
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							=       logging.get_logger(__name__)




def 					lowerCamelCase__					(    a__	:	List[str]  )		->						Union[str, Any]:
       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(a__						,   a__						,   repo_type="""dataset"""  )						,   """r"""  )  )
       UpperCamelCase_											=							{int(a__  ): 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 					lowerCamelCase__					(    a__	:	List[Any]  )		->						int:





       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 					lowerCamelCase__					(    )		->						Any:
       UpperCamelCase_											=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCamelCase_											=							Image.open(requests.get(a__						,   stream=a__  ).raw  )
       return im




def 					lowerCamelCase__					(    a__	:	List[Any]  )		->						int:





       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=a__						,   crop_pct=a__						,   image_mean=[0.485, 0.456, 0.406]						,   image_std=[0.229, 0.224, 0.225]						,   resample=PILImageResampling.BICUBIC						,   )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	Any						,   a__	:	List[str]						,   a__	:	Union[str, Any]						,   a__	:	int  )		->						Tuple:
       print("""Downloading original model from checkpoint..."""  )
       # define ConvNeXTV2 configuration based on URL
       UpperCamelCase_							,					UpperCamelCase_											=							get_convnextva_config(a__  )
       # load original state_dict from URL
       UpperCamelCase_											=							torch.hub.load_state_dict_from_url(a__  )["""model"""]

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

       # load HuggingFace model
       UpperCamelCase_											=							ConvNextVaForImageClassification(a__  )
       model.load_state_dict(a__  )
       model.eval()

       # Check outputs on an image, prepared by ConvNextImageProcessor
       UpperCamelCase_											=							convert_preprocessor(a__  )
       UpperCamelCase_											=							preprocessor(images=prepare_img()						,   return_tensors="""pt"""  )
       UpperCamelCase_											=							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":
              UpperCamelCase_											=							torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
              UpperCamelCase_											=							torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
              UpperCamelCase_											=							torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
              UpperCamelCase_											=							torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
              UpperCamelCase_											=							torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]  )
       elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
              UpperCamelCase_											=							torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]  )
       else:
              raise ValueError(f'''Unknown URL: {checkpoint_url}'''  )

       assert torch.allclose(logits[0, :5]						,   a__						,   atol=1e-3  )
       assert logits.shape == expected_shape
       print("""Model outputs match the original results!"""  )

       if save_model:
              print("""Saving model to local..."""  )
              # Create folder to save model
              if not os.path.isdir(a__  ):
                     os.mkdir(a__  )

              model.save_pretrained(a__  )
              preprocessor.save_pretrained(a__  )

       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(a__  )
              preprocessor.push_to_hub(a__  )


if __name__ == "__main__":
      _A							=       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							=       parser.parse_args()
      convert_convnextva_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
      )





"""simple docstring"""




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase							:  Dict 				=		logging.getLogger()

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







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

			'''simple docstring'''






			a__   		=     logging.StreamHandler(sys.stdout			)
			logger.addHandler(__magic_name__			)

			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					a__   		=     run_glue_with_pabee.main()
					for value in result.values():
							self.assertGreaterEqual(__magic_name__       ,    0.75			)




