
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A  :			Any     				=				logging.get_logger(__name__)

A  :			str     				=				{
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class     __A(				a    ):
				snake_case_		     =					'''blenderbot'''
				snake_case_		     =					['''past_key_values''']
				snake_case_		     =					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




				def __init__(  self	,  _snake_case=8_008	,  _snake_case=128	,  _snake_case=2	,  _snake_case=10_240	,  _snake_case=32	,  _snake_case=24	,  _snake_case=10_240	,  _snake_case=32	,  _snake_case=0.0	,  _snake_case=0.0	,  _snake_case=True	,  _snake_case=True	,  _snake_case="gelu"	,  _snake_case=2_560	,  _snake_case=0.1	,  _snake_case=0.0	,  _snake_case=0.0	,  _snake_case=0.02	,  _snake_case=1	,  _snake_case=False	,  _snake_case=0	,  _snake_case=1	,  _snake_case=2	,  _snake_case=3	,  _snake_case=2	,  **_snake_case	,  )					->   int:


								'''simple docstring'''




								__a    							=					vocab_size
								__a    							=					max_position_embeddings
								__a    							=					d_model
								__a    							=					encoder_ffn_dim
								__a    							=					encoder_layers
								__a    							=					encoder_attention_heads
								__a    							=					decoder_ffn_dim
								__a    							=					decoder_layers
								__a    							=					decoder_attention_heads
								__a    							=					dropout
								__a    							=					attention_dropout
								__a    							=					activation_dropout
								__a    							=					activation_function
								__a    							=					init_std
								__a    							=					encoder_layerdrop
								__a    							=					decoder_layerdrop
								__a    							=					use_cache
								__a    							=					encoder_layers
								__a    							=					scale_embedding  # scale factor will be sqrt(d_model) if True

								super().__init__(
								    pad_token_id=_snake_case	,  bos_token_id=_snake_case	,  eos_token_id=_snake_case	,  is_encoder_decoder=_snake_case	,  decoder_start_token_id=_snake_case	,  encoder_no_repeat_ngram_size=_snake_case	,  forced_eos_token_id=_snake_case	,  **_snake_case	,  )







class     __A(				a    ):




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								if self.task in ["default", "seq2seq-lm"]:
												__a    							=					OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]       )
												if self.use_past:
																__a    							=					{0: '''batch'''}
																__a    							=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
												else:
																__a    							=					{0: '''batch''', 1: '''decoder_sequence'''}
																__a    							=					{0: '''batch''', 1: '''decoder_sequence'''}
												if self.use_past:
																self.fill_with_past_key_values_(_snake_case	,  direction='''inputs'''       )
								elif self.task == "causal-lm":
												__a    							=					OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												    ]       )
												if self.use_past:
																__a							,  __a    							=					self.num_layers
																for i in range(_snake_case       ):
																				__a    							=					{0: '''batch''', 2: '''past_sequence + sequence'''}
																				__a    							=					{0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
												__a    							=					OrderedDict(
												    [
												        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
												        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
												        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
												    ]       )

								return common_inputs




				@property
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								if self.task in ["default", "seq2seq-lm"]:
												__a    							=					super().outputs
								else:
												__a    							=					super(_snake_case	,  self       ).outputs
												if self.use_past:
																__a							,  __a    							=					self.num_layers
																for i in range(_snake_case       ):
																				__a    							=					{0: '''batch''', 2: '''past_sequence + sequence'''}
																				__a    							=					{0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case = -1	,  _snake_case = -1	,  _snake_case = False	,  _snake_case = None	,  )					->   Mapping[str, Any]:


								'''simple docstring'''




