





'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


snake_case_  :							str            =	"./tests/fixtures/elise_format0.mid"


@unittest.skip("The note-seq package currently throws an error on import"  )
class        __a   (lowerCamelCase							,		unittest.TestCase  ):
							__a       : List[Any]          =							SpectrogramDiffusionPipeline
							__a       : str          =							PipelineTesterMixin.required_optional_params - {
							    "callback",
							    "latents",
							    "callback_steps",
							    "output_type",
							    "num_images_per_prompt",
							}
							__a       : List[str]          =							False
							__a       : Optional[int]          =							False
							__a       : List[str]          =							TOKENS_TO_AUDIO_GENERATION_PARAMS
							__a       : Any          =							TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Dict:







													"""simple docstring"""
													torch.manual_seed(0 )
													UpperCAmelCase_      :   List[Any]      =	SpectrogramNotesEncoder(
													    max_length=20_48							,  vocab_size=15_36							,  d_model=7_68							,  dropout_rate=0.1							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  feed_forward_proj='''gated-gelu'''							,  )

													UpperCAmelCase_      :   Tuple      =	SpectrogramContEncoder(
													    input_dims=1_28							,  targets_context_length=2_56							,  d_model=7_68							,  dropout_rate=0.1							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  feed_forward_proj='''gated-gelu'''							,  )

													UpperCAmelCase_      :   List[Any]      =	TaFilmDecoder(
													    input_dims=1_28							,  targets_length=2_56							,  max_decoder_noise_time=2_0_0_0_0.0							,  d_model=7_68							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  dropout_rate=0.1							,  )

													UpperCAmelCase_      :   Optional[int]      =	DDPMScheduler()

													UpperCAmelCase_      :   List[Any]      =	{
													    '''notes_encoder''': notes_encoder.eval(),
													    '''continuous_encoder''': continuous_encoder.eval(),
													    '''decoder''': decoder.eval(),
													    '''scheduler''': scheduler,
													    '''melgan''': None,
													}
													return components

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  Union[str, Any]=0 )       ->							Any:







													"""simple docstring"""
													if str(__magic_name__ ).startswith('''mps''' ):
																			UpperCAmelCase_      :   Dict      =	torch.manual_seed(__magic_name__ )
													else:
																			UpperCAmelCase_      :   Optional[int]      =	torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''input_tokens''': [
													        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
													    ],
													    '''generator''': generator,
													    '''num_inference_steps''': 4,
													    '''output_type''': '''mel''',
													}
													return inputs

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_      :   str      =	self.get_dummy_components()
													UpperCAmelCase_      :   Optional[Any]      =	SpectrogramDiffusionPipeline(**__magic_name__ )
													UpperCAmelCase_      :   str      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   Dict      =	self.get_dummy_inputs(__magic_name__ )
													UpperCAmelCase_      :   Any      =	pipe(**__magic_name__ )
													UpperCAmelCase_      :   str      =	output.audios

													UpperCAmelCase_      :   List[Any]      =	mel[0, -3:, -3:]

													assert mel_slice.shape == (3, 3)
													UpperCAmelCase_      :   Union[str, Any]      =	np.array(
													    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0] )
													assert np.abs(mel_slice.flatten() - expected_slice ).max() < 1E-2

							@skip_mps
							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Tuple:







													"""simple docstring"""
													return super().test_save_load_local()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													return super().test_save_load_optional_components()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							List[Any]:







													"""simple docstring"""
													return super().test_attention_slicing_forward_pass()

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							int:







													"""simple docstring"""
													pass

							def      UpperCAmelCase__						(				self				:  Any )       ->							Dict:







													"""simple docstring"""
													pass



							@skip_mps
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class        __a   (unittest.TestCase  ):

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

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







													"""simple docstring"""
													# TODO - test that pipeline can decode tokens in a callback
													# so that music can be played live
													UpperCAmelCase_      :   Dict      =	torch_device

													UpperCAmelCase_      :   List[str]      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   Union[str, Any]      =	pipe.melgan
													UpperCAmelCase_      :   List[str]      =	None

													UpperCAmelCase_      :   Optional[int]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													def callback(__magic_name__				:  str							,  __magic_name__				:  List[str] ):
																			# decode mel to audio
																			UpperCAmelCase_      :   Tuple      =	melgan(input_features=mel_output.astype(np.floataa ) )[0]
																			assert len(audio[0] ) == 8_19_20 * (step + 1)
																			# simulate that audio is played
																			return audio

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()
													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	input_tokens[:3]
													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													pipe(__magic_name__							,  num_inference_steps=5							,  generator=__magic_name__							,  callback=__magic_name__							,  output_type='''mel''' )

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







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	torch_device

													UpperCAmelCase_      :   Any      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   Any      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )
													# just run two denoising loops
													UpperCAmelCase_      :   int      =	input_tokens[:2]

													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Optional[Any]      =	pipe(__magic_name__							,  num_inference_steps=2							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	output.audios[0]

													assert abs(np.abs(__magic_name__ ).sum() - 3_6_1_2.8_4_1 ) < 1E-1



							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	torch_device

													UpperCAmelCase_      :   Optional[int]      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   List[Any]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[Any]      =	processor(__magic_name__ )

													# just run 4 denoising loops
													UpperCAmelCase_      :   Dict      =	input_tokens[:4]

													UpperCAmelCase_      :   Tuple      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	pipe(__magic_name__							,  num_inference_steps=1_00							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[int]      =	output.audios[0]
													assert abs(np.abs(__magic_name__ ).sum() - 9_3_8_9.1_1_1_1 ) < 5E-2












'''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()
snake_case_  :							Optional[int]            =	logging.get_logger(__name__)





