'''simple docstring'''




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 (							UpperCamelCase__				)	->    Optional[Any]:
    __lowerCamelCase		=  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
    __lowerCamelCase		=  True if '''large''' in model_name or '''huge''' in model_name else False
    __lowerCamelCase		=  True if '''large''' in model_name or '''huge''' in model_name else False
    __lowerCamelCase		=  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:
            __lowerCamelCase		=  [3, 3, 3, 3]
            __lowerCamelCase		=  [5, 5, 5, 5]
        elif "fl4" in model_name:
            __lowerCamelCase		=  [4, 4, 4, 4]
            __lowerCamelCase		=  [3, 3, 3, 3]

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

    if "tiny" in model_name:
        __lowerCamelCase		=  96
    elif "small" in model_name:
        __lowerCamelCase		=  96
    elif "base" in model_name:
        __lowerCamelCase		=  1_28
    elif "large" in model_name:
        __lowerCamelCase		=  1_92
    elif "xlarge" in model_name:
        __lowerCamelCase		=  2_56
    elif "huge" in model_name:
        __lowerCamelCase		=  3_52

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

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

    __lowerCamelCase		=  FocalNetConfig(
        embed_dim=UpperCamelCase__				,  depths=UpperCamelCase__				,  focal_levels=UpperCamelCase__				,  focal_windows=UpperCamelCase__				,  use_conv_embed=UpperCamelCase__				,  idalabel=UpperCamelCase__				,  labelaid=UpperCamelCase__				,  use_post_layernorm=UpperCamelCase__				,  use_layerscale=UpperCamelCase__				,  )

    return config






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

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

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

    return name






def __lowerCAmelCase (							UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__=False				)	->    Optional[Any]:
    # fmt: off
    __lowerCamelCase		=  {
        '''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

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

    # rename keys
    for key in state_dict.copy().keys():
        __lowerCamelCase		=  state_dict.pop(UpperCamelCase__				)
        __lowerCamelCase		=  val

    __lowerCamelCase		=  get_focalnet_config(UpperCamelCase__				)
    __lowerCamelCase		=  FocalNetForImageClassification(UpperCamelCase__				)
    model.eval()

    # load state dict
    model.load_state_dict(UpperCamelCase__				)

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

    __lowerCamelCase		=  BitImageProcessor(
        do_resize=UpperCamelCase__				,  size={'''shortest_edge''': 2_56}				,  resample=PILImageResampling.BILINEAR				,  do_center_crop=UpperCamelCase__				,  crop_size=2_24				,  do_normalize=UpperCamelCase__				,  image_mean=UpperCamelCase__				,  image_std=UpperCamelCase__				,  )
    __lowerCamelCase		=  Image.open(requests.get(UpperCamelCase__				,  stream=UpperCamelCase__				).raw				)
    __lowerCamelCase		=  processor(images=UpperCamelCase__				,  return_tensors='''pt'''				)

    __lowerCamelCase		=  transforms.Compose(
        [
            transforms.Resize(2_56				),
            transforms.CenterCrop(2_24				),
            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]				),
        ]				)

    __lowerCamelCase		=  image_transforms(UpperCamelCase__				).unsqueeze(0				)

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

    __lowerCamelCase		=  model(**UpperCamelCase__				)

    __lowerCamelCase		=  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":
        __lowerCamelCase		=  torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]				)
    elif model_name == "focalnet-tiny-lrf":
        __lowerCamelCase		=  torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]				)
    elif model_name == "focalnet-small":
        __lowerCamelCase		=  torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]				)
    elif model_name == "focalnet-small-lrf":
        __lowerCamelCase		=  torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]				)
    elif model_name == "focalnet-base":
        __lowerCamelCase		=  torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]				)
    elif model_name == "focalnet-base-lrf":
        __lowerCamelCase		=  torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]				)
    assert torch.allclose(outputs.logits[0, :3]				,  UpperCamelCase__				,  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(UpperCamelCase__				)
        processor.save_pretrained(UpperCamelCase__				)

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


"""simple docstring"""




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
_A			:       Optional[int]   		=       """
Human: <<task>>

Assistant: """


_A			:       List[Any]   		=       """huggingface-tools/default-prompts"""
_A			:       Optional[int]   		=       {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def   __magic_name__	(      __snake_case				:       int     , __snake_case				:       List[Any]     , __snake_case				:       Dict="run"  ) ->				Union[str, Any]:
       if prompt_or_repo_id is None:
              lowercase    :				List[Any]       		=							DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search("\\s"     , __snake_case  ) is not None:
              return prompt_or_repo_id

       lowercase    :				Optional[int]       		=							cached_file(
           __snake_case     , PROMPT_FILES[mode]     , repo_type="dataset"     , user_agent={"agent": agent_name}  )
       with open(__snake_case     , "r"     , encoding="utf-8"  ) as f:
              return f.read()


