






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 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple						):
  __UpperCAmelCase				:      str	 =    [2, 2, 6, 2] if """tiny""" in model_name else [2, 2, 18, 2]
  __UpperCAmelCase				:      Any	 =    True if """large""" in model_name or """huge""" in model_name else False
  __UpperCAmelCase				:      int	 =    True if """large""" in model_name or """huge""" in model_name else False
  __UpperCAmelCase				:      Optional[int]	 =    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:
      __UpperCAmelCase				:      Union[str, Any]	 =    [3, 3, 3, 3]
      __UpperCAmelCase				:      Union[str, Any]	 =    [5, 5, 5, 5]
    elif "fl4" in model_name:
      __UpperCAmelCase				:      str	 =    [4, 4, 4, 4]
      __UpperCAmelCase				:      Optional[Any]	 =    [3, 3, 3, 3]

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

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

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

  __UpperCAmelCase				:      str	 =    json.load(open(hf_hub_download(__lowerCamelCase    ,						__lowerCamelCase    ,						repo_type="""dataset"""						)    ,						"""r"""						)						)
  __UpperCAmelCase				:      Optional[int]	 =    {int(__lowerCamelCase						): v for k, v in idalabel.items()}
  __UpperCAmelCase				:      Union[str, Any]	 =    {v: k for k, v in idalabel.items()}

  __UpperCAmelCase				:      List[str]	 =    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 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple						):
  if "patch_embed.proj" in name:
    __UpperCAmelCase				:      List[Any]	 =    name.replace("""patch_embed.proj"""    ,						"""embeddings.patch_embeddings.projection"""						)
  if "patch_embed.norm" in name:
    __UpperCAmelCase				:      Dict	 =    name.replace("""patch_embed.norm"""    ,						"""embeddings.norm"""						)
  if "layers" in name:
    __UpperCAmelCase				:      int	 =    """encoder.""" + name
  if "encoder.layers" in name:
    __UpperCAmelCase				:      Optional[int]	 =    name.replace("""encoder.layers"""    ,						"""encoder.stages"""						)
  if "downsample.proj" in name:
    __UpperCAmelCase				:      Optional[Any]	 =    name.replace("""downsample.proj"""    ,						"""downsample.projection"""						)
  if "blocks" in name:
    __UpperCAmelCase				:      Union[str, Any]	 =    name.replace("""blocks"""    ,						"""layers"""						)
  if "modulation.f.weight" in name or "modulation.f.bias" in name:
    __UpperCAmelCase				:      List[str]	 =    name.replace("""modulation.f"""    ,						"""modulation.projection_in"""						)
  if "modulation.h.weight" in name or "modulation.h.bias" in name:
    __UpperCAmelCase				:      List[Any]	 =    name.replace("""modulation.h"""    ,						"""modulation.projection_context"""						)
  if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
    __UpperCAmelCase				:      str	 =    name.replace("""modulation.proj"""    ,						"""modulation.projection_out"""						)

  if name == "norm.weight":
    __UpperCAmelCase				:      Optional[Any]	 =    """layernorm.weight"""
  if name == "norm.bias":
    __UpperCAmelCase				:      Dict	 =    """layernorm.bias"""

  if "head" in name:
    __UpperCAmelCase				:      Tuple	 =    name.replace("""head"""    ,						"""classifier"""						)
  else:
    __UpperCAmelCase				:      int	 =    """focalnet.""" + name

  return name






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[int]    ,						__lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	List[str]=False						):
  # fmt: off
  __UpperCAmelCase				:      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

  __UpperCAmelCase				:      int	 =    model_name_to_url[model_name]
  print("""Checkpoint URL: """    ,						__lowerCamelCase						)
  __UpperCAmelCase				:      Dict	 =    torch.hub.load_state_dict_from_url(__lowerCamelCase    ,						map_location="""cpu"""						)["""model"""]

  # rename keys
  for key in state_dict.copy().keys():
    __UpperCAmelCase				:      Tuple	 =    state_dict.pop(__lowerCamelCase						)
    __UpperCAmelCase				:      Optional[Any]	 =    val

