
import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)
A  :			Any     				=				logging.getLogger(__name__)

A  :			int     				=				int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

A  :			Tuple     				=				{
    'gpt2': (GPTaLMHeadModel, GPTaTokenizer),
    'ctrl': (CTRLLMHeadModel, CTRLTokenizer),
    'openai-gpt': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    'xlnet': (XLNetLMHeadModel, XLNetTokenizer),
    'transfo-xl': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    'xlm': (XLMWithLMHeadModel, XLMTokenizer),
    'gptj': (GPTJForCausalLM, AutoTokenizer),
    'bloom': (BloomForCausalLM, BloomTokenizerFast),
    'llama': (LlamaForCausalLM, LlamaTokenizer),
    'opt': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
A  :			Optional[Any]     				=				'In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>'


def 					__lowerCAmelCase    (    a__						) ->						Dict:
				np.random.seed(args.seed						)
				torch.manual_seed(args.seed						)
				if args.n_gpu > 0:
								torch.cuda.manual_seed_all(args.seed						)


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						Dict:
				if args.temperature > 0.7:
								logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''						)

				__a    							=					tokenizer.encode(a__		,					add_special_tokens=a__						)
				if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()						):
								logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''						)
				return prompt_text


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						Tuple:
				# kwargs = {"language": None, "mask_token_id": None}

				# Set the language
				__a    							=					hasattr(model.config		,					'''use_lang_emb'''						) and model.config.use_lang_emb
				if hasattr(model.config		,					'''lang2id'''						) and use_lang_emb:
								__a    							=					model.config.langaid.keys()
								if args.xlm_language in available_languages:
												__a    							=					args.xlm_language
								else:
												__a    							=					None
												while language not in available_languages:
																__a    							=					input('''Using XLM. Select language in ''' + str(list(a__						)						) + ''' >>> '''						)

								__a    							=					model.config.langaid[language]
								# kwargs["language"] = tokenizer.lang2id[language]

				# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
				# XLM masked-language modeling (MLM) models need masked token
				# is_xlm_mlm = "mlm" in args.model_name_or_path
				# if is_xlm_mlm:
				#     kwargs["mask_token_id"] = tokenizer.mask_token_id

				return prompt_text


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						Any:
				__a    							=					args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				__a    							=					prefix + prompt_text
				return prompt_text


def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						List[Any]:
				__a    							=					args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
				__a    							=					prefix + prompt_text
				return prompt_text


A  :			Dict     				=				{
    'ctrl': prepare_ctrl_input,
    'xlm': prepare_xlm_input,
    'xlnet': prepare_xlnet_input,
    'transfo-xl': prepare_transfoxl_input,
}


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Union[str, Any]:
				if length < 0 and max_sequence_length > 0:
								__a    							=					max_sequence_length
				elif 0 < max_sequence_length < length:
								__a    							=					max_sequence_length  # No generation bigger than model size
				elif length < 0:
								__a    							=					MAX_LENGTH  # avoid infinite loop
				return length


def 					__lowerCAmelCase    (    a__						) ->						Any:
				__a    							=					None
				if hasattr(a__		,					'''hidden_size'''						):
								__a    							=					model_config.hidden_size
				elif hasattr(a__		,					'''n_embed'''						):
								__a    							=					model_config.n_embed
				elif hasattr(a__		,					'''n_embd'''						):
								__a    							=					model_config.n_embd

				__a    							=					None
				if hasattr(a__		,					'''num_attention_heads'''						):
								__a    							=					model_config.num_attention_heads
				elif hasattr(a__		,					'''n_head'''						):
								__a    							=					model_config.n_head

				if embedding_size is None or num_head is None or num_head == 0:
								raise ValueError('''Check the model config'''						)

				__a    							=					int(embedding_size / num_head						)
				if hasattr(a__		,					'''n_layer'''						):
								__a    							=					model_config.n_layer
				elif hasattr(a__		,					'''num_hidden_layers'''						):
								__a    							=					model_config.num_hidden_layers
				else:
								raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''						)

				return num_layer, num_head, num_embedding_size_per_head


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						Any:
				__a							,  __a							,  __a    							=					sparse_model_config(model.config						)
				if model.config.model_type == "bloom":
								__a    							=					tuple(
								    (
								        torch.empty(int(num_attention_heads * batch_size						)		,					a__		,					a__						)
								        .to(model.dtype						)
								        .to(model.device						),
								        torch.empty(int(num_attention_heads * batch_size						)		,					a__		,					a__						)
								        .to(model.dtype						)
								        .to(model.device						),
								    )
								    for _ in range(a__						)						)
				else:
								__a    							=					tuple(
								    (
								        torch.empty(a__		,					a__		,					a__		,					a__						)
								        .to(model.dtype						)
								        .to(model.device						),
								        torch.empty(a__		,					a__		,					a__		,					a__						)
								        .to(model.dtype						)
								        .to(model.device						),
								    )
								    for _ in range(a__						)						)
				return past_key_values


