'''simple docstring'''


import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


snake_case__     :			List[Any]      			=  '''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''






class      __SCREAMING_SNAKE_CASE						(       unittest.TestCase						,	lowerCamelCase_       ):







  '''simple docstring'''






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      load_tool('summarization'						)
       self.tool.setup()
       UpperCAmelCase_							:   Optional[Any]   =      load_tool('summarization'      ,		remote=snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.tool(snake_case_						)
       self.assertEqual(
           snake_case_      ,		'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'      ,		)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      self.remote_tool(snake_case_						)
       self.assertEqual(
           snake_case_      ,		'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'      ,		)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      self.tool(text=snake_case_						)
       self.assertEqual(
           snake_case_      ,		'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'      ,		)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[Any]   =      self.remote_tool(text=snake_case_						)
       self.assertEqual(
           snake_case_      ,		'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'      ,		)







'''simple docstring'''


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()
snake_case__     :			Optional[Any]      			=  logging.get_logger(__name__)


def 					_lowerCamelCase    (      lowerCamelCase_					:							List[str]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Dict   =      ConvNextConfig()

     if "tiny" in checkpoint_url:
          UpperCAmelCase_							:   Any   =      [3, 3, 9, 3]
          UpperCAmelCase_							:   Any   =      [96, 192, 384, 768]
     if "small" in checkpoint_url:
          UpperCAmelCase_							:   int   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Optional[int]   =      [96, 192, 384, 768]
     if "base" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Dict   =      [128, 256, 512, 1024]
     if "large" in checkpoint_url:
          UpperCAmelCase_							:   Optional[Any]   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   List[Any]   =      [192, 384, 768, 1536]
     if "xlarge" in checkpoint_url:
          UpperCAmelCase_							:   str   =      [3, 3, 27, 3]
          UpperCAmelCase_							:   Any   =      [256, 512, 1024, 2048]

     if "1k" in checkpoint_url:
          UpperCAmelCase_							:   int   =      1000
          UpperCAmelCase_							:   Any   =      'imagenet-1k-id2label.json'
          UpperCAmelCase_							:   str   =      (1, 1000)
     else:
          UpperCAmelCase_							:   Dict   =      21841
          UpperCAmelCase_							:   Optional[Any]   =      'imagenet-22k-id2label.json'
          UpperCAmelCase_							:   Optional[Any]   =      (1, 21841)

     UpperCAmelCase_							:   int   =      'huggingface/label-files'
     UpperCAmelCase_							:   Any   =      num_labels
     UpperCAmelCase_							:   Dict   =      json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='dataset'   )	,     'r'   )   )
     UpperCAmelCase_							:   List[Any]   =      {int(lowerCamelCase_   ): 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[9205]
          del idalabel[15027]
     UpperCAmelCase_							:   Optional[Any]   =      idalabel
     UpperCAmelCase_							:   Dict   =      {v: k for k, v in idalabel.items()}
     UpperCAmelCase_							:   Tuple   =      hidden_sizes
     UpperCAmelCase_							:   List[str]   =      depths

     return config, expected_shape


def 					_lowerCamelCase    (      lowerCamelCase_					:							int   ):



     """simple docstring"""





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

     return name


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   List[str]   =      'http://images.cocodataset.org/val2017/000000039769.jpg'
     UpperCAmelCase_							:   Optional[int]   =      Image.open(requests.get(lowerCamelCase_	,     stream=lowerCamelCase_   ).raw   )
     return im


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							int	,     lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[Any]   =      get_convnext_config(lowerCamelCase_   )
     # load original state_dict from URL
     UpperCAmelCase_							:   Dict   =      torch.hub.load_state_dict_from_url(lowerCamelCase_   )['model']
     # rename keys
     for key in state_dict.copy().keys():
          UpperCAmelCase_							:   Optional[int]   =      state_dict.pop(lowerCamelCase_   )
          UpperCAmelCase_							:   Tuple   =      val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          UpperCAmelCase_							:   int   =      state_dict.pop(lowerCamelCase_   )
          if not key.startswith('classifier'   ):
               UpperCAmelCase_							:   int   =      'convnext.' + key
          UpperCAmelCase_							:   Tuple   =      val

     # load HuggingFace model
     UpperCAmelCase_							:   Tuple   =      ConvNextForImageClassification(lowerCamelCase_   )
     model.load_state_dict(lowerCamelCase_   )
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     UpperCAmelCase_							:   Tuple   =      224 if '224' in checkpoint_url else 384
     UpperCAmelCase_							:   Optional[Any]   =      ConvNextImageProcessor(size=lowerCamelCase_   )
     UpperCAmelCase_							:   Tuple   =      image_processor(images=prepare_img()	,     return_tensors='pt'   ).pixel_values

     UpperCAmelCase_							:   Dict   =      model(lowerCamelCase_   ).logits

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

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

     Path(lowerCamelCase_   ).mkdir(exist_ok=lowerCamelCase_   )
     print(F'''Saving model to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(lowerCamelCase_   )
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
     image_processor.save_pretrained(lowerCamelCase_   )

     print('Pushing model to the hub...'   )
     UpperCAmelCase_							:   Union[str, Any]   =      '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(lowerCamelCase_	,     lowerCamelCase_   )	,     organization='nielsr'	,     commit_message='Add model'	,     )


if __name__ == "__main__":
 snake_case__     :			List[Any]      			=  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.''',
 )

 snake_case__     :			int      			=  parser.parse_args()
 convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