								__a    							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )
								# Generate decoder inputs
								__a    							=					seq_length if not self.use_past else 1
								__a    							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )
								__a    							=					{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								__a    							=					dict(**_snake_case	,  **_snake_case       )

								if self.use_past:
												if not is_torch_available():
																raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
												else:
																import torch
												__a							,  __a    							=					common_inputs['''input_ids'''].shape
												__a    							=					common_inputs['''decoder_input_ids'''].shape[1]
												__a							,  __a    							=					self.num_attention_heads
												__a    							=					(
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												__a    							=					decoder_seq_length
												__a    							=					(
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)
												__a    							=					torch.cat(
												    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case	,  _snake_case       )]	,  dim=1       )
												__a    							=					[]
												__a							,  __a    							=					self.num_layers

												for _ in range(_snake_case       ):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(_snake_case       ),
																        torch.zeros(_snake_case       ),
																        torch.zeros(_snake_case       ),
																        torch.zeros(_snake_case       ),
																    )       )
								return common_inputs




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case = -1	,  _snake_case = -1	,  _snake_case = False	,  _snake_case = None	,  )					->   Mapping[str, Any]:


								'''simple docstring'''




								__a    							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )

								if self.use_past:
												if not is_torch_available():
																raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
												else:
																import torch
												__a							,  __a    							=					common_inputs['''input_ids'''].shape
												__a    							=					seqlen
												__a							,  __a    							=					self.num_layers
												__a							,  __a    							=					self.num_attention_heads
												__a    							=					(
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												__a    							=					common_inputs['''attention_mask'''].dtype
												__a    							=					torch.cat(
												    [common_inputs['''attention_mask'''], torch.ones(_snake_case	,  _snake_case	,  dtype=_snake_case       )]	,  dim=1       )
												__a    							=					[
												    (torch.zeros(_snake_case       ), torch.zeros(_snake_case       )) for _ in range(_snake_case       )
												]
								return common_inputs




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case = -1	,  _snake_case = -1	,  _snake_case = False	,  _snake_case = None	,  )					->   Mapping[str, Any]:


								'''simple docstring'''




								__a    							=					compute_effective_axis_dimension(
								    _snake_case	,  fixed_dimension=OnnxConfig.default_fixed_batch	,  num_token_to_add=0       )

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								__a    							=					tokenizer.num_special_tokens_to_add(_snake_case       )
								__a    							=					compute_effective_axis_dimension(
								    _snake_case	,  fixed_dimension=OnnxConfig.default_fixed_sequence	,  num_token_to_add=_snake_case       )

								# Generate dummy inputs according to compute batch and sequence
								__a    							=					[''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
								__a    							=					dict(tokenizer(_snake_case	,  return_tensors=_snake_case       )       )
								return common_inputs




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case = -1	,  _snake_case = -1	,  _snake_case = False	,  _snake_case = None	,  )					->   Mapping[str, Any]:


								'''simple docstring'''




								if self.task in ["default", "seq2seq-lm"]:
												__a    							=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    _snake_case	,  batch_size=_snake_case	,  seq_length=_snake_case	,  is_pair=_snake_case	,  framework=_snake_case       )

								elif self.task == "causal-lm":
												__a    							=					self._generate_dummy_inputs_for_causal_lm(
												    _snake_case	,  batch_size=_snake_case	,  seq_length=_snake_case	,  is_pair=_snake_case	,  framework=_snake_case       )
								else:
												__a    							=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    _snake_case	,  batch_size=_snake_case	,  seq_length=_snake_case	,  is_pair=_snake_case	,  framework=_snake_case       )

								return common_inputs




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


								'''simple docstring'''




								if self.task in ["default", "seq2seq-lm"]:
												__a    							=					super()._flatten_past_key_values_(_snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )
								else:
												__a    							=					super(_snake_case	,  self       )._flatten_past_key_values_(
												    _snake_case	,  _snake_case	,  _snake_case	,  _snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								if direction not in ["inputs", "outputs"]:
												raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""       )

								__a    							=					'''past_key_values''' if direction == '''inputs''' else '''present'''
								__a							,  __a    							=					self.num_layers

								__a    							=					'''past_encoder_sequence'''
								__a    							=					'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

								for i in range(_snake_case       ):
												__a    							=					{0: '''batch''', 2: decoder_sequence}
												__a    							=					{0: '''batch''', 2: decoder_sequence}
												__a    							=					{0: '''batch''', 2: encoder_sequence}
												__a    							=					{0: '''batch''', 2: encoder_sequence}

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
					)