




import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Optional[int]:



       """simple docstring"""

       A__   				=					[2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
       A__   				=					True if '''large''' in model_name or '''huge''' in model_name else False
       A__   				=					True if '''large''' in model_name or '''huge''' in model_name else False
       A__   				=					True if '''large''' in model_name or '''huge''' in model_name else False

       if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
              if "fl3" in model_name:
                     A__   				=					[3, 3, 3, 3]
                     A__   				=					[5, 5, 5, 5]
              elif "fl4" in model_name:
                     A__   				=					[4, 4, 4, 4]
                     A__   				=					[3, 3, 3, 3]

       if "tiny" in model_name or "small" in model_name or "base" in model_name:
              A__   				=					[3, 3, 3, 3]
              if "lrf" in model_name:
                     A__   				=					[3, 3, 3, 3]
              else:
                     A__   				=					[2, 2, 2, 2]

       if "tiny" in model_name:
              A__   				=					96
       elif "small" in model_name:
              A__   				=					96
       elif "base" in model_name:
              A__   				=					128
       elif "large" in model_name:
              A__   				=					192
       elif "xlarge" in model_name:
              A__   				=					256
       elif "huge" in model_name:
              A__   				=					352

       # set label information
       A__   				=					'''huggingface/label-files'''
       if "large" in model_name or "huge" in model_name:
              A__   				=					'''imagenet-22k-id2label.json'''
       else:
              A__   				=					'''imagenet-1k-id2label.json'''

       A__   				=					json.load(open(hf_hub_download(lowercase_				,       lowercase_				,       repo_type='''dataset'''		)				,       '''r'''		)		)
       A__   				=					{int(lowercase_		): v for k, v in idalabel.items()}
       A__   				=					{v: k for k, v in idalabel.items()}

       A__   				=					FocalNetConfig(
           embed_dim=lowercase_				,       depths=lowercase_				,       focal_levels=lowercase_				,       focal_windows=lowercase_				,       use_conv_embed=lowercase_				,       idalabel=lowercase_				,       labelaid=lowercase_				,       use_post_layernorm=lowercase_				,       use_layerscale=lowercase_				,       )

       return config







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       if "patch_embed.proj" in name:
              A__   				=					name.replace('''patch_embed.proj'''				,       '''embeddings.patch_embeddings.projection'''		)
       if "patch_embed.norm" in name:
              A__   				=					name.replace('''patch_embed.norm'''				,       '''embeddings.norm'''		)
       if "layers" in name:
              A__   				=					'''encoder.''' + name
       if "encoder.layers" in name:
              A__   				=					name.replace('''encoder.layers'''				,       '''encoder.stages'''		)
       if "downsample.proj" in name:
              A__   				=					name.replace('''downsample.proj'''				,       '''downsample.projection'''		)
       if "blocks" in name:
              A__   				=					name.replace('''blocks'''				,       '''layers'''		)
       if "modulation.f.weight" in name or "modulation.f.bias" in name:
              A__   				=					name.replace('''modulation.f'''				,       '''modulation.projection_in'''		)
       if "modulation.h.weight" in name or "modulation.h.bias" in name:
              A__   				=					name.replace('''modulation.h'''				,       '''modulation.projection_context'''		)
       if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
              A__   				=					name.replace('''modulation.proj'''				,       '''modulation.projection_out'''		)

       if name == "norm.weight":
              A__   				=					'''layernorm.weight'''
       if name == "norm.bias":
              A__   				=					'''layernorm.bias'''

       if "head" in name:
              A__   				=					name.replace('''head'''				,       '''classifier'''		)
       else:
              A__   				=					'''focalnet.''' + name

       return name







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_=False		)     -> Optional[int]:



       """simple docstring"""

       A__   				=					{
           '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
           '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
           '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
           '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
           '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
           '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
           '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
           '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
           '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
           '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
       }
       # fmt: on

