





'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
    )

class 					snake_case					:




 """simple docstring"""



 def __init__(	self	:							Union[str, Any]							,     __A	:							Tuple							,     __A	:							Any=1_3							,     __A	:							int=7							,     __A	:							Optional[Any]=True							,     __A	:							List[Any]=True							,     __A	:							Dict=True							,     __A	:							Any=True							,     __A	:							List[str]=9_9							,     __A	:							Optional[int]=3_2							,     __A	:							Union[str, Any]=2							,     __A	:							Optional[int]=4							,     __A	:							List[str]=3_7							,     __A	:							Dict="gelu"							,     __A	:							str=0.1							,     __A	:							Dict=0.1							,     __A	:							Optional[int]=5_1_2							,     __A	:							Optional[int]=1_6							,     __A	:							Any=2							,     __A	:							Any=0.02							,     __A	:							Tuple=3							,     __A	:							Tuple=4							,     __A	:							str=None							,     ):
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  1_3
        __UpperCamelCase	=  7
        __UpperCamelCase	=  True
        __UpperCamelCase	=  True
        __UpperCamelCase	=  True
        __UpperCamelCase	=  True
        __UpperCamelCase	=  9_9
        __UpperCamelCase	=  3_2
        __UpperCamelCase	=  2
        __UpperCamelCase	=  4
        __UpperCamelCase	=  3_7
        __UpperCamelCase	=  'gelu'
        __UpperCamelCase	=  0.1
        __UpperCamelCase	=  0.1
        __UpperCamelCase	=  5_1_2
        __UpperCamelCase	=  1_6
        __UpperCamelCase	=  False
        __UpperCamelCase	=  -1
        __UpperCamelCase	=  True
        __UpperCamelCase	=  2
        __UpperCamelCase	=  0.02
        __UpperCamelCase	=  3
        __UpperCamelCase	=  4
        __UpperCamelCase	=  None






 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size     )

        __UpperCamelCase	=  None
        if self.use_input_mask:
               __UpperCamelCase	=  random_attention_mask([self.batch_size, self.seq_length]     )

        __UpperCamelCase	=  None
        if self.use_token_type_ids:
               __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.type_vocab_size     )

        __UpperCamelCase	=  None
        __UpperCamelCase	=  None
        __UpperCamelCase	=  None
        if self.use_labels:
               __UpperCamelCase	=  ids_tensor([self.batch_size]							,     self.type_sequence_label_size     )
               __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.num_labels     )

        __UpperCamelCase	=  DebertaConfig(
            vocab_size=self.vocab_size							,     hidden_size=self.hidden_size							,     num_hidden_layers=self.num_hidden_layers							,     num_attention_heads=self.num_attention_heads							,     intermediate_size=self.intermediate_size							,     hidden_act=self.hidden_act							,     hidden_dropout_prob=self.hidden_dropout_prob							,     attention_probs_dropout_prob=self.attention_probs_dropout_prob							,     max_position_embeddings=self.max_position_embeddings							,     type_vocab_size=self.type_vocab_size							,     relative_attention=self.relative_attention							,     max_relative_positions=self.max_relative_positions							,     position_biased_input=self.position_biased_input							,     initializer_range=self.initializer_range							,     return_dict=__UpperCamelCase							,     )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






 def 	_lowerCamelCase (	self	:							Dict							,     __A	:							Union[str, Any]							,     __A	:							List[str]							,     __A	:							Any							,     __A	:							str							,     __A	:							Any							,     __A	:							Optional[int]							,     __A	:							Any     ):
        __UpperCamelCase	=  TFDebertaModel(config=__UpperCamelCase     )
        __UpperCamelCase	=  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        __UpperCamelCase	=  [input_ids, input_mask]
        __UpperCamelCase	=  model(__UpperCamelCase     )