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						Dict:
						UpperCAmelCase_      :   int      =	ConvNextVaConfig()

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

						UpperCAmelCase_      :   Union[str, Any]      =	1000
						UpperCAmelCase_      :   Optional[Any]      =	'''imagenet-1k-id2label.json'''
						UpperCAmelCase_      :   int      =	(1, 1000)

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

						UpperCAmelCase_      :   Union[str, Any]      =	idalabel
						UpperCAmelCase_      :   List[Any]      =	{v: k for k, v in idalabel.items()}
						UpperCAmelCase_      :   str      =	hidden_sizes
						UpperCAmelCase_      :   Dict      =	depths

						return config, expected_shape





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

						return name





def 							lowerCamelCase_   (	)			->						Union[str, Any]:
						UpperCAmelCase_      :   int      =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase_      :   Optional[int]      =	Image.open(requests.get(SCREAMING_SNAKE_CASE__,      stream=SCREAMING_SNAKE_CASE__ ).raw )
						return im





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						int:
						if "224" in checkpoint_url:
												UpperCAmelCase_      :   Optional[Any]      =	224
												UpperCAmelCase_      :   Any      =	224 / 256
						elif "384" in checkpoint_url:
												UpperCAmelCase_      :   int      =	384
												UpperCAmelCase_      :   Optional[Any]      =	None
						else:
												UpperCAmelCase_      :   List[Any]      =	512
												UpperCAmelCase_      :   Union[str, Any]      =	None

						return ConvNextImageProcessor(
						    size=SCREAMING_SNAKE_CASE__,      crop_pct=SCREAMING_SNAKE_CASE__,      image_mean=[0.4_85, 0.4_56, 0.4_06],      image_std=[0.2_29, 0.2_24, 0.2_25],      resample=PILImageResampling.BICUBIC,      )





@torch.no_grad()
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      str )			->						Optional[Any]:
						print('''Downloading original model from checkpoint...''' )
						# define ConvNeXTV2 configuration based on URL
						UpperCAmelCase_    ,							UpperCAmelCase_      :   Tuple      =	get_convnextva_config(SCREAMING_SNAKE_CASE__ )
						# load original state_dict from URL
						UpperCAmelCase_      :   int      =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ )['''model''']

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

						# load HuggingFace model
						UpperCAmelCase_      :   Optional[int]      =	ConvNextVaForImageClassification(SCREAMING_SNAKE_CASE__ )
						model.load_state_dict(SCREAMING_SNAKE_CASE__ )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						UpperCAmelCase_      :   Any      =	convert_preprocessor(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Tuple      =	preprocessor(images=prepare_img(),      return_tensors='''pt''' )
						UpperCAmelCase_      :   Optional[Any]      =	model(**SCREAMING_SNAKE_CASE__ ).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":
												UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([-0.39_30, 0.17_47, -0.52_46, 0.41_77, 0.42_95] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
												UpperCAmelCase_      :   List[Any]      =	torch.tensor([-0.17_27, -0.53_41, -0.78_18, -0.47_45, -0.65_66] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
												UpperCAmelCase_      :   Dict      =	torch.tensor([-0.03_33, 0.15_63, -0.91_37, 0.10_54, 0.03_81] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
												UpperCAmelCase_      :   Tuple      =	torch.tensor([-0.17_44, -0.15_55, -0.07_13, 0.09_50, -0.14_31] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([0.99_96, 0.19_66, -0.43_86, -0.34_72, 0.66_61] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([-0.25_53, -0.67_08, -0.13_59, 0.25_18, -0.24_88] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
												UpperCAmelCase_      :   int      =	torch.tensor([-0.06_73, -0.56_27, -0.37_53, -0.27_22, 0.01_78] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([-0.63_77, -0.74_58, -0.21_50, 0.11_84, -0.05_97] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
												UpperCAmelCase_      :   List[Any]      =	torch.tensor([1.07_99, 0.23_22, -0.88_60, 1.02_19, 0.62_31] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([0.37_66, 0.49_17, -1.14_26, 0.99_42, 0.60_24] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
												UpperCAmelCase_      :   Dict      =	torch.tensor([0.42_20, -0.69_19, -0.43_17, -0.28_81, -0.66_09] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
												UpperCAmelCase_      :   Dict      =	torch.tensor([0.10_82, -0.82_86, -0.50_95, 0.46_81, -0.80_85] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([-0.24_19, -0.62_21, 0.21_76, -0.09_80, -0.75_27] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
												UpperCAmelCase_      :   Optional[int]      =	torch.tensor([0.03_91, -0.43_71, 0.37_86, 0.12_51, -0.27_84] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
												UpperCAmelCase_      :   Any      =	torch.tensor([-0.05_04, 0.56_36, -0.17_29, -0.65_07, -0.39_49] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
												UpperCAmelCase_      :   Optional[Any]      =	torch.tensor([0.35_60, 0.94_86, 0.31_49, -0.26_67, -0.51_38] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
												UpperCAmelCase_      :   List[Any]      =	torch.tensor([-0.24_69, -0.45_50, -0.58_53, -0.08_10, 0.03_09] )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
												UpperCAmelCase_      :   Tuple      =	torch.tensor([-0.30_90, 0.08_02, -0.06_82, -0.19_79, -0.28_26] )
						else:
												raise ValueError(F"""Unknown URL: {checkpoint_url}""" )

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

												model.save_pretrained(SCREAMING_SNAKE_CASE__ )
												preprocessor.save_pretrained(SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Dict      =	'''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(SCREAMING_SNAKE_CASE__ )
												preprocessor.push_to_hub(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					snake_case_  :							Tuple            =	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")

					snake_case_  :							Optional[int]            =	parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)