       A__   				=					model_name_to_url[model_name]
       print('''Checkpoint URL: '''				,       lowercase_		)
       A__   				=					torch.hub.load_state_dict_from_url(lowercase_				,       map_location='''cpu'''		)['''model''']

       # rename keys
       for key in state_dict.copy().keys():
              A__   				=					state_dict.pop(lowercase_		)
              A__   				=					val

       A__   				=					get_focalnet_config(lowercase_		)
       A__   				=					FocalNetForImageClassification(lowercase_		)
       model.eval()

       # load state dict
       model.load_state_dict(lowercase_		)

       # verify conversion
       A__   				=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''

       A__   				=					BitImageProcessor(
           do_resize=lowercase_				,       size={'''shortest_edge''': 256}				,       resample=PILImageResampling.BILINEAR				,       do_center_crop=lowercase_				,       crop_size=224				,       do_normalize=lowercase_				,       image_mean=lowercase_				,       image_std=lowercase_				,       )
       A__   				=					Image.open(requests.get(lowercase_				,       stream=lowercase_		).raw		)
       A__   				=					processor(images=lowercase_				,       return_tensors='''pt'''		)

       A__   				=					transforms.Compose(
           [
               transforms.Resize(256		),
               transforms.CenterCrop(224		),
               transforms.ToTensor(),
               transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06]				,       std=[0.2_29, 0.2_24, 0.2_25]		),
           ]		)

       A__   				=					image_transforms(lowercase_		).unsqueeze(0		)

       # verify pixel_values
       assert torch.allclose(inputs.pixel_values				,       lowercase_				,       atol=1E-4		)

       A__   				=					model(**lowercase_		)

       A__   				=					outputs.logits.argmax(-1		).item()
       print('''Predicted class:'''				,       model.config.idalabel[predicted_class_idx]		)

       print('''First values of logits:'''				,       outputs.logits[0, :3]		)

       if model_name == "focalnet-tiny":
              A__   				=					torch.tensor([0.21_66, -0.43_68, 0.21_91]		)
       elif model_name == "focalnet-tiny-lrf":
              A__   				=					torch.tensor([1.16_69, 0.01_25, -0.16_95]		)
       elif model_name == "focalnet-small":
              A__   				=					torch.tensor([0.49_17, -0.04_30, 0.13_41]		)
       elif model_name == "focalnet-small-lrf":
              A__   				=					torch.tensor([-0.25_88, -0.53_42, -0.23_31]		)
       elif model_name == "focalnet-base":
              A__   				=					torch.tensor([-0.16_55, -0.40_90, -0.17_30]		)
       elif model_name == "focalnet-base-lrf":
              A__   				=					torch.tensor([0.53_06, -0.04_83, -0.39_28]		)
       assert torch.allclose(outputs.logits[0, :3]				,       lowercase_				,       atol=1E-4		)
       print('''Looks ok!'''		)

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""		)
              model.save_pretrained(lowercase_		)
              processor.save_pretrained(lowercase_		)

       if push_to_hub:
              print(f"""Pushing model and processor of {model_name} to the hub..."""		)
              model.push_to_hub(f"""{model_name}"""		)
              processor.push_to_hub(f"""{model_name}"""		)


if __name__ == "__main__":
  _lowerCamelCase			:	int							 =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--model_name""",
      default="""focalnet-tiny""",
      type=str,
      help="""Name of the FocalNet model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )
  parser.add_argument(
      """--push_to_hub""",
      action="""store_true""",
      help="""Whether to push the model and processor to the hub.""",
  )

  _lowerCamelCase			:	List[Any]							 =     parser.parse_args()
  convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   IFInpaintingPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           return self._get_dummy_components()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int=0)		->int:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           A__   				=					{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''mask_image''': mask_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  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != '''cuda'''      ,				reason='''float16 requires CUDA''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


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





           '''simple docstring'''

           self._test_save_load_local()





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

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