        __UpperCamelCase	=  model(__UpperCamelCase     )

        self.parent.assertEqual(result.last_hidden_state.shape							,     (self.batch_size, self.seq_length, self.hidden_size)     )






 def 	_lowerCamelCase (	self	:							str							,     __A	:							Tuple							,     __A	:							Optional[int]							,     __A	:							str							,     __A	:							int							,     __A	:							Tuple							,     __A	:							List[Any]							,     __A	:							Optional[Any]     ):
        __UpperCamelCase	=  TFDebertaForMaskedLM(config=__UpperCamelCase     )
        __UpperCamelCase	=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        __UpperCamelCase	=  model(__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.vocab_size)     )






 def 	_lowerCamelCase (	self	:							List[str]							,     __A	:							Tuple							,     __A	:							Tuple							,     __A	:							Optional[int]							,     __A	:							Dict							,     __A	:							Any							,     __A	:							Union[str, Any]							,     __A	:							List[str]     ):
        __UpperCamelCase	=  self.num_labels
        __UpperCamelCase	=  TFDebertaForSequenceClassification(config=__UpperCamelCase     )
        __UpperCamelCase	=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        __UpperCamelCase	=  model(__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)     )






 def 	_lowerCamelCase (	self	:							Dict							,     __A	:							str							,     __A	:							Any							,     __A	:							Any							,     __A	:							str							,     __A	:							Optional[int]							,     __A	:							List[Any]							,     __A	:							Dict     ):
        __UpperCamelCase	=  self.num_labels
        __UpperCamelCase	=  TFDebertaForTokenClassification(config=__UpperCamelCase     )
        __UpperCamelCase	=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        __UpperCamelCase	=  model(__UpperCamelCase     )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.seq_length, self.num_labels)     )






 def 	_lowerCamelCase (	self	:							int							,     __A	:							Any							,     __A	:							List[str]							,     __A	:							List[str]							,     __A	:							Optional[Any]							,     __A	:							List[Any]							,     __A	:							int							,     __A	:							Optional[Any]     ):
        __UpperCamelCase	=  TFDebertaForQuestionAnswering(config=__UpperCamelCase     )
        __UpperCamelCase	=  {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        __UpperCamelCase	=  model(__UpperCamelCase     )
        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	:							Dict     ):
        __UpperCamelCase	=  self.prepare_config_and_inputs()
        (
            (
            __UpperCamelCase     
        )	,						(
            __UpperCamelCase     
        )	,						(
            __UpperCamelCase     
        )	,						(
            __UpperCamelCase     
        )	,						(
            __UpperCamelCase     
        )	,						(
            __UpperCamelCase     
        )	,						(
            __UpperCamelCase     
        )	,						     
        )	=  config_and_inputs
        __UpperCamelCase	=  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict






@require_tf
class 					snake_case					(   lowercase_      ,	lowercase_      ,	unittest.TestCase	):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=(
     (
         TFDebertaModel,
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_     : Optional[int]      		=(
     {
         "feature-extraction": TFDebertaModel,
         "fill-mask": TFDebertaForMaskedLM,
         "question-answering": TFDebertaForQuestionAnswering,
         "text-classification": TFDebertaForSequenceClassification,
         "token-classification": TFDebertaForTokenClassification,
         "zero-shot": TFDebertaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 SCREAMING_SNAKE_CASE_     : Optional[int]      		=False
 SCREAMING_SNAKE_CASE_     : Optional[Any]      		=False






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  TFDebertaModelTester(self     )
        __UpperCamelCase	=  ConfigTester(self							,     config_class=__UpperCamelCase							,     hidden_size=3_7     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        self.config_tester.run_common_tests()






 def 	_lowerCamelCase (	self	:							Tuple     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase     )






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase     )






 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase     )






 def 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase     )






 def 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase     )






 @slow
 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  TFDebertaModel.from_pretrained('kamalkraj/deberta-base'     )
        self.assertIsNotNone(__UpperCamelCase     )






@require_tf
class 					snake_case					(   unittest.TestCase	):




 """simple docstring"""



 @unittest.skip(reason='Model not available yet'     )
 def 	_lowerCamelCase (	self	:							List[str]     ):
        pass






 @slow
 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  TFDebertaModel.from_pretrained('kamalkraj/deberta-base'     )
        __UpperCamelCase	=  tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]     )
        __UpperCamelCase	=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
        __UpperCamelCase	=  model(__UpperCamelCase							,     attention_mask=__UpperCamelCase     )[0]

