


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)

enable_full_determinism()






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Optional[int]       					=     UNetaDModel
       lowercase_					:      List[str]       					=     """sample"""
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Tuple 						=						4
           _lowercase			:				Any 						=						3
           _lowercase			:				List[str] 						=						(32, 32)

           _lowercase			:				str 						=						floats_tensor((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				List[str] 						=						torch.tensor([10]).to(lowerCamelCase)

           return {"sample": noise, "timestep": time_step}
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return (3, 32, 32)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return (3, 32, 32)




       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						{
               'block_out_channels': (32, 64),
               'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
               'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
               'attention_head_dim': 3,
               'out_channels': 3,
               'in_channels': 3,
               'layers_per_block': 2,
               'sample_size': 32,
           }
           _lowercase			:				int 						=						self.dummy_input
           return init_dict, inputs_dict






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      List[str]       					=     UNetaDModel
       lowercase_					:      List[Any]       					=     """sample"""
       @property
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						4
           _lowercase			:				int 						=						4
           _lowercase			:				Tuple 						=						(32, 32)

           _lowercase			:				Any 						=						floats_tensor((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				Any 						=						torch.tensor([10]).to(lowerCamelCase)

           return {"sample": noise, "timestep": time_step}
       @property
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           return (4, 32, 32)
       @property
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           return (4, 32, 32)
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						{
               'sample_size': 32,
               'in_channels': 4,
               'out_channels': 4,
               'layers_per_block': 2,
               'block_out_channels': (32, 64),
               'attention_head_dim': 32,
               'down_block_types': ('DownBlock2D', 'DownBlock2D'),
               'up_block_types': ('UpBlock2D', 'UpBlock2D'),
           }
           _lowercase			:				int 						=						self.dummy_input
           return init_dict, inputs_dict
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase)

           self.assertIsNotNone(lowerCamelCase)
           self.assertEqual(len(loading_info['missing_keys']), 0)

           model.to(lowerCamelCase)
           _lowercase			:				List[str] 						=						model(**self.dummy_input).sample

           assert image is not None, "Make sure output is not None"
       @unittest.skipIf(torch_device != 'cuda', 'This test is supposed to run on GPU')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Dict 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase)
           model.to(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						model(**self.dummy_input).sample

           assert image is not None, "Make sure output is not None"
       @unittest.skipIf(torch_device != 'cuda', 'This test is supposed to run on GPU')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Tuple 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase)
           model_accelerate.to(lowerCamelCase)
           model_accelerate.eval()

           _lowercase			:				Union[str, Any] 						=						torch.randn(
               1, model_accelerate.config.in_channels, model_accelerate.config.sample_size, model_accelerate.config.sample_size, generator=torch.manual_seed(0), )
           _lowercase			:				List[str] 						=						noise.to(lowerCamelCase)
           _lowercase			:				List[Any] 						=						torch.tensor([10] * noise.shape[0]).to(lowerCamelCase)

           _lowercase			:				str 						=						model_accelerate(lowerCamelCase, lowerCamelCase)['sample']

           # two models don't need to stay in the device at the same time
           del model_accelerate
           torch.cuda.empty_cache()
           gc.collect()

           _lowercase						,							_lowercase			:				Union[str, Any] 						=						UNetaDModel.from_pretrained(
               'fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase, low_cpu_mem_usage=lowerCamelCase)
           model_normal_load.to(lowerCamelCase)
           model_normal_load.eval()
           _lowercase			:				Dict 						=						model_normal_load(lowerCamelCase, lowerCamelCase)['sample']

           assert torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-3)




       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update')
           model.eval()
           model.to(lowerCamelCase)

           _lowercase			:				List[Any] 						=						torch.randn(
               1, model.config.in_channels, model.config.sample_size, model.config.sample_size, generator=torch.manual_seed(0), )
           _lowercase			:				Dict 						=						noise.to(lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						torch.tensor([10] * noise.shape[0]).to(lowerCamelCase)

           with torch.no_grad():
               _lowercase			:				Dict 						=						model(lowerCamelCase, lowerCamelCase).sample

           _lowercase			:				List[str] 						=						output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           _lowercase			:				Dict 						=						torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0])
           # fmt: on

           self.assertTrue(torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-3))






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      str       					=     UNetaDModel
       lowercase_					:      Any       					=     """sample"""
       @property
       def      UpperCamelCase	(   self, lowerCamelCase=(32, 32))			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						4
           _lowercase			:				int 						=						3

