





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=old_name

   if "patch_embed" in old_name:
      UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=old_name.split("."     )

      if layer == "0":
         UpperCAmelCase__						=old_name.replace("0"				,							"convolution1"     )
      elif layer == "1":
         UpperCAmelCase__						=old_name.replace("1"				,							"batchnorm_before"     )
      elif layer == "3":
         UpperCAmelCase__						=old_name.replace("3"				,							"convolution2"     )
      else:
         UpperCAmelCase__						=old_name.replace("4"				,							"batchnorm_after"     )

   if "network" in old_name and re.search(R"\d\.\d"				,							A     ):
      UpperCAmelCase__						=R"\b\d{2}\b"
      if bool(re.search(A				,							A     )     ):
         UpperCAmelCase__						=re.search(R"\d\.\d\d."				,							A     ).group()
      else:
         UpperCAmelCase__						=re.search(R"\d\.\d."				,							A     ).group()
      if int(match[0]     ) < 6:
         UpperCAmelCase__						=old_name.replace(A				,							""     )
         UpperCAmelCase__						=trimmed_name.replace("network"				,							match[0] + ".meta4D_layers.blocks." + match[2:-1]     )
         UpperCAmelCase__						="intermediate_stages." + trimmed_name
      else:
         UpperCAmelCase__						=old_name.replace(A				,							""     )
         if int(match[2]     ) < num_meta4D_last_stage:
            UpperCAmelCase__						=trimmed_name.replace("network"				,							"meta4D_layers.blocks." + match[2]     )
         else:
            UpperCAmelCase__						=str(int(match[2]     ) - num_meta4D_last_stage     )
            UpperCAmelCase__						=trimmed_name.replace("network"				,							"meta3D_layers.blocks." + layer_index     )
            if "norm1" in old_name:
               UpperCAmelCase__						=trimmed_name.replace("norm1"				,							"layernorm1"     )
            elif "norm2" in old_name:
               UpperCAmelCase__						=trimmed_name.replace("norm2"				,							"layernorm2"     )
            elif "fc1" in old_name:
               UpperCAmelCase__						=trimmed_name.replace("fc1"				,							"linear_in"     )
            elif "fc2" in old_name:
               UpperCAmelCase__						=trimmed_name.replace("fc2"				,							"linear_out"     )

         UpperCAmelCase__						="last_stage." + trimmed_name

   elif "network" in old_name and re.search(R".\d."				,							A     ):
      UpperCAmelCase__						=old_name.replace("network"				,							"intermediate_stages"     )

   if "fc" in new_name:
      UpperCAmelCase__						=new_name.replace("fc"				,							"convolution"     )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      UpperCAmelCase__						=new_name.replace("norm1"				,							"batchnorm_before"     )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      UpperCAmelCase__						=new_name.replace("norm2"				,							"batchnorm_after"     )
   if "proj" in new_name:
      UpperCAmelCase__						=new_name.replace("proj"				,							"projection"     )
   if "dist_head" in new_name:
      UpperCAmelCase__						=new_name.replace("dist_head"				,							"distillation_classifier"     )
   elif "head" in new_name:
      UpperCAmelCase__						=new_name.replace("head"				,							"classifier"     )
   elif "patch_embed" in new_name:
      UpperCAmelCase__						="efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      UpperCAmelCase__						=new_name.replace("norm"				,							"layernorm"     )
      UpperCAmelCase__						="efficientformer." + new_name
   else:
      UpperCAmelCase__						="efficientformer.encoder." + new_name

   return new_name






def 				_UpperCAmelCase			(    A				,							A     ):




   '''simple docstring'''

   for key in checkpoint.copy().keys():
      UpperCAmelCase__						=checkpoint.pop(A     )
      UpperCAmelCase__						=val

   return checkpoint






def 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase__						=Image.open(requests.get(A				,							stream=A     ).raw     )

   return image






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=torch.load(A				,							map_location="cpu"     )["model"]
   UpperCAmelCase__						=EfficientFormerConfig.from_json_file(A     )
   UpperCAmelCase__						=EfficientFormerForImageClassificationWithTeacher(A     )
   UpperCAmelCase__						="_".join(checkpoint_path.split("/"     )[-1].split("."     )[0].split("_"     )[:-1]     )

   UpperCAmelCase__						=config.depths[-1] - config.num_metaad_blocks + 1
   UpperCAmelCase__						=convert_torch_checkpoint(A				,							A     )

   model.load_state_dict(A     )
   model.eval()

   UpperCAmelCase__						={
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   UpperCAmelCase__						=prepare_img()
   UpperCAmelCase__						=256
   UpperCAmelCase__						=224
   UpperCAmelCase__						=EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}				,							crop_size={"height": crop_size, "width": crop_size}				,							resample=pillow_resamplings["bicubic"]				,							)
   UpperCAmelCase__						=processor(images=A				,							return_tensors="pt"     ).pixel_values

   # original processing pipeline
   UpperCAmelCase__						=Compose(
       [
           Resize(A				,							interpolation=pillow_resamplings["bicubic"]     ),
           CenterCrop(A     ),
           ToTensor(),
           Normalize(A				,							A     ),
       ]     )
   UpperCAmelCase__						=image_transforms(A     ).unsqueeze(0     )

   assert torch.allclose(A				,							A     )

   UpperCAmelCase__						=model(A     )
   UpperCAmelCase__						=outputs.logits

   UpperCAmelCase__						=(1, 1000)

   if "l1" in model_name:
      UpperCAmelCase__						=torch.Tensor(
          [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]     )
      assert torch.allclose(logits[0, :10]				,							A				,							atol=1e-3     )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      UpperCAmelCase__						=torch.Tensor(
          [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]     )
      assert torch.allclose(logits[0, :10]				,							A				,							atol=1e-3     )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      UpperCAmelCase__						=torch.Tensor(
          [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]     )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          F"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7"""     )

   # Save Checkpoints
   Path(A     ).mkdir(exist_ok=A     )
   model.save_pretrained(A     )
   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
   processor.save_pretrained(A     )
   print(F"""Processor successfuly saved at {pytorch_dump_path}"""     )

   if push_to_hub:
      print("Pushing model to the hub..."     )

      model.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""				,							commit_message="Add model"				,							use_temp_dir=A				,							)
      processor.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""				,							commit_message="Add image processor"				,							use_temp_dir=A				,							)


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--pytorch_model_path',
        default=None,
        type=str,
        required=True,
        help='Path to EfficientFormer pytorch checkpoint.',
    )
    parser.add_argument(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help='The json file for EfficientFormer model config.',
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )

    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )
    parser.set_defaults(push_to_hub=True)

    UpperCamelCase_												=	parser.parse_args()
    convert_efficientformer_checkpoint(
        checkpoint_path=args.pytorch_model_path,
        efficientformer_config_file=args.config_file,
        pytorch_dump_path=args.pytorch_dump_path,
        push_to_hub=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']))



