




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE			:      Optional[int]      			=logging.get_logger(__name__)

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[int]  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = ConvNextConfig()

      if "tiny" in checkpoint_url:
            lowerCamelCase__					     = [3, 3, 9, 3]
            lowerCamelCase__					     = [96, 1_92, 3_84, 7_68]
      if "small" in checkpoint_url:
            lowerCamelCase__					     = [3, 3, 27, 3]
            lowerCamelCase__					     = [96, 1_92, 3_84, 7_68]
      if "base" in checkpoint_url:
            lowerCamelCase__					     = [3, 3, 27, 3]
            lowerCamelCase__					     = [1_28, 2_56, 5_12, 10_24]
      if "large" in checkpoint_url:
            lowerCamelCase__					     = [3, 3, 27, 3]
            lowerCamelCase__					     = [1_92, 3_84, 7_68, 15_36]
      if "xlarge" in checkpoint_url:
            lowerCamelCase__					     = [3, 3, 27, 3]
            lowerCamelCase__					     = [2_56, 5_12, 10_24, 20_48]

      if "1k" in checkpoint_url:
            lowerCamelCase__					     = 10_00
            lowerCamelCase__					     = """imagenet-1k-id2label.json"""
            lowerCamelCase__					     = (1, 10_00)
      else:
            lowerCamelCase__					     = 2_18_41
            lowerCamelCase__					     = """imagenet-22k-id2label.json"""
            lowerCamelCase__					     = (1, 2_18_41)

      lowerCamelCase__					     = """huggingface/label-files"""
      lowerCamelCase__					     = num_labels
      lowerCamelCase__					     = json.load(open(hf_hub_download(__A   , __A   , repo_type='dataset'  )   , 'r'  )  )
      lowerCamelCase__					     = {int(__A  ): v for k, v in idalabel.items()}
      if "1k" not in checkpoint_url:
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[92_05]
            del idalabel[1_50_27]
      lowerCamelCase__					     = idalabel
      lowerCamelCase__					     = {v: k for k, v in idalabel.items()}
      lowerCamelCase__					     = hidden_sizes
      lowerCamelCase__					     = depths

      return config, expected_shape

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	List[Any]:


      '''simple docstring'''




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

      return name

def 	lowerCAmelCase_	(     )    ->	Optional[int]:


      '''simple docstring'''




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

@torch.no_grad()
def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				str  )    ->	Dict:


      '''simple docstring'''




      lowerCamelCase__					     = get_convnext_config(__A  )
      # load original state_dict from URL
      lowerCamelCase__					     = torch.hub.load_state_dict_from_url(__A  )["""model"""]
      # rename keys
      for key in state_dict.copy().keys():
            lowerCamelCase__					     = state_dict.pop(__A  )
            lowerCamelCase__					     = val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
            lowerCamelCase__					     = state_dict.pop(__A  )
            if not key.startswith('classifier'  ):
                  lowerCamelCase__					     = """convnext.""" + key
            lowerCamelCase__					     = val

      # load HuggingFace model
      lowerCamelCase__					     = ConvNextForImageClassification(__A  )
      model.load_state_dict(__A  )
      model.eval()

      # Check outputs on an image, prepared by ConvNextImageProcessor
      lowerCamelCase__					     = 2_24 if """224""" in checkpoint_url else 3_84
      lowerCamelCase__					     = ConvNextImageProcessor(size=__A  )
      lowerCamelCase__					     = image_processor(images=prepare_img()   , return_tensors='pt'  ).pixel_values

      lowerCamelCase__					     = model(__A  ).logits

      # note: the logits below were obtained without center cropping
      if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
            lowerCamelCase__					     = torch.tensor([-0.1210, -0.6605, 0.1918]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
            lowerCamelCase__					     = torch.tensor([-0.4473, -0.1847, -0.6365]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
            lowerCamelCase__					     = torch.tensor([0.4525, 0.7539, 0.0308]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
            lowerCamelCase__					     = torch.tensor([0.3561, 0.6350, -0.0384]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
            lowerCamelCase__					     = torch.tensor([0.4174, -0.0989, 0.1489]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
            lowerCamelCase__					     = torch.tensor([0.2513, -0.1349, -0.1613]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
            lowerCamelCase__					     = torch.tensor([1.2980, 0.3631, -0.1198]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
            lowerCamelCase__					     = torch.tensor([1.2963, 0.1227, 0.1723]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
            lowerCamelCase__					     = torch.tensor([1.7956, 0.8390, 0.2820]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
            lowerCamelCase__					     = torch.tensor([-0.2822, -0.0502, -0.0878]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
            lowerCamelCase__					     = torch.tensor([-0.5672, -0.0730, -0.4348]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
            lowerCamelCase__					     = torch.tensor([0.2681, 0.2365, 0.6246]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
            lowerCamelCase__					     = torch.tensor([-0.2642, 0.3931, 0.5116]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
            lowerCamelCase__					     = torch.tensor([-0.6677, -0.1873, -0.8379]  )
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
            lowerCamelCase__					     = torch.tensor([-0.7749, -0.2967, -0.6444]  )
      else:
            raise ValueError(f"""Unknown URL: {checkpoint_url}"""  )

      assert torch.allclose(logits[0, :3]   , __A   , atol=1e-3  )
      assert logits.shape == expected_shape

