






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()
__UpperCamelCase   :						Dict							=  logging.get_logger(__name__)
def        a_	( _A					)     ->     int:







				"""simple docstring"""
				snake_case__													=       ConvNextVaConfig()

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

				snake_case__													=       1000
				snake_case__													=       "imagenet-1k-id2label.json"
				snake_case__													=       (1, 1000)

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

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

				return config, expected_shape
def        a_	( _A					)     ->     List[str]:







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

				return name
def        a_	( )     ->     Optional[Any]:







				"""simple docstring"""
				snake_case__													=       "http://images.cocodataset.org/val2017/000000039769.jpg"
				snake_case__													=       Image.open(requests.get(_a ,				stream=_a					).raw					)
				return im
def        a_	( _A					)     ->     Tuple:







				"""simple docstring"""
				if "224" in checkpoint_url:
								snake_case__													=       224
								snake_case__													=       224 / 256
				elif "384" in checkpoint_url:
								snake_case__													=       384
								snake_case__													=       None
				else:
								snake_case__													=       512
								snake_case__													=       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        a_	( _A ,				_A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				print('Downloading original model from checkpoint...'					)
				# define ConvNeXTV2 configuration based on URL
				snake_case__													=       get_convnextva_config(_a					)
				# load original state_dict from URL
				snake_case__													=       torch.hub.load_state_dict_from_url(_a					)["model"]

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

				# load HuggingFace model
				snake_case__													=       ConvNextVaForImageClassification(_a					)
				model.load_state_dict(_a					)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				snake_case__													=       convert_preprocessor(_a					)
				snake_case__													=       preprocessor(images=prepare_img() ,				return_tensors='pt'					)
				snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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] ,				_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					)

				snake_case__													=       "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__":
				__UpperCamelCase   :						Optional[Any]							=  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""")

				__UpperCamelCase   :						str							=  parser.parse_args()
				convert_convnextva_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
				)












import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.electra.modeling_flax_electra import (
							    FlaxElectraForCausalLM,
							    FlaxElectraForMaskedLM,
							    FlaxElectraForMultipleChoice,
							    FlaxElectraForPreTraining,
							    FlaxElectraForQuestionAnswering,
							    FlaxElectraForSequenceClassification,
							    FlaxElectraForTokenClassification,
							    FlaxElectraModel,
							)


class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def __init__(       self: int    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: Dict=13    ,		UpperCamelCase: Dict=7    ,		UpperCamelCase: Union[str, Any]=True    ,		UpperCamelCase: List[Any]=True    ,		UpperCamelCase: Optional[int]=True    ,		UpperCamelCase: Union[str, Any]=True    ,		UpperCamelCase: Optional[int]=99    ,		UpperCamelCase: Optional[int]=24    ,		UpperCamelCase: Any=32    ,		UpperCamelCase: Any=5    ,		UpperCamelCase: Any=4    ,		UpperCamelCase: int=37    ,		UpperCamelCase: Union[str, Any]="gelu"    ,		UpperCamelCase: str=0.1    ,		UpperCamelCase: str=0.1    ,		UpperCamelCase: int=5_12    ,		UpperCamelCase: Tuple=16    ,		UpperCamelCase: Dict=2    ,		UpperCamelCase: Any=0.02    ,		UpperCamelCase: List[Any]=4    ,		)						->						int:
											snake_case__													=       parent
											snake_case__													=       batch_size
											snake_case__													=       seq_length
											snake_case__													=       is_training
											snake_case__													=       use_attention_mask
											snake_case__													=       use_token_type_ids
											snake_case__													=       use_labels
											snake_case__													=       vocab_size
											snake_case__													=       hidden_size
											snake_case__													=       embedding_size
											snake_case__													=       num_hidden_layers
											snake_case__													=       num_attention_heads
											snake_case__													=       intermediate_size
											snake_case__													=       hidden_act
											snake_case__													=       hidden_dropout_prob
											snake_case__													=       attention_probs_dropout_prob
											snake_case__													=       max_position_embeddings
											snake_case__													=       type_vocab_size
											snake_case__													=       type_sequence_label_size
											snake_case__													=       initializer_range
											snake_case__													=       num_choices
							def lowerCAmelCase_	(       self: List[str]  )						->						List[str]:
											snake_case__													=       ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size  )

											snake_case__													=       None
											if self.use_attention_mask:
															snake_case__													=       random_attention_mask([self.batch_size, self.seq_length]  )

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

											snake_case__													=       ElectraConfig(
											    vocab_size=self.vocab_size    ,		hidden_size=self.hidden_size    ,		embedding_size=self.embedding_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    ,		initializer_range=self.initializer_range    ,		)

											return config, input_ids, token_type_ids, attention_mask


							def lowerCAmelCase_	(       self: Tuple  )						->						Any:
											snake_case__													=       self.prepare_config_and_inputs()
											snake_case__							,  snake_case__							,  snake_case__							,  snake_case__													=       config_and_inputs
											snake_case__													=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


@require_flax
class 					__SCREAMING_SNAKE_CASE(		a_      ,					unittest.TestCase		):
							_UpperCAmelCase	      =    True

							_UpperCAmelCase	      =    (
							    (
							        FlaxElectraModel,
							        FlaxElectraForCausalLM,
							        FlaxElectraForMaskedLM,
							        FlaxElectraForPreTraining,
							        FlaxElectraForTokenClassification,
							        FlaxElectraForQuestionAnswering,
							        FlaxElectraForMultipleChoice,
							        FlaxElectraForSequenceClassification,
							    )
							    if is_flax_available()
							    else ()
							)
							def lowerCAmelCase_	(       self: List[Any]  )						->						Dict:
											snake_case__													=       FlaxElectraModelTester(self  )


							@slow
							def lowerCAmelCase_	(       self: int  )						->						Optional[int]:
											for model_class_name in self.all_model_classes:
															if model_class_name == FlaxElectraForMaskedLM:
																			snake_case__													=       model_class_name.from_pretrained('google/electra-small-generator'  )
															else:
																			snake_case__													=       model_class_name.from_pretrained('google/electra-small-discriminator'  )
															snake_case__													=       model(np.ones((1, 1)  )  )
															self.assertIsNotNone(UpperCamelCase  )