  __UpperCAmelCase				:      Optional[Any]	 =    get_focalnet_config(__lowerCamelCase						)
  __UpperCAmelCase				:      Any	 =    FocalNetForImageClassification(__lowerCamelCase						)
  model.eval()

  # load state dict
  model.load_state_dict(__lowerCamelCase						)

  # verify conversion
  __UpperCAmelCase				:      Optional[int]	 =    """http://images.cocodataset.org/val2017/000000039769.jpg"""

  __UpperCAmelCase				:      Union[str, 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    ,						)
  __UpperCAmelCase				:      Optional[Any]	 =    Image.open(requests.get(__lowerCamelCase    ,						stream=__lowerCamelCase						).raw						)
  __UpperCAmelCase				:      List[Any]	 =    processor(images=__lowerCamelCase    ,						return_tensors="""pt"""						)

  __UpperCAmelCase				:      str	 =    transforms.Compose(
      [
          transforms.Resize(256						),
          transforms.CenterCrop(224						),
          transforms.ToTensor(),
          transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]    ,						std=[0.2_2_9, 0.2_2_4, 0.2_2_5]						),
      ]						)

  __UpperCAmelCase				:      List[Any]	 =    image_transforms(__lowerCamelCase						).unsqueeze(0						)

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

  __UpperCAmelCase				:      Dict	 =    model(**__lowerCamelCase						)

  __UpperCAmelCase				:      Any	 =    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":
    __UpperCAmelCase				:      Union[str, Any]	 =    torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]						)
  elif model_name == "focalnet-tiny-lrf":
    __UpperCAmelCase				:      Dict	 =    torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]						)
  elif model_name == "focalnet-small":
    __UpperCAmelCase				:      int	 =    torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]						)
  elif model_name == "focalnet-small-lrf":
    __UpperCAmelCase				:      int	 =    torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]						)
  elif model_name == "focalnet-base":
    __UpperCAmelCase				:      Optional[int]	 =    torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]						)
  elif model_name == "focalnet-base-lrf":
    __UpperCAmelCase				:      Optional[Any]	 =    torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]						)
  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__":
       a						:			Union[str, 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.",
       )

       a						:			Any     	=					parser.parse_args()
       convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					'MCTCTFeatureExtractor'
	lowerCAmelCase__   =					'AutoTokenizer'



	def __init__(       self		,    lowercase		,    lowercase					)							->			str:
					super().__init__(lowercase		,    lowercase					)
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False



	def __call__(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase		,    **lowercase					)

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."					)
									lowerCamelCase_         =     kwargs.pop("raw_speech"					)
					else:
									lowerCamelCase_         =     kwargs.pop("audio"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("sampling_rate"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("text"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process."					)

					if audio is not None:
									lowerCamelCase_         =     self.feature_extractor(lowercase		,    *lowercase		,    sampling_rate=lowercase		,    **lowercase					)
					if text is not None:
									lowerCamelCase_         =     self.tokenizer(lowercase		,    **lowercase					)

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_         =     encodings["input_ids"]
									return inputs



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					return self.tokenizer.batch_decode(*lowercase		,    **lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			Optional[Any]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase		,    **lowercase					)

					lowerCamelCase_         =     kwargs.pop("input_features"		,    lowercase					)
					lowerCamelCase_         =     kwargs.pop("labels"		,    lowercase					)
					if len(lowercase					) > 0:
									lowerCamelCase_         =     args[0]
									lowerCamelCase_         =     args[1:]

					if input_features is not None:
									lowerCamelCase_         =     self.feature_extractor.pad(lowercase		,    *lowercase		,    **lowercase					)
					if labels is not None:
									lowerCamelCase_         =     self.tokenizer.pad(lowercase		,    **lowercase					)

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_         =     labels["input_ids"]
									return input_features



	def        SCREAMING_SNAKE_CASE_(       self		,    *lowercase		,    **lowercase					)							->			List[str]:
					return self.tokenizer.decode(*lowercase		,    **lowercase					)



	@contextmanager
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call."					)
					lowerCamelCase_         =     True
					lowerCamelCase_         =     self.tokenizer
					yield
					lowerCamelCase_         =     self.feature_extractor
					lowerCamelCase_         =     False