      Path(__A  ).mkdir(exist_ok=__A  )
      print(f"""Saving model to {pytorch_dump_folder_path}"""  )
      model.save_pretrained(__A  )
      print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
      image_processor.save_pretrained(__A  )

      print('Pushing model to the hub...'  )
      lowerCamelCase__					     = """convnext"""
      if "tiny" in checkpoint_url:
            model_name += "-tiny"
      elif "small" in checkpoint_url:
            model_name += "-small"
      elif "base" in checkpoint_url:
            model_name += "-base"
      elif "xlarge" in checkpoint_url:
            model_name += "-xlarge"
      elif "large" in checkpoint_url:
            model_name += "-large"
      if "224" in checkpoint_url:
            model_name += "-224"
      elif "384" in checkpoint_url:
            model_name += "-384"
      if "22k" in checkpoint_url and "1k" not in checkpoint_url:
            model_name += "-22k"
      if "22k" in checkpoint_url and "1k" in checkpoint_url:
            model_name += "-22k-1k"

      model.push_to_hub(
          repo_path_or_name=Path(__A   , __A  )   , organization='nielsr'   , commit_message='Add model'   , )


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE			:      Dict      			=argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--checkpoint_url""",
          default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
          type=str,
          help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )

      __SCREAMING_SNAKE_CASE			:      Tuple      			=parser.parse_args()
      convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def 	snake_case_      (__A     :						Any					,   __A     :						Union[str, Any]     )			->	int:
   assert isinstance(__A					,   __A     )
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,   [False, True]     )
def 	snake_case_      (__A     :						int					,   __A     :						int					,   __A     :						List[str]     )			->	Tuple:
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize(
    """features"""					,   [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,   )
def 	snake_case_      (__A     :						Dict					,   __A     :						List[Any]					,   __A     :						List[str]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			List[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader(__A					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						str					,   __A     :						Optional[int]					,   __A     :						int     )			->	Union[str, Any]:
   __lowerCAmelCase  :			str					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[Any]					    =						CsvDatasetReader(__A					,   cache_dir=__A					,   split=__A     ).read()
   _check_csv_dataset(__A					,   __A     )
   assert dataset.split == split if split else "train"





@pytest.mark.parametrize("""path_type"""					,   [str, list]     )
def 	snake_case_      (__A     :						Union[str, Any]					,   __A     :						Any					,   __A     :						List[str]     )			->	Optional[int]:
   if issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Dict					    =						csv_path
   elif issubclass(__A					,   __A     ):
      __lowerCAmelCase  :			Tuple					    =						[csv_path]
   __lowerCAmelCase  :			Dict					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Dict					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_dataset(__A					,   __A     )





def 	snake_case_      (__A     :						int					,   __A     :						Optional[int]					,   __A     :						Any=("train",)     )			->	Optional[int]:
   assert isinstance(__A					,   __A     )
   for split in splits:
      __lowerCAmelCase  :			int					    =						dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize("""keep_in_memory"""					,   [False, True]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						List[str]					,   __A     :						Any     )			->	List[str]:
   __lowerCAmelCase  :			int					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Optional[Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A					,   keep_in_memory=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize(
    """features"""					,   [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]					,   )
def 	snake_case_      (__A     :						List[str]					,   __A     :						List[Any]					,   __A     :						Optional[int]     )			->	Optional[int]:
   __lowerCAmelCase  :			Optional[int]					    =						tmp_path / """cache"""
   # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
   __lowerCAmelCase  :			Union[str, Any]					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			Optional[int]					    =						features.copy() if features else default_expected_features
   __lowerCAmelCase  :			Optional[Any]					    =						(
       Features({feature: Value(__A     ) for feature, dtype in features.items()}     ) if features is not None else None
   )
   __lowerCAmelCase  :			Union[str, Any]					    =						CsvDatasetReader({"""train""": csv_path}					,   features=__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A     )





@pytest.mark.parametrize("""split"""					,   [None, NamedSplit("""train"""     ), """train""", """test"""]     )
def 	snake_case_      (__A     :						Optional[Any]					,   __A     :						Optional[Any]					,   __A     :						Union[str, Any]     )			->	Optional[int]:
   if split:
      __lowerCAmelCase  :			Any					    =						{split: csv_path}
   else:
      __lowerCAmelCase  :			Dict					    =						{"""train""": csv_path, """test""": csv_path}
   __lowerCAmelCase  :			List[str]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			int					    =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
   __lowerCAmelCase  :			int					    =						CsvDatasetReader(__A					,   cache_dir=__A     ).read()
   _check_csv_datasetdict(__A					,   __A					,   splits=list(path.keys()     )     )
   assert all(dataset[split].split == split for split in path.keys()     )





def 	snake_case_      (__A     :						List[str]     )			->	Optional[Any]:
   with open(__A					,   encoding="""utf-8"""     ) as csvfile:
      yield from csv.reader(__A     )





def 	snake_case_      (__A     :						Optional[int]					,   __A     :						Optional[int]     )			->	Union[str, Any]:
   __lowerCAmelCase  :			Optional[Any]					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			Tuple					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Tuple					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=1     ).write()

   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						List[str]     )			->	str:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[str]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			Dict					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=2     ).write()

   __lowerCAmelCase  :			Union[str, Any]					    =						iter_csv_file(__A     )
   __lowerCAmelCase  :			Dict					    =						iter_csv_file(__A     )

   for rowa, rowa in zip(__A					,   __A     ):
      assert rowa == rowa





def 	snake_case_      (__A     :						int					,   __A     :						Optional[Any]     )			->	List[str]:
   __lowerCAmelCase  :			Any					    =						tmp_path / """cache"""
   __lowerCAmelCase  :			List[Any]					    =						os.path.join(__A					,   """tmp.csv"""     )
   __lowerCAmelCase  :			List[str]					    =						CsvDatasetReader({"""train""": csv_path}					,   cache_dir=__A     ).read()
   with pytest.raises(__A     ):
      CsvDatasetWriter(dataset["""train"""]					,   __A					,   num_proc=0     )