        __UpperCamelCase	=  tf.constant(
            [
                [
                    [-0.5985_5896, -0.8055_2566, -0.846_2135],
                    [1.448_4025, -0.9348_3794, -0.8059_3085],
                    [0.312_2741, 0.0031_6059, -1.413_1377],
                ]
            ]     )
        tf.debugging.assert_near(output[:, 1:4, 1:4]							,     __UpperCamelCase							,     atol=1e-4     )







'''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()
a__							:							Union[str, Any]			=logging.get_logger(__name__)
def     lowercase__       (       __lowercase						:  Union[str, Any]					)			->				Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase	=  ConvNextVaConfig()

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

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

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

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

       return config, expected_shape
def     lowercase__       (       __lowercase						:  Tuple					)			->				Optional[Any]:



       """simple docstring"""






       if "downsample_layers.0.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.0.0'    ,  'embeddings.patch_embeddings'					)
       if "downsample_layers.0.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.0.1'    ,  'embeddings.norm'					)  # we rename to layernorm later on
       if "downsample_layers.1.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.1.0'    ,  'stages.1.downsampling_layer.0'					)
       if "downsample_layers.1.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.1.1'    ,  'stages.1.downsampling_layer.1'					)
       if "downsample_layers.2.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.2.0'    ,  'stages.2.downsampling_layer.0'					)
       if "downsample_layers.2.1" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.2.1'    ,  'stages.2.downsampling_layer.1'					)
       if "downsample_layers.3.0" in name:
              __UpperCamelCase	=  name.replace('downsample_layers.3.0'    ,  'stages.3.downsampling_layer.0'					)
       if "downsample_layers.3.1" in name:
              __UpperCamelCase	=  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	=  name[: len('stages.0'					)] + '.layers' + name[len('stages.0'					) :]
       if "gamma" in name:
              __UpperCamelCase	=  name.replace('gamma'    ,  'weight'					)
       if "beta" in name:
              __UpperCamelCase	=  name.replace('beta'    ,  'bias'					)
       if "stages" in name:
              __UpperCamelCase	=  name.replace('stages'    ,  'encoder.stages'					)
       if "norm" in name:
              __UpperCamelCase	=  name.replace('norm'    ,  'layernorm'					)
       if "head" in name:
              __UpperCamelCase	=  name.replace('head'    ,  'classifier'					)

       return name
def     lowercase__       (       )			->				Any:



       """simple docstring"""






       __UpperCamelCase	=  'http://images.cocodataset.org/val2017/000000039769.jpg'
       __UpperCamelCase	=  Image.open(requests.get(__lowercase    ,  stream=__lowercase					).raw					)
       return im
def     lowercase__       (       __lowercase						:  Optional[Any]					)			->				Dict:



       """simple docstring"""






       if "224" in checkpoint_url:
              __UpperCamelCase	=  224
              __UpperCamelCase	=  224 / 256
       elif "384" in checkpoint_url:
              __UpperCamelCase	=  384
              __UpperCamelCase	=  None
       else:
              __UpperCamelCase	=  512
              __UpperCamelCase	=  None

       return ConvNextImageProcessor(
           size=__lowercase    ,  crop_pct=__lowercase    ,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]    ,  image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    ,  resample=PILImageResampling.BICUBIC    ,  )
@torch.no_grad()
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict    ,  __lowercase						:  Dict					)			->				Optional[int]:



       """simple docstring"""






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

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

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

       # Check outputs on an image, prepared by ConvNextImageProcessor
       __UpperCamelCase	=  convert_preprocessor(__lowercase					)
       __UpperCamelCase	=  preprocessor(images=prepare_img()    ,  return_tensors='pt'					)
       __UpperCamelCase	=  model(**__lowercase					).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	=  torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]					)
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              __UpperCamelCase	=  torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]					)
       else:
              raise ValueError(F'''Unknown URL: {checkpoint_url}'''					)

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

              model.save_pretrained(__lowercase					)
              preprocessor.save_pretrained(__lowercase					)

       __UpperCamelCase	=  '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(__lowercase					)
              preprocessor.push_to_hub(__lowercase					)


if __name__ == "__main__":
    a__							:							str			=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__							:							Optional[Any]			=parser.parse_args()
    convert_convnextva_checkpoint(
        args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
    )
