
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLMForMultipleChoice,
						    TFXLMForQuestionAnsweringSimple,
						    TFXLMForSequenceClassification,
						    TFXLMForTokenClassification,
						    TFXLMModel,
						    TFXLMWithLMHeadModel,
						    XLMConfig,
						)






class 					__lowercase		:


					def __init__(	self       ,       a__       ,       )		->							Tuple:





									'''simple docstring'''
									A_       	=  parent
									A_       	=  1_3
									A_       	=  7
									A_       	=  True
									A_       	=  True
									A_       	=  True
									A_       	=  True
									A_       	=  True
									A_       	=  False
									A_       	=  False
									A_       	=  False
									A_       	=  2
									A_       	=  9_9
									A_       	=  0
									A_       	=  3_2
									A_       	=  2
									A_       	=  4
									A_       	=  0.1
									A_       	=  0.1
									A_       	=  5_1_2
									A_       	=  1_6
									A_       	=  2
									A_       	=  0.02
									A_       	=  3
									A_       	=  4
									A_       	=  '''last'''
									A_       	=  True
									A_       	=  None
									A_       	=  0


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size						)
									A_       	=  random_attention_mask([self.batch_size, self.seq_length]       ,       dtype=tf.floataa						)

									A_       	=  None
									if self.use_input_lengths:
													A_       	=  (
													    ids_tensor([self.batch_size]       ,       vocab_size=2						) + self.seq_length - 2
													)  # small variation of seq_length

									A_       	=  None
									if self.use_token_type_ids:
													A_       	=  ids_tensor([self.batch_size, self.seq_length]       ,       self.n_langs						)

									A_       	=  None
									A_       	=  None
									A_       	=  None
									if self.use_labels:
													A_       	=  ids_tensor([self.batch_size]       ,       self.type_sequence_label_size						)
													A_       	=  ids_tensor([self.batch_size, self.seq_length]       ,       self.num_labels						)
													A_       	=  ids_tensor([self.batch_size]       ,       2       ,       dtype=tf.floataa						)
													A_       	=  ids_tensor([self.batch_size]       ,       self.num_choices						)

									A_       	=  XLMConfig(
									    vocab_size=self.vocab_size       ,       n_special=self.n_special       ,       emb_dim=self.hidden_size       ,       n_layers=self.num_hidden_layers       ,       n_heads=self.num_attention_heads       ,       dropout=self.hidden_dropout_prob       ,       attention_dropout=self.attention_probs_dropout_prob       ,       gelu_activation=self.gelu_activation       ,       sinusoidal_embeddings=self.sinusoidal_embeddings       ,       asm=self.asm       ,       causal=self.causal       ,       n_langs=self.n_langs       ,       max_position_embeddings=self.max_position_embeddings       ,       initializer_range=self.initializer_range       ,       summary_type=self.summary_type       ,       use_proj=self.use_proj       ,       bos_token_id=self.bos_token_id       ,       )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       )		->							str:





									'''simple docstring'''
									A_       	=  TFXLMModel(config=a__						)
									A_       	=  {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
									A_       	=  model(a__						)

									A_       	=  [input_ids, input_mask]
									A_       	=  model(a__						)
									self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       )		->							List[Any]:





									'''simple docstring'''
									A_       	=  TFXLMWithLMHeadModel(a__						)

									A_       	=  {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
									A_       	=  model(a__						)

									A_       	=  outputs

									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       )		->							Optional[int]:





									'''simple docstring'''
									A_       	=  TFXLMForQuestionAnsweringSimple(a__						)

									A_       	=  {'''input_ids''': input_ids, '''lengths''': input_lengths}

									A_       	=  model(a__						)

									self.parent.assertEqual(result.start_logits.shape       ,       (self.batch_size, self.seq_length)						)
									self.parent.assertEqual(result.end_logits.shape       ,       (self.batch_size, self.seq_length)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       )		->							Dict:





									'''simple docstring'''
									A_       	=  TFXLMForSequenceClassification(a__						)

									A_       	=  {'''input_ids''': input_ids, '''lengths''': input_lengths}

									A_       	=  model(a__						)

									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       )		->							List[Any]:





									'''simple docstring'''
									A_       	=  self.num_labels
									A_       	=  TFXLMForTokenClassification(config=a__						)
									A_       	=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									A_       	=  model(a__						)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.num_labels)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       )		->							Optional[Any]:





									'''simple docstring'''
									A_       	=  self.num_choices
									A_       	=  TFXLMForMultipleChoice(config=a__						)
									A_       	=  tf.tile(tf.expand_dims(a__       ,       1						)       ,       (1, self.num_choices, 1)						)
									A_       	=  tf.tile(tf.expand_dims(a__       ,       1						)       ,       (1, self.num_choices, 1)						)
									A_       	=  tf.tile(tf.expand_dims(a__       ,       1						)       ,       (1, self.num_choices, 1)						)
									A_       	=  {
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									    '''token_type_ids''': multiple_choice_token_type_ids,
									}
									A_       	=  model(a__						)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_choices)						)


					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									A_       	=  self.prepare_config_and_inputs()
									(
									    (
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					(
									    A_
									)	,					
									)       	=  config_and_inputs
									A_       	=  {
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''langs''': token_type_ids,
									    '''lengths''': input_lengths,
									}
									return config, inputs_dict






