





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			(    A     ):




   '''simple docstring'''

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

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

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

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

   UpperCAmelCase__						=json.load(open(hf_hub_download(A				,							A				,							repo_type="dataset"     )				,							"r"     )     )
   UpperCAmelCase__						={int(A     ): v for k, v in idalabel.items()}
   UpperCAmelCase__						={v: k for k, v in idalabel.items()}

   UpperCAmelCase__						=FocalNetConfig(
       embed_dim=A				,							depths=A				,							focal_levels=A				,							focal_windows=A				,							use_conv_embed=A				,							idalabel=A				,							labelaid=A				,							use_post_layernorm=A				,							use_layerscale=A				,							)

   return config






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

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

   if name == "norm.weight":
      UpperCAmelCase__						="layernorm.weight"
   if name == "norm.bias":
      UpperCAmelCase__						="layernorm.bias"

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

   return name






def 				_UpperCAmelCase			(    A				,							A				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						={
       "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__						=model_name_to_url[model_name]
   print("Checkpoint URL: "				,							A     )
   UpperCAmelCase__						=torch.hub.load_state_dict_from_url(A				,							map_location="cpu"     )["model"]

   # rename keys
   for key in state_dict.copy().keys():
      UpperCAmelCase__						=state_dict.pop(A     )
      UpperCAmelCase__						=val

   UpperCAmelCase__						=get_focalnet_config(A     )
   UpperCAmelCase__						=FocalNetForImageClassification(A     )
   model.eval()

   # load state dict
   model.load_state_dict(A     )

   # verify conversion
   UpperCAmelCase__						="http://images.cocodataset.org/val2017/000000039769.jpg"

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

   UpperCAmelCase__						=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]     ),
       ]     )

   UpperCAmelCase__						=image_transforms(A     ).unsqueeze(0     )

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

   UpperCAmelCase__						=model(**A     )

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

   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)










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=requests.get(
       F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,							headers={"User-agent": "A random string"}				,							)
   if response.status_code == 429:
      raise requests.HTTPError

   UpperCAmelCase__						=response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(A     )}

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          item: data["data"]["children"][id_]["data"][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))



