
"""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()
UpperCamelCase      :							Union[str, Any]          =					logging.get_logger(__name__)






def 				A		(							snake_case :str						) ->      Union[str, Any]:
 __UpperCamelCase							    =			ConvNextConfig()

 if "tiny" in checkpoint_url:
  __UpperCamelCase							    =			[3, 3, 9, 3]
  __UpperCamelCase							    =			[9_6, 1_9_2, 3_8_4, 7_6_8]
 if "small" in checkpoint_url:
  __UpperCamelCase							    =			[3, 3, 2_7, 3]
  __UpperCamelCase							    =			[9_6, 1_9_2, 3_8_4, 7_6_8]
 if "base" in checkpoint_url:
  __UpperCamelCase							    =			[3, 3, 2_7, 3]
  __UpperCamelCase							    =			[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
 if "large" in checkpoint_url:
  __UpperCamelCase							    =			[3, 3, 2_7, 3]
  __UpperCamelCase							    =			[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
 if "xlarge" in checkpoint_url:
  __UpperCamelCase							    =			[3, 3, 2_7, 3]
  __UpperCamelCase							    =			[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

 if "1k" in checkpoint_url:
  __UpperCamelCase							    =			1_0_0_0
  __UpperCamelCase							    =			'imagenet-1k-id2label.json'
  __UpperCamelCase							    =			(1, 1_0_0_0)
 else:
  __UpperCamelCase							    =			2_1_8_4_1
  __UpperCamelCase							    =			'imagenet-22k-id2label.json'
  __UpperCamelCase							    =			(1, 2_1_8_4_1)

 __UpperCamelCase							    =			'huggingface/label-files'
 __UpperCamelCase							    =			num_labels
 __UpperCamelCase							    =			json.load(open(hf_hub_download(snake_case							,   snake_case							,   repo_type='dataset'						)							,   'r'						)						)
 __UpperCamelCase							    =			{int(snake_case						): 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[9_2_0_5]
  del idalabel[1_5_0_2_7]
 __UpperCamelCase							    =			idalabel
 __UpperCamelCase							    =			{v: k for k, v in idalabel.items()}
 __UpperCamelCase							    =			hidden_sizes
 __UpperCamelCase							    =			depths

 return config, expected_shape






def 				A		(							snake_case :str						) ->      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 "stages" in name:
  __UpperCamelCase							    =			name.replace('stages'							,   'encoder.stages'						)
 if "norm" in name:
  __UpperCamelCase							    =			name.replace('norm'							,   'layernorm'						)
 if "gamma" in name:
  __UpperCamelCase							    =			name.replace('gamma'							,   'layer_scale_parameter'						)
 if "head" in name:
  __UpperCamelCase							    =			name.replace('head'							,   'classifier'						)

 return name






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






@torch.no_grad()
def 				A		(							snake_case :str							,   snake_case :Dict						) ->      List[str]:
 __UpperCamelCase    ,      __UpperCamelCase							    =			get_convnext_config(snake_case						)
 # load original state_dict from URL
 __UpperCamelCase							    =			torch.hub.load_state_dict_from_url(snake_case						)['model']
 # rename keys
 for key in state_dict.copy().keys():
  __UpperCamelCase							    =			state_dict.pop(snake_case						)
  __UpperCamelCase							    =			val
 # add prefix to all keys expect classifier head
 for key in state_dict.copy().keys():
  __UpperCamelCase							    =			state_dict.pop(snake_case						)
  if not key.startswith('classifier'						):
   __UpperCamelCase							    =			'convnext.' + key
  __UpperCamelCase							    =			val

 # load HuggingFace model
 __UpperCamelCase							    =			ConvNextForImageClassification(snake_case						)
 model.load_state_dict(snake_case						)
 model.eval()

 # Check outputs on an image, prepared by ConvNextImageProcessor
 __UpperCamelCase							    =			2_2_4 if '224' in checkpoint_url else 3_8_4
 __UpperCamelCase							    =			ConvNextImageProcessor(size=snake_case						)
 __UpperCamelCase							    =			image_processor(images=prepare_img()							,   return_tensors='pt'						).pixel_values

 __UpperCamelCase							    =			model(snake_case						).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							    =			torch.tensor([-0.1_210, -0.6_605, 0.1_918]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
  __UpperCamelCase							    =			torch.tensor([-0.4_473, -0.1_847, -0.6_365]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
  __UpperCamelCase							    =			torch.tensor([0.4_525, 0.7_539, 0.0_308]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
  __UpperCamelCase							    =			torch.tensor([0.3_561, 0.6_350, -0.0_384]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
  __UpperCamelCase							    =			torch.tensor([0.4_174, -0.0_989, 0.1_489]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
  __UpperCamelCase							    =			torch.tensor([0.2_513, -0.1_349, -0.1_613]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
  __UpperCamelCase							    =			torch.tensor([1.2_980, 0.3_631, -0.1_198]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
  __UpperCamelCase							    =			torch.tensor([1.2_963, 0.1_227, 0.1_723]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
  __UpperCamelCase							    =			torch.tensor([1.7_956, 0.8_390, 0.2_820]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
  __UpperCamelCase							    =			torch.tensor([-0.2_822, -0.0_502, -0.0_878]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
  __UpperCamelCase							    =			torch.tensor([-0.5_672, -0.0_730, -0.4_348]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
  __UpperCamelCase							    =			torch.tensor([0.2_681, 0.2_365, 0.6_246]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
  __UpperCamelCase							    =			torch.tensor([-0.2_642, 0.3_931, 0.5_116]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
  __UpperCamelCase							    =			torch.tensor([-0.6_677, -0.1_873, -0.8_379]						)
 elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
  __UpperCamelCase							    =			torch.tensor([-0.7_749, -0.2_967, -0.6_444]						)
 else:
  raise ValueError(f'Unknown URL: {checkpoint_url}'						)

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

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

 print('Pushing model to the hub...'						)
 __UpperCamelCase							    =			'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(snake_case							,   snake_case						)							,   organization='nielsr'							,   commit_message='Add model'							,   )


if __name__ == "__main__":
      UpperCamelCase      :							str          =					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.",
      )

      UpperCamelCase      :							str          =					parser.parse_args()
      convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