@require_tf
class 					__lowercase		(					A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							List[str]     	=	(
					    (
					        TFXLMModel,
					        TFXLMWithLMHeadModel,
					        TFXLMForSequenceClassification,
					        TFXLMForQuestionAnsweringSimple,
					        TFXLMForTokenClassification,
					        TFXLMForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					__magic_name__						:							Any     	=	(
					    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
					)  # TODO (PVP): Check other models whether language generation is also applicable
					__magic_name__						:							Union[str, Any]     	=	(
					    {
					        '''feature-extraction''': TFXLMModel,
					        '''fill-mask''': TFXLMWithLMHeadModel,
					        '''question-answering''': TFXLMForQuestionAnsweringSimple,
					        '''text-classification''': TFXLMForSequenceClassification,
					        '''text-generation''': TFXLMWithLMHeadModel,
					        '''token-classification''': TFXLMForTokenClassification,
					        '''zero-shot''': TFXLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					__magic_name__						:							Optional[Any]     	=	False
					__magic_name__						:							Dict     	=	False


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__						)		->							List[str]:





									'''simple docstring'''
									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith('''Fast'''						)
									):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

									return False


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





									'''simple docstring'''
									A_       	=  TFXLMModelTester(self						)
									A_       	=  ConfigTester(self       ,       config_class=a__       ,       emb_dim=3_7						)


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





									'''simple docstring'''
									self.config_tester.run_common_tests()


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





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_model(*a__						)


					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_lm_head(*a__						)


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





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_qa(*a__						)


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





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_sequence_classif(*a__						)


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_token_classification(*a__						)


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





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_multiple_choice(*a__						)


					@slow
					def        lowerCAmelCase_    (	self						)		->							List[str]:





									'''simple docstring'''
									for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_       	=  TFXLMModel.from_pretrained(a__						)
													self.assertIsNotNone(a__						)






@require_tf
class 					__lowercase		(					unittest.TestCase       ):


					@slow
					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									A_       	=  TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''						)
									A_       	=  tf.convert_to_tensor([[1_4, 4_4_7]]       ,       dtype=tf.intaa						)  # the president
									A_       	=  [
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									    1_4,
									    4_4_7,
									]  # the president the president the president the president the president the president the president the president the president the president
									# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
									A_       	=  model.generate(a__       ,       do_sample=a__						)
									self.assertListEqual(output_ids[0].numpy().tolist()       ,       a__						)

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()
SCREAMING_SNAKE_CASE   :		List[str]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  ConvNextVaConfig()

				if "atto" in checkpoint_url:
								A_       	=  [2, 2, 6, 2]
								A_       	=  [40, 80, 1_60, 3_20]
				if "femto" in checkpoint_url:
								A_       	=  [2, 2, 6, 2]
								A_       	=  [48, 96, 1_92, 3_84]
				if "pico" in checkpoint_url:
								A_       	=  [2, 2, 6, 2]
								A_       	=  [64, 1_28, 2_56, 5_12]
				if "nano" in checkpoint_url:
								A_       	=  [2, 2, 8, 2]
								A_       	=  [80, 1_60, 3_20, 6_40]
				if "tiny" in checkpoint_url:
								A_       	=  [3, 3, 9, 3]
								A_       	=  [96, 1_92, 3_84, 7_68]
				if "base" in checkpoint_url:
								A_       	=  [3, 3, 27, 3]
								A_       	=  [1_28, 2_56, 5_12, 10_24]
				if "large" in checkpoint_url:
								A_       	=  [3, 3, 27, 3]
								A_       	=  [1_92, 3_84, 7_68, 15_36]
				if "huge" in checkpoint_url:
								A_       	=  [3, 3, 27, 3]
								A_       	=  [3_52, 7_04, 14_08, 28_16]

				A_       	=  10_00
				A_       	=  '''imagenet-1k-id2label.json'''
				A_       	=  (1, 10_00)

				A_       	=  '''huggingface/label-files'''
				A_       	=  num_labels
				A_       	=  json.load(open(hf_hub_download(__UpperCamelCase ,						__UpperCamelCase ,						repo_type='''dataset'''     ) ,						'''r'''     )     )
				A_       	=  {int(__UpperCamelCase     ): 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_  (   __UpperCamelCase     ):
				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_  (   ):
				A_       	=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A_       	=  Image.open(requests.get(__UpperCamelCase ,						stream=__UpperCamelCase     ).raw     )
				return im



def        lowerCamelCase_  (   __UpperCamelCase     ):
				if "224" in checkpoint_url:
								A_       	=  2_24
								A_       	=  2_24 / 2_56
				elif "384" in checkpoint_url:
								A_       	=  3_84
								A_       	=  None
				else:
								A_       	=  5_12
								A_       	=  None

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



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				print('''Downloading original model from checkpoint...'''     )
				# define ConvNeXTV2 configuration based on URL
				A_	,					A_       	=  get_convnextva_config(__UpperCamelCase     )
				# load original state_dict from URL
				A_       	=  torch.hub.load_state_dict_from_url(__UpperCamelCase     )['''model''']

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

				# load HuggingFace model
				A_       	=  ConvNextVaForImageClassification(__UpperCamelCase     )
				model.load_state_dict(__UpperCamelCase     )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				A_       	=  convert_preprocessor(__UpperCamelCase     )
				A_       	=  preprocessor(images=prepare_img() ,						return_tensors='''pt'''     )
				A_       	=  model(**__UpperCamelCase     ).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([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								A_       	=  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								A_       	=  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								A_       	=  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								A_       	=  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								A_       	=  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								A_       	=  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								A_       	=  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								A_       	=  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								A_       	=  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								A_       	=  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								A_       	=  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]     )
				else:
								raise ValueError(F"Unknown URL: {checkpoint_url}"     )

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

								model.save_pretrained(__UpperCamelCase     )
								preprocessor.save_pretrained(__UpperCamelCase     )

				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(__UpperCamelCase     )
								preprocessor.push_to_hub(__UpperCamelCase     )


if __name__ == "__main__":
						SCREAMING_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")

						SCREAMING_SNAKE_CASE   :		Dict					 =       parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)