           _lowercase			:				Union[str, Any] 						=						floats_tensor((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				List[Any] 						=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa, device=lowerCamelCase)

           return {"sample": noise, "timestep": time_step}
       @property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return (3, 32, 32)
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return (3, 32, 32)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						{
               'block_out_channels': [32, 64, 64, 64],
               'in_channels': 3,
               'layers_per_block': 1,
               'out_channels': 3,
               'time_embedding_type': 'fourier',
               'norm_eps': 1E-6,
               'mid_block_scale_factor': math.sqrt(2.0),
               'norm_num_groups': None,
               'down_block_types': [
                   'SkipDownBlock2D',
                   'AttnSkipDownBlock2D',
                   'SkipDownBlock2D',
                   'SkipDownBlock2D',
               ],
               'up_block_types': [
                   'SkipUpBlock2D',
                   'SkipUpBlock2D',
                   'AttnSkipUpBlock2D',
                   'SkipUpBlock2D',
               ],
           }
           _lowercase			:				List[Any] 						=						self.dummy_input
           return init_dict, inputs_dict
       @slow
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256', output_loading_info=lowerCamelCase)
           self.assertIsNotNone(lowerCamelCase)
           self.assertEqual(len(loading_info['missing_keys']), 0)

           model.to(lowerCamelCase)
           _lowercase			:				List[str] 						=						self.dummy_input
           _lowercase			:				Dict 						=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						noise
           _lowercase			:				List[Any] 						=						model(**lowerCamelCase)

           assert image is not None, "Make sure output is not None"
       @slow
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256')
           model.to(lowerCamelCase)

           _lowercase			:				Tuple 						=						4
           _lowercase			:				List[str] 						=						3
           _lowercase			:				str 						=						(2_56, 2_56)

           _lowercase			:				int 						=						torch.ones((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				List[str] 						=						torch.tensor(batch_size * [1E-4]).to(lowerCamelCase)

           with torch.no_grad():
               _lowercase			:				Dict 						=						model(lowerCamelCase, lowerCamelCase).sample

           _lowercase			:				Optional[Any] 						=						output[0, -3:, -3:, -1].flatten().cpu()
           # fmt: off
           _lowercase			:				Tuple 						=						torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8])
           # fmt: on

           self.assertTrue(torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-2))
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				int 						=						UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update')
           model.to(lowerCamelCase)

           _lowercase			:				Optional[int] 						=						4
           _lowercase			:				Optional[Any] 						=						3
           _lowercase			:				Optional[int] 						=						(32, 32)

           _lowercase			:				str 						=						torch.ones((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				Tuple 						=						torch.tensor(batch_size * [1E-4]).to(lowerCamelCase)

           with torch.no_grad():
               _lowercase			:				Optional[int] 						=						model(lowerCamelCase, lowerCamelCase).sample

           _lowercase			:				Optional[int] 						=						output[0, -3:, -3:, -1].flatten().cpu()
           # fmt: off
           _lowercase			:				Tuple 						=						torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6])
           # fmt: on

           self.assertTrue(torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-2))




       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass






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 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				List[str] 						=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    _lowercase			:				Tuple 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Any 						=						True if 'large' in model_name or 'huge' in model_name else False
    _lowercase			:				Dict 						=						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:
            _lowercase			:				Any 						=						[3, 3, 3, 3]
            _lowercase			:				Any 						=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            _lowercase			:				Dict 						=						[4, 4, 4, 4]
            _lowercase			:				Tuple 						=						[3, 3, 3, 3]

    if "tiny" in model_name or "small" in model_name or "base" in model_name:
        _lowercase			:				str 						=						[3, 3, 3, 3]
        if "lrf" in model_name:
            _lowercase			:				Optional[int] 						=						[3, 3, 3, 3]
        else:
            _lowercase			:				Dict 						=						[2, 2, 2, 2]

    if "tiny" in model_name:
        _lowercase			:				List[str] 						=						96
    elif "small" in model_name:
        _lowercase			:				Dict 						=						96
    elif "base" in model_name:
        _lowercase			:				Optional[int] 						=						128
    elif "large" in model_name:
        _lowercase			:				List[Any] 						=						192
    elif "xlarge" in model_name:
        _lowercase			:				Optional[Any] 						=						256
    elif "huge" in model_name:
        _lowercase			:				Dict 						=						352

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

    _lowercase			:				Union[str, Any] 						=						json.load(open(hf_hub_download(lowerCamelCase_				,					lowerCamelCase_				,					repo_type='dataset'					)				,					'r'					)					)
    _lowercase			:				int 						=						{int(lowerCamelCase_					): v for k, v in idalabel.items()}
    _lowercase			:				Any 						=						{v: k for k, v in idalabel.items()}

