
from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


A  :			List[Any]     				=				{
    'id': '5733be284776f41900661182',
    'title': 'University_of_Notre_Dame',
    'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
    'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
    'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [5_1_5]},
}


@pytest.mark.parametrize(
    '''task_cls'''		,					[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]		,					)
def 					__lowerCAmelCase    (    a__						) ->						Dict:
				__a    							=					task_cls()
				__a    							=					asdict(_SCREAMING_SNAKE_CASE						)
				__a    							=					task_template_from_dict(_SCREAMING_SNAKE_CASE						)
				assert task == reloaded







class     __A:




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					LanguageModeling(text_column='''input_text'''       )
								assert {"input_text": "text"} == task.column_mapping




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					Features({'''text''': Value('''string'''       )}       )
								__a    							=					{'text_column': 'input_text'}
								__a    							=					LanguageModeling.from_dict(__a       )
								assert "language-modeling" == task.task
								assert input_schema == task.input_schema







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					sorted(['''pos''', '''neg''']       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								__a    							=					TextClassification(text_column='''input_text'''	,  label_column='''input_label'''       )
								self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}	,  task.column_mapping       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					Features({'''text''': Value('''string'''       )}       )
								# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
								__a    							=					Features({'''labels''': ClassLabel}       )
								__a    							=					{'text_column': 'input_text', 'label_column': 'input_labels'}
								__a    							=					TextClassification.from_dict(__a       )
								self.assertEqual('''text-classification'''	,  task.task       )
								self.assertEqual(__a	,  task.input_schema       )
								self.assertEqual(__a	,  task.label_schema       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a    							=					TextClassification(text_column='''input_text'''	,  label_column='''input_label'''       )
								self.assertEqual(task.label_schema['''labels''']	,  __a       )
								__a    							=					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels       )}       )       )
								self.assertEqual(task.label_schema['''labels''']	,  ClassLabel(names=self.labels       )       )







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__a    							=					QuestionAnsweringExtractive(
								    context_column='''input_context'''	,  question_column='''input_question'''	,  answers_column='''input_answers'''       )
								self.assertDictEqual(
								    {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}	,  task.column_mapping       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a    							=					Features({'''question''': Value('''string'''       ), '''context''': Value('''string'''       )}       )
								__a    							=					Features(
								    {
								        '''answers''': Sequence(
								            {
								                '''text''': Value('''string'''       ),
								                '''answer_start''': Value('''int32'''       ),
								            }       )
								    }       )
								__a    							=					{
								    'context_column': 'input_input_context',
								    'question_column': 'input_question',
								    'answers_column': 'input_answers',
								}
								__a    							=					QuestionAnsweringExtractive.from_dict(__a       )
								self.assertEqual('''question-answering-extractive'''	,  task.task       )
								self.assertEqual(__a	,  task.input_schema       )
								self.assertEqual(__a	,  task.label_schema       )







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__a    							=					Summarization(text_column='''input_text'''	,  summary_column='''input_summary'''       )
								self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}	,  task.column_mapping       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					Features({'''text''': Value('''string'''       )}       )
								__a    							=					Features({'''summary''': Value('''string'''       )}       )
								__a    							=					{'text_column': 'input_text', 'summary_column': 'input_summary'}
								__a    							=					Summarization.from_dict(__a       )
								self.assertEqual('''summarization'''	,  task.task       )
								self.assertEqual(__a	,  task.input_schema       )
								self.assertEqual(__a	,  task.label_schema       )







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					AutomaticSpeechRecognition(audio_column='''input_audio'''	,  transcription_column='''input_transcription'''       )
								self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}	,  task.column_mapping       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					Features({'''audio''': Audio()}       )
								__a    							=					Features({'''transcription''': Value('''string'''       )}       )
								__a    							=					{
								    'audio_column': 'input_audio',
								    'transcription_column': 'input_transcription',
								}
								__a    							=					AutomaticSpeechRecognition.from_dict(__a       )
								self.assertEqual('''automatic-speech-recognition'''	,  task.task       )
								self.assertEqual(__a	,  task.input_schema       )
								self.assertEqual(__a	,  task.label_schema       )







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								__a    							=					sorted(['''pos''', '''neg''']       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					AudioClassification(audio_column='''input_audio'''	,  label_column='''input_label'''       )
								self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}	,  task.column_mapping       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					Features({'''audio''': Audio()}       )
								__a    							=					Features({'''labels''': ClassLabel}       )
								__a    							=					{
								    'audio_column': 'input_image',
								    'label_column': 'input_label',
								}
								__a    							=					AudioClassification.from_dict(__a       )
								self.assertEqual('''audio-classification'''	,  task.task       )
								self.assertEqual(__a	,  task.input_schema       )
								self.assertEqual(__a	,  task.label_schema       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					AudioClassification(audio_column='''input_audio'''	,  label_column='''input_label'''       )
								self.assertEqual(task.label_schema['''labels''']	,  __a       )
								__a    							=					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels       )}       )       )
								self.assertEqual(task.label_schema['''labels''']	,  ClassLabel(names=self.labels       )       )







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					sorted(['''pos''', '''neg''']       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a    							=					ImageClassification(image_column='''input_image'''	,  label_column='''input_label'''       )
								self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}	,  task.column_mapping       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Any:


								'''simple docstring'''




								__a    							=					Features({'''image''': Image()}       )
								__a    							=					Features({'''labels''': ClassLabel}       )
								__a    							=					{
								    'image_column': 'input_image',
								    'label_column': 'input_label',
								}
								__a    							=					ImageClassification.from_dict(__a       )
								self.assertEqual('''image-classification'''	,  task.task       )
								self.assertEqual(__a	,  task.input_schema       )
								self.assertEqual(__a	,  task.label_schema       )




