

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       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
    SCREAMING_SNAKE_CASE_									=						True if 'large' in model_name or 'huge' in model_name else False
    SCREAMING_SNAKE_CASE_									=						True if 'large' in model_name or 'huge' in model_name else False
    SCREAMING_SNAKE_CASE_									=						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:
            SCREAMING_SNAKE_CASE_									=						[3, 3, 3, 3]
            SCREAMING_SNAKE_CASE_									=						[5, 5, 5, 5]
        elif "fl4" in model_name:
            SCREAMING_SNAKE_CASE_									=						[4, 4, 4, 4]
            SCREAMING_SNAKE_CASE_									=						[3, 3, 3, 3]

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

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

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

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

    SCREAMING_SNAKE_CASE_									=						FocalNetConfig(
        embed_dim=_SCREAMING_SNAKE_CASE ,   depths=_SCREAMING_SNAKE_CASE ,   focal_levels=_SCREAMING_SNAKE_CASE ,   focal_windows=_SCREAMING_SNAKE_CASE ,   use_conv_embed=_SCREAMING_SNAKE_CASE ,   idalabel=_SCREAMING_SNAKE_CASE ,   labelaid=_SCREAMING_SNAKE_CASE ,   use_post_layernorm=_SCREAMING_SNAKE_CASE ,   use_layerscale=_SCREAMING_SNAKE_CASE ,   )

    return config


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


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

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

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

    return name


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Tuple=False       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{
        '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

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

    # rename keys
    for key in state_dict.copy().keys():
        SCREAMING_SNAKE_CASE_									=						state_dict.pop(_SCREAMING_SNAKE_CASE       )
        SCREAMING_SNAKE_CASE_									=						val

    SCREAMING_SNAKE_CASE_									=						get_focalnet_config(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						FocalNetForImageClassification(_SCREAMING_SNAKE_CASE       )
    model.eval()

    # load state dict
    model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

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

    SCREAMING_SNAKE_CASE_									=						transforms.Compose(
        [
            transforms.Resize(256       ),
            transforms.CenterCrop(224       ),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406] ,   std=[0.229, 0.224, 0.225]       ),
        ]       )

    SCREAMING_SNAKE_CASE_									=						image_transforms(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )

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

    SCREAMING_SNAKE_CASE_									=						model(**_SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						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":
        SCREAMING_SNAKE_CASE_									=						torch.tensor([0.2166, -0.4368, 0.2191]       )
    elif model_name == "focalnet-tiny-lrf":
        SCREAMING_SNAKE_CASE_									=						torch.tensor([1.1669, 0.0125, -0.1695]       )
    elif model_name == "focalnet-small":
        SCREAMING_SNAKE_CASE_									=						torch.tensor([0.4917, -0.0430, 0.1341]       )
    elif model_name == "focalnet-small-lrf":
        SCREAMING_SNAKE_CASE_									=						torch.tensor([-0.2588, -0.5342, -0.2331]       )
    elif model_name == "focalnet-base":
        SCREAMING_SNAKE_CASE_									=						torch.tensor([-0.1655, -0.4090, -0.1730]       )
    elif model_name == "focalnet-base-lrf":
        SCREAMING_SNAKE_CASE_									=						torch.tensor([0.5306, -0.0483, -0.3928]       )
    assert torch.allclose(outputs.logits[0, :3] ,   _SCREAMING_SNAKE_CASE ,   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(_SCREAMING_SNAKE_CASE       )
        processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

    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__":
       UpperCamelCase__  :      Any			=    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.",
       )

       UpperCamelCase__  :      Dict			=    parser.parse_args()
       convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command



class       __snake_case  (    unittest.TestCase       ):
  __lowerCAmelCase     :	Dict						 =  inspect.getfile(accelerate.test_utils       )
  __lowerCAmelCase     :	Optional[Any]						 =  os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['scripts', 'test_cli.py']       )

  __lowerCAmelCase     :	Tuple						 =  ['accelerate', 'launch']
  __lowerCAmelCase     :	Union[str, Any]						 =  Path.home() / '.cache/huggingface/accelerate'
  __lowerCAmelCase     :	List[str]						 =  'default_config.yaml'
  __lowerCAmelCase     :	List[Any]						 =  config_folder / config_file
  __lowerCAmelCase     :	str						 =  config_folder / '_default_config.yaml'

  __lowerCAmelCase     :	Optional[int]						 =  Path('tests/test_configs'       )

  @classmethod
  def 						lowerCAmelCase__     (					cls):
      if cls.config_path.is_file():
          cls.config_path.rename(cls.changed_path)

  @classmethod
  def 						lowerCAmelCase__     (					cls):
      if cls.changed_path.is_file():
          cls.changed_path.rename(cls.config_path)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.base_cmd
      if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
          cmd += ["--multi_gpu"]
      execute_subprocess_async(cmd + [self.test_file_path]						,		env=os.environ.copy())

  def 						lowerCAmelCase__     (					self):
      for config in sorted(self.test_config_path.glob('**/*.yaml')):
          with self.subTest(config_file=_A):
              execute_subprocess_async(
                  self.base_cmd + ['--config_file', str(_A), self.test_file_path]						,		env=os.environ.copy())


  def 						lowerCAmelCase__     (					self):
      execute_subprocess_async(['accelerate', 'test']						,		env=os.environ.copy())






class       __snake_case  (    unittest.TestCase       ):
  __lowerCAmelCase     :	Optional[Any]						 =  'test-tpu'
  __lowerCAmelCase     :	str						 =  'us-central1-a'
  __lowerCAmelCase     :	Union[str, Any]						 =  'ls'
  __lowerCAmelCase     :	Union[str, Any]						 =  ['accelerate', 'tpu-config']
  __lowerCAmelCase     :	Union[str, Any]						 =  'cd /usr/share'
  __lowerCAmelCase     :	List[Any]						 =  'tests/test_samples/test_command_file.sh'
  __lowerCAmelCase     :	Dict						 =  'Running gcloud compute tpus tpu-vm ssh'

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd
          + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/0_12_0.yaml',
              '--command',
              self.command,
              '--tpu_zone',
              self.tpu_zone,
              '--tpu_name',
              self.tpu_name,
              '--debug',
          ]						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']						,		return_stdout=_A)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/latest.yaml',
              '--command',
              self.command,
              '--command',
              'echo "Hello World"',
              '--debug',
          ]						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd
          + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/0_12_0.yaml',
              '--command_file',
              self.command_file,
              '--tpu_zone',
              self.tpu_zone,
              '--tpu_name',
              self.tpu_name,
              '--debug',
          ]						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""						,		_A						,		)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""						,		_A						,		)


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						run_command(
          self.cmd
          + [
              '--config_file',
              'tests/test_configs/latest.yaml',
              '--install_accelerate',
              '--accelerate_version',
              '12.0.0',
              '--debug',
          ]						,		return_stdout=_A						,		)
      self.assertIn(
          f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""						,		_A						,		)