    _lowercase			:				Optional[Any] 						=						FocalNetConfig(
        embed_dim=lowerCamelCase_				,					depths=lowerCamelCase_				,					focal_levels=lowerCamelCase_				,					focal_windows=lowerCamelCase_				,					use_conv_embed=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid=lowerCamelCase_				,					use_post_layernorm=lowerCamelCase_				,					use_layerscale=lowerCamelCase_				,					)

    return config



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Any:
    if "patch_embed.proj" in name:
        _lowercase			:				Optional[Any] 						=						name.replace('patch_embed.proj'				,					'embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        _lowercase			:				str 						=						name.replace('patch_embed.norm'				,					'embeddings.norm'					)
    if "layers" in name:
        _lowercase			:				Any 						=						'encoder.' + name
    if "encoder.layers" in name:
        _lowercase			:				int 						=						name.replace('encoder.layers'				,					'encoder.stages'					)
    if "downsample.proj" in name:
        _lowercase			:				Tuple 						=						name.replace('downsample.proj'				,					'downsample.projection'					)
    if "blocks" in name:
        _lowercase			:				str 						=						name.replace('blocks'				,					'layers'					)
    if "modulation.f.weight" in name or "modulation.f.bias" in name:
        _lowercase			:				List[str] 						=						name.replace('modulation.f'				,					'modulation.projection_in'					)
    if "modulation.h.weight" in name or "modulation.h.bias" in name:
        _lowercase			:				int 						=						name.replace('modulation.h'				,					'modulation.projection_context'					)
    if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
        _lowercase			:				Any 						=						name.replace('modulation.proj'				,					'modulation.projection_out'					)

    if name == "norm.weight":
        _lowercase			:				Any 						=						'layernorm.weight'
    if name == "norm.bias":
        _lowercase			:				Tuple 						=						'layernorm.bias'

    if "head" in name:
        _lowercase			:				Optional[int] 						=						name.replace('head'				,					'classifier'					)
    else:
        _lowercase			:				Optional[int] 						=						'focalnet.' + name

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_=False					)       ->		str:
    # fmt: off
    _lowercase			:				Dict 						=						{
        '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

    _lowercase			:				Dict 						=						model_name_to_url[model_name]
    print('Checkpoint URL: '				,					lowerCamelCase_					)
    _lowercase			:				List[str] 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    # rename keys
    for key in state_dict.copy().keys():
        _lowercase			:				Dict 						=						state_dict.pop(lowerCamelCase_					)
        _lowercase			:				Optional[int] 						=						val

    _lowercase			:				Union[str, Any] 						=						get_focalnet_config(lowerCamelCase_					)
    _lowercase			:				Optional[Any] 						=						FocalNetForImageClassification(lowerCamelCase_					)
    model.eval()

    # load state dict
    model.load_state_dict(lowerCamelCase_					)

    # verify conversion
    _lowercase			:				Optional[Any] 						=						'http://images.cocodataset.org/val2017/000000039769.jpg'

    _lowercase			:				Any 						=						BitImageProcessor(
        do_resize=lowerCamelCase_				,					size={'shortest_edge': 256}				,					resample=PILImageResampling.BILINEAR				,					do_center_crop=lowerCamelCase_				,					crop_size=224				,					do_normalize=lowerCamelCase_				,					image_mean=lowerCamelCase_				,					image_std=lowerCamelCase_				,					)
    _lowercase			:				List[str] 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[Any] 						=						processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    _lowercase			:				str 						=						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]					),
        ]					)

    _lowercase			:				List[str] 						=						image_transforms(lowerCamelCase_					).unsqueeze(0					)

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

    _lowercase			:				Dict 						=						model(**lowerCamelCase_					)

    _lowercase			:				int 						=						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":
        _lowercase			:				Optional[Any] 						=						torch.tensor([0.21_66, -0.43_68, 0.21_91]					)
    elif model_name == "focalnet-tiny-lrf":
        _lowercase			:				int 						=						torch.tensor([1.16_69, 0.01_25, -0.16_95]					)
    elif model_name == "focalnet-small":
        _lowercase			:				str 						=						torch.tensor([0.49_17, -0.04_30, 0.13_41]					)
    elif model_name == "focalnet-small-lrf":
        _lowercase			:				Any 						=						torch.tensor([-0.25_88, -0.53_42, -0.23_31]					)
    elif model_name == "focalnet-base":
        _lowercase			:				List[Any] 						=						torch.tensor([-0.16_55, -0.40_90, -0.17_30]					)
    elif model_name == "focalnet-base-lrf":
        _lowercase			:				int 						=						torch.tensor([0.53_06, -0.04_83, -0.39_28]					)
    assert torch.allclose(outputs.logits[0, :3]				,					lowerCamelCase_				,					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(lowerCamelCase_					)
        processor.save_pretrained(lowerCamelCase_					)

    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__":
 SCREAMING_SNAKE_CASE			: 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.",
 )

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


