch.hub.load_state_dict_from_url(lowercase_     )["""model"""]

       print("""Converting model parameters..."""     )
       # rename keys
       for key in state_dict.copy().keys():
              snake_case__					:	Any          =					state_dict.pop(lowercase_     )
              snake_case__					:	List[str]          =					val
       # add prefix to all keys expect classifier head
       for key in state_dict.copy().keys():
              snake_case__					:	List[Any]          =					state_dict.pop(lowercase_     )
              if not key.startswith("""classifier"""     ):
                     snake_case__					:	List[Any]          =					"""convnextv2.""" + key
              snake_case__					:	Union[str, Any]          =					val

       # load HuggingFace model
       snake_case__					:	List[str]          =					ConvNextVaForImageClassification(lowercase_     )
       model.load_state_dict(lowercase_     )
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       snake_case__					:	Dict          =					convert_preprocessor(lowercase_     )
       snake_case__					:	int          =					preprocessor(images=prepare_img()    ,       return_tensors="""pt"""     )
       snake_case__					:	Optional[int]          =					model(**lowercase_     ).logits

       # note: the logits below were obtained without center cropping
       if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
              snake_case__					:	Optional[Any]          =					torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              snake_case__					:	List[str]          =					torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              snake_case__					:	Union[str, Any]          =					torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              snake_case__					:	Any          =					torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              snake_case__					:	Any          =					torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              snake_case__					:	Union[str, Any]          =					torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              snake_case__					:	List[Any]          =					torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              snake_case__					:	Union[str, Any]          =					torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              snake_case__					:	str          =					torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              snake_case__					:	Union[str, Any]          =					torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              snake_case__					:	int          =					torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              snake_case__					:	List[str]          =					torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              snake_case__					:	Optional[Any]          =					torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              snake_case__					:	Optional[int]          =					torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              snake_case__					:	Dict          =					torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              snake_case__					:	List[str]          =					torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              snake_case__					:	Optional[Any]          =					torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]     )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              snake_case__					:	Optional[Any]          =					torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]     )
       else:
              raise ValueError(f"""Unknown URL: {checkpoint_url}"""     )

       assert torch.allclose(logits[0, :5]    ,       lowercase_    ,       atol=1E-3     )
       assert logits.shape == expected_shape
       print("""Model outputs match the original results!"""     )

       if save_model:
              print("""Saving model to local..."""     )
              # Create folder to save model
              if not os.path.isdir(lowercase_     ):
                     os.mkdir(lowercase_     )

              model.save_pretrained(lowercase_     )
              preprocessor.save_pretrained(lowercase_     )

       snake_case__					:	Tuple          =					"""convnextv2"""
       if "atto" in checkpoint_url:
              model_name += "-atto"
       if "femto" in checkpoint_url:
              model_name += "-femto"
       if "pico" in checkpoint_url:
              model_name += "-pico"
       if "nano" in checkpoint_url:
              model_name += "-nano"
       elif "tiny" in checkpoint_url:
              model_name += "-tiny"
       elif "base" in checkpoint_url:
              model_name += "-base"
       elif "large" in checkpoint_url:
              model_name += "-large"
       elif "huge" in checkpoint_url:
              model_name += "-huge"
       if "22k" in checkpoint_url and "1k" not in checkpoint_url:
              model_name += "-22k"
       elif "22k" in checkpoint_url and "1k" in checkpoint_url:
              model_name += "-22k-1k"
       elif "1k" in checkpoint_url:
              model_name += "-1k"
       if "224" in checkpoint_url:
              model_name += "-224"
       elif "384" in checkpoint_url:
              model_name += "-384"
       elif "512" in checkpoint_url:
              model_name += "-512"

       if push_to_hub:
              print(f"""Pushing {model_name} to the hub..."""     )
              model.push_to_hub(lowercase_     )
              preprocessor.push_to_hub(lowercase_     )


if __name__ == "__main__":
     lowerCAmelCase__  =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt',
         type=str,
         help='URL of the original ConvNeXTV2 checkpoint you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='model',
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--save_model', action='store_true', help='Save model to local')
     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image preprocessor to the hub')

     lowerCAmelCase__  =     parser.parse_args()
     convert_convnextva_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
     )










'''simple docstring'''







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_lowerCamelCase				:		str				      =    '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

_lowerCamelCase				:		Union[str, Any]				      =    '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

_lowerCamelCase				:		List[Any]				      =    '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'





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




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return datasets.MetricInfo(
							    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"					),
							                "prediction_text": datasets.Value("string"					),
							                "no_answer_probability": datasets.Value("float32"					),
							            },
							            "references": {
							                "id": datasets.Value("string"					),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"					), "answer_start": datasets.Value("int32"					)}					),
							            },
							        }					),    codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],    reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],    )






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1.0					):
							_snake_case      			=			{p["id"]: p["no_answer_probability"] for p in predictions}
							_snake_case      			=			[{"paragraphs": [{"qas": references}]}]
							_snake_case      			=			{p["id"]: p["prediction_text"] for p in predictions}

							_snake_case      			=			make_qid_to_has_ans(__UpperCamelCase					)  # maps qid to True/False
							_snake_case      			=			[k for k, v in qid_to_has_ans.items() if v]
							_snake_case      			=			[k for k, v in qid_to_has_ans.items() if not v]

							_snake_case			,       _snake_case      			=			get_raw_scores(__UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase					)

							if has_ans_qids:
									_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase,    qid_list=__UpperCamelCase					)
									merge_eval(__UpperCamelCase,    __UpperCamelCase,    "HasAns"					)
							if no_ans_qids:
									_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase,    qid_list=__UpperCamelCase					)
									merge_eval(__UpperCamelCase,    __UpperCamelCase,    "NoAns"					)
							find_all_best_thresh(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							return dict(__UpperCamelCase					)