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						str:
				__a    							=					len(a__						)
				__a    							=					tokenizer.batch_encode_plus(a__		,					return_tensors='''pt'''						)
				__a    							=					dummy_input.to(model.device						)
				if model.config.use_cache:
								__a    							=					generate_past_key_values(a__		,					a__		,					1						)
				__a    							=					torch.cat(
				    [
				        torch.zeros(dummy_input['''attention_mask'''].shape[0]		,					1						)
				        .to(dummy_input['''attention_mask'''].dtype						)
				        .to(model.device						),
				        dummy_input['''attention_mask'''],
				    ]		,					-1		,					)
				return dummy_input







class     __A(				a    ):
				snake_case_		     =					('''_optimized''', '''_default''')




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


								'''simple docstring'''




								__a    							=					optimized
								__a    							=					default




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


								'''simple docstring'''




								if kwargs["past_key_values"] is None and self._default.config.use_cache:
												__a    							=					generate_past_key_values(self._default	,  kwargs['''input_ids'''].shape[0]	,  0       )
								kwargs.pop('''position_ids'''	,  _snake_case       )
								for k in list(kwargs.keys()       ):
												if kwargs[k] is None or isinstance(kwargs[k]	,  _snake_case       ):
																kwargs.pop(_snake_case       )
								__a    							=					self._optimized(**_snake_case       )
								__a    							=					outputs[0]
								__a    							=					outputs[1]
								__a    							=					CausalLMOutputWithPast(
								    loss=_snake_case	,  logits=_snake_case	,  past_key_values=_snake_case	,  hidden_states=_snake_case	,  attentions=_snake_case	,  )
								return fixed_output




				def __getattr__(  self	,  _snake_case       )					->   int:


								'''simple docstring'''




								return getattr(self._default	,  _snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=None	,  _snake_case=None	,  _snake_case=None	,  **_snake_case       )					->   Optional[int]:


								'''simple docstring'''




								return self._default.prepare_inputs_for_generation(
								    _snake_case	,  past_key_values=_snake_case	,  inputs_embeds=_snake_case	,  use_cache=_snake_case	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   Tuple[Tuple[torch.Tensor]]:


								'''simple docstring'''




								return self._default._reorder_cache(_snake_case	,  _snake_case       )



def 					__lowerCAmelCase    (    ) ->						Dict:
				__a    							=					argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_type'''		,					default=a__		,					type=a__		,					required=a__		,					help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()						)		,					)
				parser.add_argument(
				    '''--model_name_or_path'''		,					default=a__		,					type=a__		,					required=a__		,					help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()						)		,					)

				parser.add_argument('''--prompt'''		,					type=a__		,					default=''''''						)
				parser.add_argument('''--length'''		,					type=a__		,					default=20						)
				parser.add_argument('''--stop_token'''		,					type=a__		,					default=a__		,					help='''Token at which text generation is stopped'''						)

				parser.add_argument(
				    '''--temperature'''		,					type=a__		,					default=1.0		,					help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''		,					)
				parser.add_argument(
				    '''--repetition_penalty'''		,					type=a__		,					default=1.0		,					help='''primarily useful for CTRL model; in that case, use 1.2'''						)
				parser.add_argument('''--k'''		,					type=a__		,					default=0						)
				parser.add_argument('''--p'''		,					type=a__		,					default=0.9						)

				parser.add_argument('''--prefix'''		,					type=a__		,					default=''''''		,					help='''Text added prior to input.'''						)
				parser.add_argument('''--padding_text'''		,					type=a__		,					default=''''''		,					help='''Deprecated, the use of `--prefix` is preferred.'''						)
				parser.add_argument('''--xlm_language'''		,					type=a__		,					default=''''''		,					help='''Optional language when used with the XLM model.'''						)

				parser.add_argument('''--seed'''		,					type=a__		,					default=42		,					help='''random seed for initialization'''						)
				parser.add_argument('''--no_cuda'''		,					action='''store_true'''		,					help='''Avoid using CUDA when available'''						)
				parser.add_argument('''--num_return_sequences'''		,					type=a__		,					default=1		,					help='''The number of samples to generate.'''						)
				parser.add_argument(
				    '''--fp16'''		,					action='''store_true'''		,					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''		,					)
				parser.add_argument('''--jit'''		,					action='''store_true'''		,					help='''Whether or not to use jit trace to accelerate inference'''						)
				__a    							=					parser.parse_args()