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[Any]:


								'''simple docstring'''




								__a    							=					ImageClassification(image_column='''input_image'''	,  label_column='''input_label'''       )
								self.assertEqual(task.label_schema['''labels''']	,  __a       )
								__a    							=					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels       )}       )       )
								self.assertEqual(task.label_schema['''labels''']	,  ClassLabel(names=self.labels       )       )







class     __A(				lowerCAmelCase_    ):




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   str:


								'''simple docstring'''




								__a    							=					DatasetInfo(task_templates=QuestionAnsweringExtractive()       )
								__a    							=					Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}	,  info=__a       )
								assert isinstance(dataset.info.task_templates	,  __a       )
								assert len(dataset.info.task_templates       ) == 1

								def keep_task(_snake_case       ):
												return x

								def dont_keep_task(_snake_case       ):
												__a    							=					deepcopy(__a       )
												__a    							=					0
												return out

								__a    							=					dataset.map(__a       )
								assert mapped_dataset.info.task_templates == dataset.info.task_templates
								# reload from cache
								__a    							=					dataset.map(__a       )
								assert mapped_dataset.info.task_templates == dataset.info.task_templates

								__a    							=					dataset.map(__a       )
								assert mapped_dataset.info.task_templates == []
								# reload from cache
								__a    							=					dataset.map(__a       )
								assert mapped_dataset.info.task_templates == []




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								__a    							=					Features({'''text''': Value('''string'''       ), '''label''': ClassLabel(names=('''pos''', '''neg''')       )}       )
								__a    							=					TextClassification(text_column='''text'''	,  label_column='''label'''       )
								__a    							=					DatasetInfo(features=__a	,  task_templates=__a       )
								__a    							=					Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}	,  info=__a       )

								def process(_snake_case       ):
												return example

								__a    							=					dataset.remove_columns('''label'''       )
								__a    							=					modified_dataset.map(__a       )
								assert mapped_dataset.info.task_templates == []

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()
A  :			Tuple     				=				logging.get_logger(__name__)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				__a    							=					ConvNextVaConfig()

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

				__a    							=					1000
				__a    							=					'''imagenet-1k-id2label.json'''
				__a    							=					(1, 1000)

				__a    							=					'''huggingface/label-files'''
				__a    							=					num_labels
				__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
				__a    							=					{int(a__						): v for k, v in idalabel.items()}

				__a    							=					idalabel
				__a    							=					{v: k for k, v in idalabel.items()}
				__a    							=					hidden_sizes
				__a    							=					depths

				return config, expected_shape


def 					__lowerCAmelCase    (    a__						) ->						str:
				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 "gamma" in name:
								__a    							=					name.replace('''gamma'''		,					'''weight'''						)
				if "beta" in name:
								__a    							=					name.replace('''beta'''		,					'''bias'''						)
				if "stages" in name:
								__a    							=					name.replace('''stages'''		,					'''encoder.stages'''						)
				if "norm" in name:
								__a    							=					name.replace('''norm'''		,					'''layernorm'''						)
				if "head" in name:
								__a    							=					name.replace('''head'''		,					'''classifier'''						)

				return name


def 					__lowerCAmelCase    (    ) ->						Tuple:
				__a    							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a    							=					Image.open(requests.get(a__		,					stream=a__						).raw						)
				return im


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				if "224" in checkpoint_url:
								__a    							=					224
								__a    							=					224 / 256
				elif "384" in checkpoint_url:
								__a    							=					384
								__a    							=					None
				else:
								__a    							=					512
								__a    							=					None

				return ConvNextImageProcessor(
				    size=a__		,					crop_pct=a__		,					image_mean=[0.485, 0.456, 0.406]		,					image_std=[0.229, 0.224, 0.225]		,					resample=PILImageResampling.BICUBIC		,					)


@torch.no_grad()
def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						List[Any]:
				print('''Downloading original model from checkpoint...'''						)
				# define ConvNeXTV2 configuration based on URL
				__a							,  __a    							=					get_convnextva_config(a__						)
				# load original state_dict from URL
				__a    							=					torch.hub.load_state_dict_from_url(a__						)['''model''']

				print('''Converting model parameters...'''						)
				# rename keys
				for key in state_dict.copy().keys():
								__a    							=					state_dict.pop(a__						)
								__a    							=					val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								__a    							=					state_dict.pop(a__						)
								if not key.startswith('''classifier'''						):
												__a    							=					'''convnextv2.''' + key
								__a    							=					val

				# load HuggingFace model
				__a    							=					ConvNextVaForImageClassification(a__						)
				model.load_state_dict(a__						)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				__a    							=					convert_preprocessor(a__						)
				__a    							=					preprocessor(images=prepare_img()		,					return_tensors='''pt'''						)
				__a    							=					model(**a__						).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":
								__a    							=					torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								__a    							=					torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								__a    							=					torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								__a    							=					torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								__a    							=					torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								__a    							=					torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								__a    							=					torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								__a    							=					torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								__a    							=					torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								__a    							=					torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								__a    							=					torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]						)
				else:
								raise ValueError(F"""Unknown URL: {checkpoint_url}"""						)

				assert torch.allclose(logits[0, :5]		,					a__		,					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(a__						):
												os.mkdir(a__						)

								model.save_pretrained(a__						)
								preprocessor.save_pretrained(a__						)

				__a    							=					'''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(a__						)
								preprocessor.push_to_hub(a__						)


if __name__ == "__main__":
					A  :			Optional[int]     				=				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')

					A  :			int     				=				parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)