				__a    							=					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
				__a    							=					0 if args.no_cuda else torch.cuda.device_count()

				logger.warning(F"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""						)

				set_seed(a__						)

				# Initialize the model and tokenizer
				try:
								__a    							=					args.model_type.lower()
								__a							,  __a    							=					MODEL_CLASSES[args.model_type]
				except KeyError:
								raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''						)

				__a    							=					tokenizer_class.from_pretrained(args.model_name_or_path						)
				if tokenizer.pad_token is None:
								__a    							=					tokenizer.eos_token
				__a    							=					model_class.from_pretrained(args.model_name_or_path						)
				model.to(args.device						)

				if args.fpaa:
								model.half()
				__a    							=					getattr(model.config		,					'''max_position_embeddings'''		,					0						)
				__a    							=					adjust_length_to_model(args.length		,					max_sequence_length=a__						)
				logger.info(a__						)

				__a    							=					args.prompt if args.prompt else input('''Model prompt >>> '''						)

				# Different models need different input formatting and/or extra arguments
				__a    							=					args.model_type in PREPROCESSING_FUNCTIONS.keys()
				if requires_preprocessing:
								__a    							=					PREPROCESSING_FUNCTIONS.get(args.model_type						)
								__a    							=					prepare_input(a__		,					a__		,					a__		,					a__						)

								if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
												__a    							=					{'''add_space_before_punct_symbol''': True}
								else:
												__a    							=					{}

								__a    							=					tokenizer.encode(
								    a__		,					add_special_tokens=a__		,					return_tensors='''pt'''		,					**a__						)
				else:
								__a    							=					args.prefix if args.prefix else args.padding_text
								__a    							=					tokenizer.encode(prefix + prompt_text		,					add_special_tokens=a__		,					return_tensors='''pt'''						)
				__a    							=					encoded_prompt.to(args.device						)

				if encoded_prompt.size()[-1] == 0:
								__a    							=					None
				else:
								__a    							=					encoded_prompt

				if args.jit:
								__a    							=					['''enable jit''']
								__a    							=					prepare_jit_inputs(a__		,					a__		,					a__						)
								torch._C._jit_set_texpr_fuser_enabled(a__						)
								__a    							=					False
								if hasattr(a__		,					'''forward'''						):
												__a    							=					inspect.signature(model.forward						)
								else:
												__a    							=					inspect.signature(model.__call__						)
								__a    							=					tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(a__		,					a__						) is not None						)
								__a    							=					torch.jit.trace(a__		,					a__		,					strict=a__						)
								__a    							=					torch.jit.freeze(traced_model.eval()						)
								traced_model(*a__						)
								traced_model(*a__						)

								__a    							=					_ModelFallbackWrapper(a__		,					a__						)

				__a    							=					model.generate(
				    input_ids=a__		,					max_length=args.length + len(encoded_prompt[0]						)		,					temperature=args.temperature		,					top_k=args.k		,					top_p=args.p		,					repetition_penalty=args.repetition_penalty		,					do_sample=a__		,					num_return_sequences=args.num_return_sequences		,					)

				# Remove the batch dimension when returning multiple sequences
				if len(output_sequences.shape						) > 2:
								output_sequences.squeeze_()

				__a    							=					[]

				for generated_sequence_idx, generated_sequence in enumerate(a__						):
								print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""						)
								__a    							=					generated_sequence.tolist()

								# Decode text
								__a    							=					tokenizer.decode(a__		,					clean_up_tokenization_spaces=a__						)

								# Remove all text after the stop token
								__a    							=					text[: text.find(args.stop_token						) if args.stop_token else None]

								# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
								__a    							=					(
								    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]		,					clean_up_tokenization_spaces=a__						)						) :]
								)

								generated_sequences.append(a__						)
								print(a__						)

				return generated_sequences


if __name__ == "__main__":
					main()

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
					)